NHS Digital Data Release Register - reformatted

NHS Bristol, North Somerset And South Gloucestershire CCG projects

1113 data files in total were disseminated unsafely (information about files used safely is missing for TRE/"system access" projects).

🚩 NHS Bristol, North Somerset And South Gloucestershire CCG was sent multiple files from the same dataset, in the same month, both with optouts respected and with optouts ignored. NHS Bristol, North Somerset And South Gloucestershire CCG may not have compared the two files, but the identifiers are consistent between datasets, and outside of a good TRE NHS Digital can not know what recipients actually do.

GDPPR COVID-19 – CCG - Pseudo — DARS-NIC-400071-X3M7N

Type of data: information not disclosed for TRE projects

Opt outs honoured: No - Statutory exemption to flow confidential data without consent, Anonymised - ICO Code Compliant (Statutory exemption to flow confidential data without consent)

Legal basis: CV19: Regulation 3 (4) of the Health Service (Control of Patient Information) Regulations 2002, CV19: Regulation 3 (4) of the Health Service (Control of Patient Information) Regulations 2002; Health and Social Care Act 2012 - s261(5)(d)

Purposes: No (Clinical Commissioning Group (CCG), Sub ICB Location)

Sensitive: Sensitive

When:DSA runs 2020-09-01 — 2021-03-31 2021.01 — 2021.05.

Access method: One-Off, Frequent Adhoc Flow

Data-controller type: NHS BRISTOL, NORTH SOMERSET AND SOUTH GLOUCESTERSHIRE CCG, NHS BRISTOL, NORTH SOMERSET AND SOUTH GLOUCESTERSHIRE ICB - 15C

Sublicensing allowed: No

Datasets:

  1. GPES Data for Pandemic Planning and Research (COVID-19)
  2. COVID-19 Ethnic Category Data Set
  3. COVID-19 Vaccination Status
  4. COVID-19 General Practice Extraction Service (GPES) Data for Pandemic Planning and Research (GDPPR)

Objectives:

NHS Digital has been provided with the necessary powers to support the Secretary of State’s response to COVID-19 under the COVID-19 Public Health Directions 2020 (COVID-19 Directions) and support various COVID-19 purposes, the data shared under this agreement can be used for these specified purposes except where they would require the reidentification of individuals.

GPES data for pandemic planning and research (GDPPR COVID 19)
To support the response to the outbreak, NHS Digital has been legally directed to collect and analyse healthcare information about patients from their GP record for the duration of the COVID-19 emergency period under the COVID-19 Directions.
The data which NHS Digital has collected and is providing under this agreement includes coded health data, which is held in a patient’s GP record, such as details of:
• diagnoses and findings
• medications and other prescribed items
• investigations, tests and results
• treatments and outcomes
• vaccinations and immunisations

Details of any sensitive SNOMED codes included in the GDPPR data set can be found in the Reference Data and GDPPR COVID 19 user guides hosted on the NHS Digital website. SNOMED codes are included in GDPPR data.
There are no free text record entries in the data.

The Controller will use the pseudonymised GDPPR COVID 19 data to provide intelligence to support their local response to the COVID-19 emergency. The data is analysed so that health care provision can be planned to support the needs of the population within the CCG area for the COVID-19 purposes.

Such uses of the data include but are not limited to:

• Analysis of missed appointments - Analysis of local missed/delayed referrals due to the COVID-19 crisis to estimate the potential impact and to estimate when ‘normal’ health and care services may resume, linked to Paragraph 2.2.3 of the COVID-19 Directions.

• Patient risk stratification and predictive modelling - to highlight patients at risk of requiring hospital admission due to COVID-19, computed using algorithms executed against linked de-identified data, and identification of future service delivery models linked to Paragraph 2.2.2 of the COVID-19 Directions. As with all risk stratification, this would lead to the identification of the characteristics of a cohort that could subsequently, and separately, be used to identify individuals for intervention. However the identification of individuals will not be done as part of this data sharing agreement, and the data shared under this agreement will not be reidentified.

• Resource Allocation - In order to assess system wide impact of COVID-19, the GDPPR COVID 19 data will allow reallocation of resources to the worst hit localities using their expertise in scenario planning, clinical impact and assessment of workforce needs, linked to Paragraph 2.2.4 of the COVID-19 Directions:

The data may only be linked by the Data Controller or their respective Data Processor, to other pseudonymised datasets which it holds under a current data sharing agreement only where such data is provided for the purposes of general commissioning by NHS Digital. The Health Service Control of Patient Information Regulations (COPI) will also apply to any data linked to the GDPPR data.
The linked data may only be used for purposes stipulated within this agreement and may only be held and used whilst both data sharing agreements are live and in date. Using the linked data for any other purposes, including non-COVID-19 purposes would be considered a breach of this agreement. Reidentification of individuals is not permitted under this DSA.

LEGAL BASIS FOR PROCESSING DATA:
Legal Basis for NHS Digital to Disseminate the Data:
NHS Digital is able to disseminate data with the Recipients for the agreed purposes under a notice issued to NHS Digital by the Secretary of State for Health and Social Care under Regulation 3(4) of the Health Service Control of Patient Information Regulations (COPI) dated 17 March 2020 (the NHSD COPI Notice).
The Recipients are health organisations covered by Regulation 3(3) of COPI and the agreed purposes (paragraphs 2.2.2-2.2.4 of the COVID-19 Directions, as stated below in section 5a) for which the disseminated data is being shared are covered by Regulation 3(1) of COPI.

Under the Health and Social Care Act, NHS Digital is relying on section 261(5)(d) – necessary or expedient to share the disseminated data with the Recipients for the agreed purposes.


Legal Basis for Processing:
The Recipients are able to receive and process the disseminated data under a notice issued to the Recipients by the Secretary of State for Health and Social Care under Regulation 3(4) of COPI dated 20th March (the Recipient COPI Notice section 2).

The Secretary of State has issued notices under the Health Service Control of Patient Information Regulations 2002 requiring the following organisations to process information:

Health organisations

“Health Organisations” defined below under Regulation 3(3) of COPI includes CCGs for the reasons explained below. These are clinically led statutory NHS bodies responsible for the planning and commissioning of health care services for their local area

The Secretary of State for Health and Social Care has issued NHS Digital with a Notice under Regulation 3(4) of the National Health Service (Control of Patient Information Regulations) 2002 (COPI) to require NHS Digital to share confidential patient information with organisations permitted to process confidential information under Regulation 3(3) of COPI. These include:

• persons employed or engaged for the purposes of the health service

Under Section 26 of the Health and Social Care Act 2012, CCG’s have a duty to provide and manage health services for the population.

Regulation 7 of COPI includes certain limitations. The request has considered these limitations, considering data minimisation, access controls and technical and organisational measures.

Under GDPR, the Recipients can rely on Article 6(1)(c) – Legal Obligation to receive and process the Disclosed Data from NHS Digital for the Agreed Purposes under the Recipient COPI Notice. As this is health information and therefore special category personal data the Recipients can also rely on Article 9(2)(h) – preventative or occupational medicine and para 6 of Schedule 1 DPA – statutory purpose.

Expected Benefits:

• Manage demand and capacity
• Reallocation of resources
• Bring in additional workforce support
• Assists commissioners to make better decisions to support patients
• Identifying COVID-19 trends and risks to public health
• Enables CCGs to provide guidance and develop policies to respond to the outbreak
• Controlling and helping to prevent the spread of the virus

Outputs:

• Operational planning to predict likely demand on primary, community and acute service for vulnerable patients due to the impact of COVID-19
• Analysis of resource allocation
• Investigating and monitoring the effects of COVID-19
• Patient Stratification in relation to COVID-19, such as:
o Patients at highest risk of admission
o Frail and elderly
o Patients that are currently in hospital
o Patients with prescriptions related to COVID-19
o Patients recently Discharged from hospital
For avoidance of doubt these are pseudonymised patient cohorts, not identifiable.

Processing:

PROCESSING CONDITIONS:
Data must only be used for the purposes stipulated within this Data Sharing Agreement. Any additional disclosure / publication will require further approval from NHS Digital.

Data Processors must only act upon specific instructions from the Data Controller.

All access to data is managed under Role-Based Access Controls. Users can only access data authorised by their role and the tasks that they are required to undertake.

Patient level data will not be linked other than as specifically detailed within this Data Sharing Agreement.

NHS Digital reminds all organisations party to this agreement of the need to comply with the Data Sharing Framework Contract requirements, including those regarding the use (and purposes of that use) by “Personnel” (as defined within the Data Sharing Framework Contract i.e.: employees, agents and contractors of the Data Recipient who may have access to that data).

The Recipients will take all required security measures to protect the disseminated data and they will not generate copies of their cuts of the disseminated data unless this is strictly necessary. Where this is necessary, the Recipients will keep a log of all copies of the disseminated data and who is controlling them and ensure these are updated and destroyed securely.

Onward sharing of patient level data is not permitted under this agreement. Only aggregated reports with small number suppression can be shared externally.

The data disseminated will only be used for COVID-19 GDPPR purposes as described in this DSA, any other purpose is excluded.

SEGREGATION:
Where the Data Processor and/or the Data Controller hold both identifiable and pseudonymised data, the data will be held separately so data cannot be linked.

AUDIT
All access to data is auditable by NHS Digital in accordance with the Data Sharing Framework Contract and NHS Digital terms.
Under the Local Audit and Accountability Act 2014, section 35, Secretary of State has power to audit all data that has flowed, including under COPI.

DATA MINIMISATION:
Data Minimisation in relation to the data sets listed within the application are listed below:

• Patients who are normally registered and/or resident within the CCG region (including historical activity where the patient was previously registered or resident in another commissioner area).
and/or
• Patients treated by a provider where the CCG is the host/co-ordinating commissioner and/or has the primary responsibility for the provider services in the local health economy.
and/or
• Activity identified by the provider and recorded as such within national systems (such as SUS+) as for the attention of the CCG.

The Data Services for Commissioners Regional Office (DSCRO) obtains the following data sets:
- GDPPR COVID 19 Data
Pseudonymisation is completed within the DSCRO and is then disseminated as follows:
1. Pseudonymised GDPPR COVID 19 data is securely transferred from the DSCRO to the Data Controller / Processor
2. Aggregation of required data will be completed by the Controller (or the Processor as instructed by the Controller).
3. Patient level data may not be shared by the Controller (or any of its processors).

DSfC - NHS Bristol, North Somerset and South Gloucestershire CCG - Comm IV RS — DARS-NIC-186885-Q1T3D

Type of data: information not disclosed for TRE projects

Opt outs honoured: N, Y, No - data flow is not identifiable, Yes - patient objections upheld, Anonymised - ICO Code Compliant, Identifiable (Section 251, Section 251 NHS Act 2006, Mixture of confidential data flow(s) with support under section 251 NHS Act 2006 and non-confidential data flow(s))

Legal basis: Health and Social Care Act 2012 – s261(1) and s261(2)(b)(ii), Section 251 approval is in place for the flow of identifiable data, Health and Social Care Act 2012 - s261(5)(d), National Health Service Act 2006 - s251 - 'Control of patient information'. , Health and Social Care Act 2012 - s261 - 'Other dissemination of information', Health and Social Care Act 2012 – s261(1) and s261(2)(b)(ii), Health and Social Care Act 2012 – s261(7); National Health Service Act 2006 - s251 - 'Control of patient information'., Health and Social Care Act 2012 – s261(2)(b)(ii)

Purposes: No (Clinical Commissioning Group (CCG), Sub ICB Location)

Sensitive: Sensitive

When:DSA runs 2019-04-01 — 2022-03-31 2018.06 — 2021.05.

Access method: Frequent adhoc flow, Frequent Adhoc Flow, One-Off

Data-controller type: NHS BRISTOL, NORTH SOMERSET AND SOUTH GLOUCESTERSHIRE CCG, NHS BRISTOL, NORTH SOMERSET AND SOUTH GLOUCESTERSHIRE ICB - 15C

Sublicensing allowed: No

Datasets:

  1. Acute-Local Provider Flows
  2. Ambulance-Local Provider Flows
  3. Children and Young People Health
  4. Community Services Data Set
  5. Community-Local Provider Flows
  6. Demand for Service-Local Provider Flows
  7. Diagnostic Imaging Dataset
  8. Diagnostic Services-Local Provider Flows
  9. Emergency Care-Local Provider Flows
  10. Experience, Quality and Outcomes-Local Provider Flows
  11. Improving Access to Psychological Therapies Data Set
  12. Maternity Services Data Set
  13. Mental Health and Learning Disabilities Data Set
  14. Mental Health Minimum Data Set
  15. Mental Health Services Data Set
  16. Mental Health-Local Provider Flows
  17. National Cancer Waiting Times Monitoring DataSet (CWT)
  18. Other Not Elsewhere Classified (NEC)-Local Provider Flows
  19. Population Data-Local Provider Flows
  20. Primary Care Services-Local Provider Flows
  21. Public Health and Screening Services-Local Provider Flows
  22. SUS for Commissioners
  23. Civil Registration - Births
  24. Civil Registration - Deaths
  25. National Diabetes Audit
  26. Patient Reported Outcome Measures
  27. e-Referral Service for Commissioning
  28. Personal Demographic Service
  29. Summary Hospital-level Mortality Indicator
  30. National Cancer Waiting Times Monitoring DataSet (NCWTMDS)
  31. Improving Access to Psychological Therapies Data Set_v1.5
  32. Civil Registrations of Death
  33. Community Services Data Set (CSDS)
  34. Diagnostic Imaging Data Set (DID)
  35. Improving Access to Psychological Therapies (IAPT) v1.5
  36. Mental Health and Learning Disabilities Data Set (MHLDDS)
  37. Mental Health Minimum Data Set (MHMDS)
  38. Mental Health Services Data Set (MHSDS)
  39. Patient Reported Outcome Measures (PROMs)
  40. Summary Hospital-level Mortality Indicator (SHMI)

Objectives:


Invoice Validation
Invoice validation is part of a process by which providers of care or services get paid for the work they do.
Invoices are submitted to the Clinical Commissioning Group (CCG) so they are able to ensure that the activity claimed for each patient is their responsibility. This is done by processing and analysing Secondary User Services (SUS+) data, which is received into a secure Controlled Environment for Finance (CEfF). The SUS+ data is identifiable at the level of NHS number. The NHS number is only used to confirm the accuracy of backing-data sets and will not be used further.
The legal basis for this to occur is under Section 251 of NHS Act 2006.
Invoice Validation with be conducted by NHS Bristol, North Somerset and South Gloucestershire CCG
The CCG are advised by NHS Bristol, North Somerset and South Gloucestershire CCG whether payment for invoices can be made or not.

Risk Stratification
Risk stratification is a tool for identifying and predicting which patients are at high risk or are likely to be at high risk and prioritising the management of their care in order to prevent worse outcomes.
To conduct risk stratification Secondary User Services (SUS+) data, identifiable at the level of NHS number is linked with Primary Care data (from GPs) and an algorithm is applied to produce risk scores. Risk Stratification provides a forecast of future demand by identifying high risk patients. Commissioners can then prepare plans for patients who may require high levels of care. Risk Stratification also enables General Practitioners (GPs) to better target intervention in Primary Care.
The legal basis for this to occur is under Section 251 of NHS Act 2006 (CAG 7-04(a)).
Risk Stratification will be conducted by South Central and West Commissioning Support Unit

Commissioning
The NHS and local councils have come together in 44 areas covering all of England to develop proposals to improve health and care. They have formed new partnerships – known as sustainability and transformation partnerships – to plan jointly for the next few years.

Sustainability and transformation partnerships build on collaborative work that began under the NHS Shared Planning Guidance for 2016/17 – 2020/21, to support implementation of the Five Year Forward View. They are supported by six national health and care bodies: NHS England; NHS Improvement; the Care Quality Commission (CQC); Health Education England (HEE); Public Health England (PHE) and the National Institute for Health and Care Excellence (NICE).

The CCG is part of the Bristol, North Somerset and South Gloucestershire Sustainable Transformation Partnership. The STP is responsible for implementing large parts of the 5 year forward view from NHS England. The STP is implementing several initiatives:

- Putting the patient at the heart of the health system
- Working across organisational boundaries to deliver care and including social care, public Health, providers and GPs as well as CCGs
- Reviewing patient pathways to improve patient experience whilst reducing costs e.g. reduce the number of standard tests a patient may have and only have the ones they need
- Planning the demand and capacity across the healthcare system across 2 CCGs to ensure we have the right buildings, services and staff to cope with demand whilst reducing the impact on costs
- Working to prevent or capture conditions early as they are cheaper to treat
- Introduce initiatives to change behaviours e.g. move more care into the community
- Patient pathway planning for the above


To ensure the patient is at the heart of care, the STP is focussing on where services are required across the geographical region. This assists to ensure delivery of care in the right place for patients who may move and change services across the CCG.

The CCG will work proactively and collaboratively in the STP to redesign services across boundaries to integrate services.

The CCG will use pseudonymised data to provide intelligence to support the commissioning of health services. The data (containing both clinical and financial information) is analysed so that health care provision can be planned to support the needs of the population within the STP area.
The CCG commissions services from a range of providers covering a wide array of services. Each of the data flow categories requested supports the commissioned activity of one or more providers.
The following pseudonymised datasets are required to provide intelligence to support commissioning of health services:
- Secondary Uses Service (SUS+)
- Local Provider Flows
o Acute
o Ambulance
o Community
o Demand for Service
o Diagnostic Service
o Emergency Care
o Experience, Quality and Outcomes
o Mental Health
o Other Not Elsewhere Classified
o Population Data
o Primary Care Services
o Public Health Screening
- Mental Health Minimum Data Set (MHMDS)
- Mental Health Learning Disability Data Set (MHLDDS)
- Mental Health Services Data Set (MHSDS)
- Maternity Services Data Set (MSDS)
- Improving Access to Psychological Therapy (IAPT)
- Child and Young People Health Service (CYPHS)
- Community Services Data Set (CSDS)
- Diagnostic Imaging Data Set (DIDS)
- National Cancer Waiting Times Monitoring Data Set (CWT)
The pseudonymised data is required to for the following purposes:
§ Population health management:
• Understanding the interdependency of care services
• Targeting care more effectively
• Using value as the redesign principle
• Ensuring we do what we should
§ Data Quality and Validation – allowing data quality checks on the submitted data
§ Thoroughly investigating the needs of the population, to ensure the right services are available for individuals when and where they need them
§ Understanding cohorts of residents who are at risk of becoming users of some of the more expensive services, to better understand and manage those needs
§ Monitoring population health and care interactions to understand where people may slip through the net, or where the provision of care may be being duplicated
§ Modelling activity across all data sets to understand how services interact with each other, and to understand how changes in one service may affect flows through another
§ Service redesign
§ Health Needs Assessment – identification of underlying disease prevalence within the local population
§ Patient stratification and predictive modelling - to identify specific patients at risk of requiring hospital admission and other avoidable factors such as risk of falls, computed using algorithms executed against linked de-identified data, and identification of future service delivery models

The pseudonymised data is required to ensure that analysis of health care provision can be completed to support the needs of the health profile of the population within the CCG area based on the full analysis of multiple pseudonymised datasets.
Processing for commissioning will be conducted by South Central and West Commissioning Support Unit and Outcomes Based Health (OBH)

Yielded Benefits:

Data used in support of ongoing transformation plans to deliver against savings plan. To date, around £28.5m of savings identified. Work supported by analyst use of data to initially develop and savings plans and identify impacts. Ongoing activity monitoring support and reporting. Data used to monitor impact of major system changes, including the overnight closure of an AE department within the BNSSG area. Activity data used to measure impact on system and patients and inform decision making around the process. Development of bespoke reporting tools which are used in a range of settings, including an unwarranted practice variation tool which has been used to identify referral outliers and inform further work to understand activity. Continued use of data in support of monitoring around delivery of constitutional standards, with standards achieved in a number of measures. Data used to help inform plans aimed at achieving targets in other areas requiring improvement, by understanding drivers behind performance and identifying opportunities for improvements. Identify data and performance issues at provider organisations to initiate discussions at contract meetings which has led to: further analysis to identify root causes and mitigating actions; using contractual levers i.e. issuing contractual notices (Contract Performance Notices and Information Breach Notices) to facilitate improvement through joint investigations and remedial action plans and; identifying good practice and lessons learned to ensure consistency across our 3 providers to support equity of provision. Provide assurance to CCG Governing Body and Healthy Together STP (System Delivery Oversight Group): that contracts with providers are being managed effectively and; how the BNSSG population is being served; Provides evidence to STP system wide groups on areas of good and poor performance such as urgent care, planned care and cancer to share learning, identify improvements and develop joint plans. Support the work with providers to develop joint activity plans, performance trajectories and strategies. As a CCG we are an evidence-based organisation using data to underpin our decision making processes. Key achievements which have been informed by use of data include: Funding mental health support in schools: we became the first city in the country to fund a mental health-focused training package for staff in every primary and secondary school, working closely with Bristol City Council. The training is designed to raise the profile of Child and Adolescent Mental Health Services (CAMHS), with the focus on early intervention to make a significant difference to young people’s lives. Advancing our dementia care: We have established leading dementia wellbeing services, with around 2,000 patients benefiting from specialised support last year. A key feature is that people are never discharged from the service and can access support whenever they need it. We’re making continued investments and the number of people the service supports has nearly doubled over the last 12 months. Sustainability and Transformation Partnership: We developed a new plan to guide our commissioning activity until 2021 as a partnership between Bristol, North Somerset and South Gloucestershire CCGs. After drafting the plan together, we involved the community and other clinical groups to gain their input. We had meetings with the three local authority health scrutiny committees and community volunteers. Our work together set us up to develop the best possible healthcare services for our population’s needs in the coming years. Designing healthcare with our young people: We held a major consultation to find out what local young people, their parents and professionals involved in their care, thought of our community health services. We received over 1,200 ideas and have been using that feedback to shape children’s and young people’s health services. Improving hospital discharge for older people: Our Discharge to Assess (D2A) scheme speeds up hospital discharge times for older patients, helping them get home quicker. The D2A team support patients to be discharged and assess ongoing care at home, on the same day, by community teams of social workers, nurses, physiotherapists and occupational therapists. The scheme has helped 20 people who would otherwise be in hospital receive therapy in their own homes and enabled us to save a total of 376 hospital bed days. Appointing a new community healthcare services provider: In partnership with NHS England and North Somerset Council, we appointed North Somerset Community Partnership (NSCP) to provide community healthcare services. They provide all adult and some children’s community health services and specialist services, such as those for people with Parkinson’s disease, until April 2019. Developing better out-of-hospital care: Helping people to live safely and independently at home as they age has been a high priority. We’ve introduced several initiatives to help people get better outside of hospital, from helping them discharge as soon as they’re fit to leave, to outpatient appointments in the community, and working with Age UK to provide better support to people as they age. Transforming community services for children: We re-procured children’s community health services for Bristol and South Gloucestershire. This involved a partnership of five commissioning organisations working together to develop a cost-effective service for the whole area that recognises specific local needs, delivering coordinated care to children, young people and families. Tackling diabetes and promoting self-care: We joined the second wave of the national Diabetes Prevention Programme in April 2017, building on a pilot at Leap Valley GP practice in Emersons Green, Bristol. Together with Bristol and South Gloucestershire CCGs, we’ve also since been selected to pilot the digital stream, which aims to establish whether digital interventions are effective in supporting behaviour change in people with non-diabetic hyperglycaemia (NDH) and overweight and or obese individuals.

Expected Benefits:


Invoice Validation
1. Financial validation of activity
2. CCG Budget control
3. Commissioning and performance management
4. Meeting commissioning objectives without compromising patient confidentiality
5. The avoidance of misappropriation of public funds to ensure the ongoing delivery of patient care

Risk Stratification
Risk stratification promotes improved case management in primary care and will lead to the following benefits being realised:
1. Improved planning by better understanding patient flows through the healthcare system, thus allowing commissioners to design appropriate pathways to improve patient flow and allowing commissioners to identify priorities and identify plans to address these.
2. Improved quality of services through reduced emergency readmissions, especially avoidable emergency admissions. This is achieved through mapping of frequent users of emergency services thus allowing early intervention.
3. Improved access to services by identifying which services may be in demand but have poor access, and from this identify areas where improvement is required.
4. Supports the commissioner to meets its requirement to reduce premature mortality in line with the CCG Outcome Framework by allowing for more targeted intervention in primary care.
5. Better understanding of local population characteristics through analysis of their health and healthcare outcomes
All of the above lead to improved patient experience through more effective commissioning of services.

Commissioning
1. Supporting Quality Innovation Productivity and Prevention (QIPP) to review demand management, integrated care and pathways.
a. Analysis to support full business cases.
b. Develop business models.
c. Monitor In year projects.
2. Supporting Joint Strategic Needs Assessment (JSNA) for specific disease types.
3. Health economic modelling using:
a. Analysis on provider performance against 18 weeks wait targets.
b. Learning from and predicting likely patient pathways for certain conditions, in order to influence early interventions and other treatments for patients.
c. Analysis of outcome measures for differential treatments, accounting for the full patient pathway.
d. Analysis to understand emergency care and linking A&E and Emergency Urgent Care Flows (EUCC).
4. Commissioning cycle support for grouping and re-costing previous activity.
5. Enables monitoring of:
a. CCG outcome indicators.
b. Financial and Non-financial validation of activity.
c. Successful delivery of integrated care within the CCG.
d. Checking frequent or multiple attendances to improve early intervention and avoid admissions.
e. Case management.
f. Care service planning.
g. Commissioning and performance management.
h. List size verification by GP practices.
i. Understanding the care of patients in nursing homes.
6. Feedback to NHS service providers on data quality at an aggregate and individual record level – only on data initially provided by the service providers.
7. Improved planning by better understanding patient flows through the healthcare system, thus allowing commissioners to design appropriate pathways to improve patient flow and allowing commissioners to identify priorities and identify plans to address these.
8. Improved quality of services through reduced emergency readmissions, especially avoidable emergency admissions. This is achieved through mapping of frequent users of emergency services and early intervention of appropriate care.
9. Improved access to services by identifying which services may be in demand but have poor access, and from this identify areas where improvement is required.
10. Potentially reduced premature mortality by more targeted intervention in primary care, which supports the commissioner to meets its requirement to reduce premature mortality in line with the CCG Outcome Framework.
11. Better understanding of the health of and the variations in health outcomes within the population to help understand local population characteristics.
12. Better understanding of contract requirements, contract execution, and required services for management of existing contracts, and to assist with identification and planning of future contracts
13. Insights into patient outcomes, and identification of the possible efficacy of outcomes-based contracting opportunities.
14. Reviewing current service provision
a. Cost-benefit analysis and service impact assessments to underpin service transformation across health economy
b. Service planning and re-design (development of NMoC and integrated care pathways, new partnerships, working with new providers etc.)
c. Impact analysis for different models or productivity measures, efficiency and experience
d. Service and pathway review
e. Service utilisation review
15. Ensuring compliance with evidence and guidance
a. Testing approaches with evidence and compliance with guidance.
16. Monitoring outcomes
a. Analysis of variation in outcomes across population group
17. Understanding how services impact across the health economy
a. Service evaluation
b. Programme reviews
c. Analysis of productivity, outcomes, experience, plan, targets and actuals
d. Assessing value for money and efficiency gains
e. Understanding impact of services on health inequalities
18. Understanding how services impact on the health of the population and patient cohorts
a. Measuring and assessing improvement in service provision, patient experience & outcomes and the cost to achieve this
b. Propensity matching and scoring
c. Triple aim analysis
19. Understanding future drivers for change across health economy
a. Forecasting health and care needs for population and population cohorts across STPs
b. Identifying changes in disease trends and prevalence
c. Efficiencies that can be gained from procuring services across wider footprints, from new innovations
d. Predictive modelling
20. Delivering services that meet changing needs of population
a. Analysis to support policy development
b. Ethical and equality impact assessments
c. Implementation of NMOC
d. What do next years contracts need to include?
e. Workforce planning
21. Maximising services and outcomes within financial envelopes across health economy
a. What-if analysis
b. Cost-benefit analysis
c. Health economics analysis
d. Scenario planning and modelling
e. Investment and disinvestment in services analysis
f. Opportunity analysis

Outcomes Based Healthcare:

Outcomes are often described as those things that matter to people, and are typically the end results of care across complete care pathways. Access to a population-level view of all people with relevant long-term conditions, in near real-time, is the essential starting point for accurate outcome measurement. It is also an essential enabler supporting quality process improvement within care pathways.

Outcomes measured using existing clinical and administrative data typically measure the reduction in illness, disease and complications, and their severity, in addition to system activity metrics.

Measuring outcomes aims to refocus providers (including health and social care providers) to work together to reduce the burden of disease. By setting longer-term targets and improvement trajectories for each outcome, providers can focus their efforts on improving these outcomes, for specific population groups, over a period of years.

Outputs:


Invoice Validation
1. Addressing poor data quality issues
2. Production of reports for business intelligence
3. Budget reporting
4. Validation of invoices for non-contracted events

Risk Stratification
1. As part of the risk stratification processing activity detailed above, GPs have access to the risk stratification tool which highlights patients for whom the GP is responsible and have been classed as at risk. The only identifier available to GPs is the NHS numbers of their own patients. Any further identification of the patients will be completed by the GP on their own systems.
2. Output from the risk stratification tool will provide aggregate reporting of number and percentage of population found to be at risk.
3. Record level output will be available for commissioners (of the CCG), pseudonymised at patient level.
4. GP Practices will be able to view the risk scores for individual patients with the ability to display the underlying SUS+ data for the individual patients when it is required for direct care purposes by someone who has a legitimate relationship with the patient.
5. The CCG will be able to target specific patient groups and enable clinicians with the duty of care for the patient to offer appropriate interventions. The CCG will also be able to:
a. Stratify populations based on: disease profiles; conditions currently being treated; current service use; pharmacy use and risk of future overall cost
b. Plan work for commissioning services and contracts
c. Set up capitated budgets
d. Identify health determinants of risk of admission to hospital, or other adverse care outcomes.

Commissioning
1. Commissioner reporting:
a. Summary by provider view - plan & actuals year to date (YTD).
b. Summary by Patient Outcome Data (POD) view - plan & actuals YTD.
c. Summary by provider view - activity & finance variance by POD.
d. Planned care by provider view - activity & finance plan & actuals YTD.
e. Planned care by POD view - activity plan & actuals YTD.
f. Provider reporting.
g. Statutory returns.
h. Statutory returns - monthly activity return.
i. Statutory returns - quarterly activity return.
j. Delayed discharges.
k. Quality & performance referral to treatment reporting.
2. Readmissions analysis.
3. Production of aggregate reports for CCG Business Intelligence.
4. Production of project / programme level dashboards.
5. Monitoring of acute / community / mental health quality matrix.
6. Clinical coding reviews / audits.
7. Budget reporting down to individual GP Practice level.
8. GP Practice level dashboard reports include high flyers.
9. Comparators of CCG performance with similar CCGs as set out by a specific range of care quality and performance measures detailed activity and cost reports
10. Data Quality and Validation measures allowing data quality checks on the submitted data
11. Contract Management and Modelling
12. Patient Stratification, such as:
a. Patients at highest risk of admission
b. Most expensive patients (top 15%)
c. Frail and elderly
d. Patients that are currently in hospital
e. Patients with most referrals to secondary care
f. Patients with most emergency activity
g. Patients with most expensive prescriptions
h. Patients recently moving from one care setting to another
i. Discharged from hospital
ii. Discharged from community
13. Profiling population health and wider determinants to identify and target those most in need
a. Understanding population profile and demographics
b. Identify patient cohorts with specific needs or who may benefit from interventions
c. Identifying disease prevalence. health and care needs for population cohorts
d. Contributing to Joint Strategic Needs Assessment (JSNA)
e. Geographical mapping and analysis
14. Identifying and managing preventable and existing conditions
a. Identifying types of individuals and population cohorts at risk of non-elective re-admission
b. Risk stratification to identify populations suitable for case management
c. Risk profiling and predictive modelling
d. Risk stratification for planning services for population cohorts
e. Identification of disease incidence and diagnosis stratification
15. Reducing health inequalities
a. Identifying cohorts of patients who have worse health outcomes typically deprived, ethnic groups, homeless, travellers etc. to enable services to proactively target their needs
b. Socio-demographic analysis
16. Managing demand
a. Waiting times analysis
b. Service demand and supply modelling
c. Understanding cross-border and overseas visitor
d. Winter planning
e. Emergency preparedness, business continuity, recovery and contingency planning
17. Care co-ordination and planning
a. Planning packages of care
b. Service planning
c. Planning care co-ordination
18. Monitoring individual patient health, service utilisation, pathway compliance experience & outcomes across the heath and care system
a. Patient pathway analysis across health and care
b. Outcomes & experience analysis
c. Analysis to support anti-terror initiatives
d. Analysis to identify vulnerable patients with potential safeguarding issues
e. Understanding equity of care and unwarranted variation
f. Modelling patient flow
g. Tracking patient pathways
h. Monitoring to support NMoC, ACOs, STPs
i. Identifying duplications in care
j. Identifying gaps in care, missed diagnoses and triple fail events
k. Analysing individual and aggregated timelines
19. Undertaking budget planning, management and reporting
a. Tracking financial performance against plans
b. Budget reporting
c. Tariff development
d. Developing and monitoring capitated budgets
e. Developing and monitoring individual-level budgets
f. Future budget planning and forecasting
g. Paying for care of overseas visitors and cross-border flow
20. Monitoring the value for money
a. Service-level costing & comparisons
b. Identification of cost pressures
a. Cost benefit analysis
b. Equity of spend across services and population cohorts
c. Finance impact assessment
21. Comparing population groups, peers, national and international best practice
d. Identification of variation in productivity, cost, outcomes, quality, experience, compared with peers, national and international & best practice
e. Benchmarking against other parts of the country
f. Identifying unwarranted variations
22. Comparing expected levels
g. Standardised comparisons for prevalence, activity, cost, quality, experience, outcomes for given populations
23. Comparing local targets & plan
h. Monitoring of local variation in productivity, cost, outcomes, quality and experience
i. Local performance dashboards by service provider, commissioner, geography, NMOC, STPs
24. Monitoring activity and cost compliance against contract and agreed plans
j. Contract monitoring
k. Contract reconciliation and challenge
l. Invoice validation
25. Monitoring provider quality, demand, experience and outcomes against contract and agreed plans
m. Performance dashboards
n. CQUIN reporting
o. Clinical audit
p. Patient experience surveys
q. Demand, supply, outcome & experience analysis
r. Monitoring cross-border flows and overseas visitor activity
26. Improving provider data quality
s. Coding audit
t. Data quality validation and review
u. Checking validity of patient identity and commissioner assignment

Outcomes Based Healthcare:
The main outputs are aggregated monthly values for each outcome (with small number suppression, including any values under 5). The aggregated monthly outcomes data will be made available through OBH’s online tool (available to named individuals in the CCG and CCG commissioned providers only) via a secure login.
This enables the CCGs and to:
- visualise baselines for each outcome
- set improvement trajectories
- monitor outcomes on an on-going basis.
In addition, the CCGs will receive an information schedule describing the outcomes to be monitored, the technical description, and annual baseline data for each outcome.
All outputs will be delivered within the timescales of the contract between OBH and the CCGs.

Processing:

Data must only be used as stipulated within this Data Sharing Agreement.

Data Processors must only act upon specific instructions from the Data Controller.

Data can only be stored at the addresses listed under storage addresses.

The Data Controller and any Data Processor will only have access to records of patients of residence and registration within the CCG.

Patient level data will not be shared outside of the CCG unless it is for the purpose of Direct Care, where it may be shared only with those health professionals who have a legitimate relationship with the patient and a legitimate reason to access the data.
All access to data is managed under Roles-Based Access Controls

CCGs should work with general practices within their CCG to help them fulfil data controller responsibilities regarding flow of identifiable data into risk stratification tools.

No patient level data will be linked other than as specifically detailed within this agreement. Data will only be shared with those parties listed and will only be used for the purposes laid out in the application/agreement. The data to be released from NHS Digital will not be national data, but only that data relating to the specific locality and that data required by the applicant.
The DSCRO (part of NHS Digital) will apply Type 2 objections before any identifiable data leaves the DSCRO.
NHS Digital reminds all organisations party to this agreement of the need to comply with the Data Sharing Framework Contract requirements, including those regarding the use (and purposes of that use) by “Personnel” (as defined within the Data Sharing Framework Contract ie: employees, agents and contractors of the Data Recipient who may have access to that data)

Segregation
Where the Data Processor and/or the Data Controller hold both identifiable and pseudonymised data, the data will be held separately so data cannot be linked.

All access to data is auditable by NHS Digital.

Data for the purpose of Invoice Validation is kept within the CEfF, and only used by staff properly trained and authorised for the activity. Only CEfF staff are able to access data in the CEfF and only CEfF staff operate the invoice validation process within the CEfF. Data flows directly in to the CEfF from the DSCRO and from the providers – it does not flow through any other processors.


Invoice Validation
NHS Bristol, North Somerset and South Gloucestershire CCG
1. SUS+ Data is obtained from the SUS+ Repository by the Data Services for Commissioners Regional Office (DSCRO).
2. The DSCRO pushes a one-way data flow of SUS+ data into the Controlled Environment for Finance (CEfF) located in the CCG.
3. The CEfF conduct the following processing activities for invoice validation purposes:
a. Validating that the Clinical Commissioning Group is responsible for payment for the care of the individual by using SUS+ and/or backing flow data.
b. Once the backing information is received, this will be checked against national NHS and local commissioning policies as well as being checked against system access and reports provided by NHS Digital to confirm the payments are:
i. In line with Payment by Results tariffs
ii. In relation to a patient registered with the CCG GP or resident within the CCG area.
iii. The health care provided should be paid by the CCG in line with CCG guidance. 
4. The CCG are notified by the CEfF that the invoice has been validated and can be paid. Any discrepancies or non-validated invoices are investigated and resolved


Risk Stratification
South Central and West Commissioning Support Unit
1. Identifiable SUS+ data is obtained from the SUS Repository to the Data Services for Commissioners Regional Office (DSCRO).
2. Data quality management and standardisation of data is completed by the DSCRO and the data identifiable at the level of NHS number is transferred securely to South Central and West Commissioning Support Unit, who hold the SUS+ data within the secure Data Centre on N3.
3. Identifiable GP Data is securely sent from the GP system to South Central and West Commissioning Support Unit.
4. SUS+ data is linked to GP data in the risk stratification tool by the data processor.
5. As part of the risk stratification processing activity, GPs have access to the risk stratification tool within the data processor, which highlights patients with whom the GP has a legitimate relationship and have been classed as at risk. The only identifier available to GPs is the NHS numbers of their own patients. Any further identification of the patients will be completed by the GP on their own systems.
6. Once South Central and West Commissioning Support Unit has completed the processing, the CCG can access the online system via a secure connection to access the data pseudonymised at patient level.

Commissioning
The Data Services for Commissioners Regional Office (DSCRO) obtains the following data sets:
1. SUS+
2. Local Provider Flows (received directly from providers)
a. Acute
b. Ambulance
c. Community
d. Demand for Service
e. Diagnostic Service
f. Emergency Care
g. Experience, Quality and Outcomes
h. Mental Health
i. Other Not Elsewhere Classified
j. Population Data
k. Primary Care Services
l. Public Health Screening
3. Mental Health Minimum Data Set (MHMDS)
4. Mental Health Learning Disability Data Set (MHLDDS)
5. Mental Health Services Data Set (MHSDS)
6. Maternity Services Data Set (MSDS)
7. Improving Access to Psychological Therapy (IAPT)
8. Child and Young People Health Service (CYPHS)
9. Community Services Data Set (CSDS)
10. Diagnostic Imaging Data Set (DIDS)
11. National Cancer Waiting Times Monitoring Data Set (CWT)
Data quality management and pseudonymisation is completed within the DSCRO and is then disseminated as follows:
Data Processor 1 - South, Central and West Commissioning Support Unit

1. Pseudonymised SUS, Local Provider data, Mental Health data (MHSDS, MHMDS, MHLDDS), Maternity data (MSDS), Improving Access to Psychological Therapies data (IAPT), Child and Young People’s Health data (CYPHS), Community Services Data Set (CSDS), Diagnostic Imaging data (DIDS) and National Cancer Waiting Times Monitoring Data Set (CWT)
2. only is held until points 2 – 8 are completed.
3. South, Central and West Commissioning Support Unit receives GP data. GP Data is received as follows:
o Identifiable GP data is submitted to South Central and West Commissioning Support Unit.
o The identifiable data lands in a ring-fenced area for GP data only.
o The GP data is pseudonymised using a pseudonymisation tool, different to that used by the DSCRO.
o There is a Data Processing Agreement in place between the GP and South Central and West Commissioning Support Unit. A specific named individual within South Central and West Commissioning Support Unit acts on behalf of the GP.
o This individual has access to a black box. The pseudonymised data is passed through the black box process where the pseudonymisation is mapped to the pseudonymisation used by the DSCRO.
o Once mapped, the data is passed into South Central and West Commissioning Support, but before South Central and West Commissioning Support Unit will receive the data from the ring-fenced area, they require confirmation that the identifiable data has been deleted.
o South Central and West Commissioning Support Unit are then sent the pseudonymised GP data with the pseudo algorithm specific to them.
4. South, Central and West Commissioning Support Unit also receive a flow of social care data. Social Care data is received in one of the following 2 ways:
o Pseudonymised:
§ Social Care data is pseudonymised within the provider using a pseudonymisation tool, different to that used by the DSCRO. The provider requests a pseudonymisation key from the DSCRO. The key can only be used once. The key is specific to the Local Authority and to that specific date.
§ The pseudonymised data lands in a ring-fenced area for social care data only.
§ There is a Data Processing Agreement in place between the Provider and South Central and West Commissioning Support Unit. A specific named individual within South Central and West Commissioning Support Unit acts on behalf of the Provider.
§ This individual has access to a black box. The pseudonymised data is passed through the black box process where the pseudonymisation is mapped to the pseudonymisation used by the DSCRO.
§ The data is then passed into the non-ringfenced area with the pseudo algorithm specific to them.
o Identifiable:
§ Identifiable social care data is submitted to South Central and West Commissioning Support Unit.
§ The identifiable data lands in a ring-fenced area for social care data only.
§ The social care data is pseudonymised using a pseudonymisation tool, different to that used by the DSCRO.
§ There is a Data Processing Agreement in place between the Local Authority and South Central and West Commissioning Support Unit. A specific named individual within South Central and West Commissioning Support Unit acts on behalf of the provider.
§ This individual has access to a black box. The pseudonymised data is passed through the black box process where the pseudonymisation is mapped to the pseudonymisation used by the DSCRO.
§ Once mapped, the data is passed into South Central and West Commissioning Support, but before South Central and West Commissioning Support Unit will receive the data from the ring-fenced area, they require confirmation that the identifiable data has been deleted.
§ South Central and West Commissioning Support Unit are then sent the pseudonymised social care data with the pseudo algorithm specific to them.
5. Once the pseudonymised GP data and social care data is received, South, Central and West Commissioning Support Unit make a request to the DSCRO.
6. The DSCRO check the dates of the key generation (Point 2d and 3aii/3biv).
7. The DSCRO then send a mapping table to South, Central and West Commissioning Support Unit
8. South, Central and West Commissioning Support Unit then overwrite the organisation specific keys with the DSCRO key.
9. The mapping table is then deleted.
10. The DSCRO pass the pseudonymised SUS, local provider data, Mental Health (MHSDS, MHMDS, MHLDDS), Maternity (MSDS), Improving Access to Psychological Therapies (IAPT), Child and Young People’s Health (CYPHS) and Diagnostic Imaging (DIDS) securely to South, Central and West Commissioning Support Unit for the addition of derived fields, linkage of data sets and analysis
11. Social care data is then linked to the data sets listed within point 9.
12. South, Central and West Commissioning Support Unit then pass the processed, pseudonymised and linked data to the CCG.
13. Aggregation of required data for CCG management use will be completed by South, Central and West Commissioning Support Unit as instructed by the CCG.
14. Patient level data will not be shared outside of the CCG and will only be shared within the CCG on a need to know basis, as per the purposes stipulated within the Data Sharing Agreement. External aggregated reports only with small number suppression can be shared as set out within NHS Digital guidance applicable to each data set.

Data Processor 2 - Outcomes Based Health
1. Pseudonymised SUS+ (pseudonymised using an encryption key’ specific to this project), only is securely transferred from the DSCRO to South, Central and West Commissioning Support Unit..
2. South, Central and West Commissioning Support Unit add derived fields and securely transfer the data to the CCG.
3. The CCG passes the pseudonymised data to Outcomes Based Health
4. Outcomes Based Health then analyse the data to:
o Develop outcomes, baselining and monitoring of individual outcomes
o Provide detailed analysis relating to outcomes
5. Aggregation of required data for CCG management use will be completed by Outcomes Based Health or the CCG as instructed by the CCG.
6. Patient level data will not be shared outside of the CCG and will only be shared within the CCG on a need to know basis, as per the purposes stipulated within the Data Sharing Agreement. External aggregated reports only with small number suppression can be shared as set out within NHS Digital guidance applicable to each data set.

Making CCG operational planning more robust by using a large activity sample size to derive analytics — DARS-NIC-238370-G8Z6V

Type of data: information not disclosed for TRE projects

Opt outs honoured: No - data flow is not identifiable, Anonymised - ICO Code Compliant, No (Does not include the flow of confidential data)

Legal basis: Health and Social Care Act 2012 – s261(1) and s261(2)(b)(ii), Health and Social Care Act 2012 – s261(1) and s261(2)(b)(ii), Health and Social Care Act 2012 – s261(2)(b)(ii)

Purposes: No (Clinical Commissioning Group (CCG), Sub ICB Location)

Sensitive: Non Sensitive, and Non-Sensitive

When:DSA runs 2019-09-11 — 2020-09-10 2019.10 — 2020.01.

Access method: Frequent Adhoc Flow, One-Off

Data-controller type: NHS BRISTOL, NORTH SOMERSET AND SOUTH GLOUCESTERSHIRE CCG, NHS BRISTOL, NORTH SOMERSET AND SOUTH GLOUCESTERSHIRE ICB - 15C

Sublicensing allowed: No

Datasets:

  1. Hospital Episode Statistics Accident and Emergency
  2. Hospital Episode Statistics Admitted Patient Care
  3. Hospital Episode Statistics Outpatients
  4. Hospital Episode Statistics Accident and Emergency (HES A and E)
  5. Hospital Episode Statistics Admitted Patient Care (HES APC)
  6. Hospital Episode Statistics Outpatients (HES OP)

Objectives:

Statistical and mathematical modelling is the process of taking a real world problem or system and creating a simplified description of it (a model) – using mathematical concepts and language. The model is designed to capture features of interest in the real world system and provide means to study the interrelation of those features, make predictions, or study the possible effects of changing some of those features.

In a healthcare planning context, a model will typically take a mixture of quantitative/numerical variables (corresponding to concepts such as arrivals at a hospital, length of stay, age, cost, lab results, clinical scores, or counts of activity) and categorical variables (corresponding to things like type of illness, sex, or smoking status), and codify the relationships between them using mathematical formulae.

In situations where outcomes are uncertain (such as when making predictions, when there is potential measurement error, or when there is a random element to when or how individual events occur), the concept of a probability distribution is particularly useful in constructing models. A probability distribution takes the measure of interest (hospital length of stay, for example) and quantifies the chance that each value of that measure occurs. Real life data can often be well approximated by theoretical probability distributions which are described by established mathematical formulae and whose properties are well understood. This is a more fine-grained (and realistic) approach than simply describing the measure by its average (mean or median) value alone.

An extremely simple and unrealistic mathematical model might describe, for example, length of stay in an inpatient ward as a simple formula which calculates the predicted length of stay for a given patient would be some constant multiplied by their age on admission. A more sophisticated model might predict the length of stay based on a combination of age, sex and frailty, multiplying each by a constant and adding them together to get the expected length of stay.

An alternative model might account for some of the natural random variability in the process – obviously, not all patients admitted to a ward where the average length of stay is 3 days will stay for exactly 3 days. To account for this, the model might instead predict lengths of stay for individual patients by choosing values at random from a probability distribution. The particular distribution could be chosen so that it agrees with some easily obtainable summary measures (such as the mean and standard deviation) of the actual data. The choice of length of stay value to use from the distribution could be made by a process similar to flipping a coin or rolling a die, then reading of the value which corresponds to the number which comes up. More sophisticated approaches, and/or ones which made different assumptions, could also be taken.

The terms “events” and “shocks” in the sections below have been used interchangeably to refer to factors which affect the generalisability of the data or its reflection of system behaviour expected/normal circumstances, including both data quality problems and deliberate operational interventions.

NHS Bristol, North Somerset and South Gloucestershire Clinical Commissioning Group (BNSSG CCG) commissions, plans and monitors healthcare services for a population of approximately 1.1 million people in a sub-region of South West England based around the Bristol urban centre. It was created in 2018 by the merger of three predecessor organisations and as part of that process took on greatly enhanced responsibility for system-wide data analytics, including the creation of a specific modelling and analytics (M&A) team. This team’s remit is to provide advanced analytical support on both business-as-usual (BAU) and transformation projects to the CCG and the local Sustainability and Transformation Partnership/emergent Integrated Care System. This involves work with local acute, mental health, community and primary care providers, and to enhance existing business intelligence and analytics capacity in the system by providing access to more sophisticated and accurate methods and outputs than were historically available. Note that this reorganisation included the in-housing of many analytic functions traditionally out-sourced to Commissioning Support Units, hence BNSSG CCG data requirements may be more extensive than those for CCGs in which analytics expertise is bought from an external supplier.

Standard reporting against nationally reportable standards, and traditional, basic, activity and cost summaries are routinely produced by non-specialist analysts using SUS (Secondary Use Services) SEM (a specific file format) data restricted to the CCG’s registered population.

However, data which is both more granular and raw in definition, and more extensive in geographical coverage (for the whole of England), is required by highly specialised mathematical analysts within the CCG’s M&A team to construct metrics needed for more sophisticated modelling techniques. The BNSSG CCG will use Hospital Episode Statistics (HES) data to derive robust estimates of key operational metrics including probability distributions for inpatient hospital care length of stay (LOS), inpatient, outpatient, and emergency arrival rates, waiting times and associated measures (as opposed to single number summary statistics). All of these will be determined for defined clinical, demographic, time-period and potentially geographic (e.g. urban vs rural) and provider-type profiles in order to make them meaningfully applicable to operational and strategic planning, service redesign, and intervention analysis projects.

The analysis will use pseudonymised non-sensitive patient-level data to derive empirical probability distributions for the processes of interest, and from these fit theoretical distributions which can be used in simulation modelling and statistical analysis of existing and proposed patient pathways. Using local data alone is not a reliable option since; when considering activity within specified clinical, demographic and time profiles the effects of individual extreme values in the data, periods of poor data quality/missing data, service transfers, interventions and system reconfiguration, as well as the inevitable small numbers in some groupings (even in a population of greater than 1 million), mean derived distributions would be unreliable and the level of variability which would need to be included in estimate would severely constrain its usefulness. By both greatly increasing the effective “sample size” and smoothing out the effect of local time-limited service changes and data quality issues, using all-England patient level data will mitigate the effect of these events/shocks in any given local system (such as temporary consultant vacancies, ward closures, service suspensions, electronic system changes, etc.) and allow for the construction of robust, practically usable distribution estimates. These will in turn facilitate improved accuracy in projection, planning and monitoring of activity which relies on models built using those metrics. Using data at an all-England level as opposed to single CCG level is consistent with the approach historically taken by the NHS nationally, for example in the construction of NHS England’s Indicative Hospital Activity Model (IHAM).

Examples of the type of modelling which the outputs of this project will be used to support include (but are not restricted to): discrete event simulation (DES) modelling of multi-stage patient pathways which include both hospital admission and outpatient appointments; system dynamics modelling of urgent care patient flows; and comparison of local admitted patient care pathway performance for specific treatments following local policy interventions to expected “do nothing” scenarios. Modelling is currently being done in all of these contexts within BNSSG, but the precision, accuracy and interpretability of the results is constrained by local data limitations and could be greatly improved by the use of more robust distributions and metrics, derived from data with national coverage.

Because much hospital activity exhibits seasonality, and to account for the effect of year-on-year changing policy and financial incentives, a minimum of three years of data is required to derive reliable estimates. To make it relevant to future and current planning and analysis, the data needs to be from the most recent available periods. To ensure the maximum possible feasible data minimisation, this request has thus been restricted to the most recent three years of data. HES data has been chosen because it contains a sufficient degree of granularity to allow sub-setting into groups which match how specific services are (or may be) constructed, in terms of which patient groups they serve.

Expected Benefits:

The potential benefits of accurate and reliable operational planning for healthcare service delivery in general are well established. It is also well established that unreliability of projections and metrics can make such planning difficult.

This data is being requested to support a project which directly addresses that problem by seeking to improve the accuracy and reliability of analytics used for practical, local system-wide operational planning of CCG-level healthcare services.

As already referred to in previous sections, reliance on analysis solely of local data to derive, for example, expected arrival, admission and length of stay metrics for particular types of service, can be subject to local events and shocks which mask the "natural" metrics of the service and distort analysis based solely on that local data. Additionally, when analysing data at a fine level of granularity (e.g. deriving metrics for a highly specialised service), the "sample size" in the local data is in some cases too small to make valid statistical inferences from, or else the small sample size leads to unhelpful levels of uncertainty in the estimates. This poses a problem when working to construct accurate and reliable operational and contract plans for the local system.

The national data will improve BNSSG CCG’s picture of (for example) underlying growth and enable the CCG to "fill in the gaps" which are inevitable in low sample sizes, and that can cause volatility in any resulting projections. For example, where an otherwise irregular surge in activity - perhaps only due to the opening of additional clinic slots - can have a material impact on any associated growth figures, which would not necessarily be borne-out in future years. Or where the odd very long LOS can have a drastic effect on the averages deduced.

The outputs will have a direct effect of decision making in a variety of strategic and operational settings. For example, inpatient length of stay (LOS) measures will be used in modelling which directly informs choices of capacity (in terms of bed numbers, staffing etc.) in service reconfigurations and design of new services. This includes multi-organisational patient care pathways - activity metrics will be used directly in the production and verification of activity projections used for operational planning and funding of future service levels; activity, LOS, and other measures will be used to derive more realistic performance measures for local services, allowing the generation of models to detect variations from expected activity.

The availability of robust metrics derived from data with a national coverage will greatly enhance the reliability of these modelling outputs with expected operational and financial benefits in the local health system, and potential for similar benefits to be realised by other areas building on the BNSSG work. The financial scale of the impact is potentially very large – the CCG manages a budget of more than £1bn per year and decisions about funding particular capacity levels, based on future activity estimates, is in the order of millions of pounds each year, and the operational effect on correct service capacity estimates has a non-quantitative but significant effect on patient care and experience, in terms of waiting times, capacity to deliver appropriate treatment in urgent care settings, and the ability of the local system to absorb shocks in terms of changes to and variation in activity levels.

Initial use of the outputs in a local context would be within a matter of months and some wider dissemination of results and measures could follow soon after, while detailed assessment of the utility of the derived metrics would be assessed and adjusted over a period of subsequent financial years up to the project end in 2023.

National benefits

Probability distribution library
A key high value initial output of NHS Bristol, North Somerset and South Gloucestershire CCG's work with the national HES data will be the creation of a directory of length of stay/arrival rate probability distributions for a wide range of service point types, which can be scaled to match local requirements based on locally estimated averages.

For example, distributions associated with component service points of a knee surgery pathway, identified by filtering the data by a combination of treatment specialty, diagnosis, and procedure codes (necessary to construct real-life services from the data – from experience, NHS Bristol, North Somerset and South Gloucestershire CCG knows that while simple use of treatment specialty codes or high level diagnostic measures in isolation may be useful for some accounting purposes, it maps poorly onto the operational representation of actual services).
Distributions will be derived at these generic service levels, but more specific results will also be produced by further filtering by demographics, geography and/or provider type. The purpose of this further filtering will be to distinguish expected unique characteristics of the service/component wards and clinics in a range of meaningfully defined practical settings. One such distinction of settings could be a knee surgery pathway at a large specialist urban hospital with a population skewed to a younger age range, versus the same service at a smaller rural hospital serving a substantially older population.

Depending on the service type, and whether filtering yields a sufficient quantity of records in the given setting, time-specific distributions may also be derived for some service types (to account for time of day, day of week, or time of year seasonal changes in arrival rates or lengths of stay).

Distributions will be fitted to the data using standard techniques (such as maximum likelihood, moment matching etc.), implemented through the statistical programming environment R, which is rapidly gaining traction as the tool of choice for advanced analytics in the NHS.
The resulting library of arrival and length of stay distributions will (by design) be equally useful to any CCG, STP, ICS or other system-planning NHS organisation (and indeed to providers engaged in planning of pathways substantively contained within their own organisations) with a sufficiently advanced analytic workforce. The distributions would form a set of generic building blocks which a wide range of hypothetical services, in a wide range of practical settings, could be constructed.

They will effectively form a reference set of expected measures which could be used to construct models of activity when designing a new service which does not currently exist, when modifying the configuration of an existing service, or when assessing the performance of an existing service against the assumed generic case. By design, they will be generically applicable to any area of the country – local circumstances will be accounted for by choosing a set of measures which capture characteristics which analysts/modellers intend to be present in their local system, rather than using the geographical source of the data as a proxy for these characteristics (except when a geographical variable is the explicit characteristic of interest, e.g. in distinguishing between urban and rural areas).

One direct use of these measures will be in demand and capacity models, for which length of stay is a key input. In constructing simulation models (such as discrete event simulation) – one of the barriers to uptake of simulation within the NHS is the lack of availability of appropriate data to inform such models. NHS Bristol, North Somerset and South Gloucestershire CCG has separately developed its own simulation software which will be distributed nationally to NHS organisations in the coming months, and the library of distributions constructed using HES data could be subsequently disseminated to organisations using that (or other) software.

Relationship between day of discharge and length of stay (and other measures)
NHS Bristol, North Somerset and South Gloucestershire CCG has already instigated a local study into the statistical relationship between the day of week on which patients are discharged and the total hospital length of stay of those groups of patients, and also into potentially associated measures such as 30-day readmission rates. The stability of the results is potentially affected by non-recurrent factors in local data but by using all-England HES data, subsetted explicitly for the relevant characteristics, it should be possible to derive stable results which are generally applicable nationally and across specific areas. These results could then be used to assess the desirability of interventions to modify day-of-week discharge patterns and resource allocation in local systems.

From the local investigations already performed, it appears that there could be substantial scope for improving hospital efficiency, since many discharges appear to be delayed over the weekend until early the following week. Reducing these delays through a 7-day service would reduce hospital length of stay by 10%, which would correspond to material bed number savings, while receiving none of the drawbacks. National data is required to test this finding further, in both accounting for potential sample size insufficiency and checking whether local results hold nationally.

Other projects
There are a number of other system-level projects, both in scoping and already started, for which national data would be beneficial. For example, in re-visiting the 85% bed occupancy target where work in underway to assess whether this commonly-used target for average bed occupancy should be tailored to ward and service types. The general principle is that more variability in arrivals and length of stay mean the average occupancy target should be pitched lower (in order to accommodate the greater impact of peaks and troughs), while less variability would mean being able to make more routine use out of the bed base with a lesser threat of damaging pinch points.

Outputs:

The following outputs are anticipated to be created upon accessing the HES data from NHS Digital.

Specialty-level growth projections with updated estimates taking into account national trends - this is a major project due for completion 2023.

Specialty-level length of stay estimates, with updated estimates taking into account national information - this is a major project due for completion 2023.

A range of similar analyses to determine appropriate metrics to support system-wide operational planning and monitoring (including but not restricted to demand and capacity planning). Full detail and timelines are to be reactively determined from having first completed initial examination and assessment of the data, but this is not anticipated to be extending beyond 2023.

The majority of these outputs (all of which will be aggregated with small number suppression in line with the HES analysis guide) will be contained within written Word document reports, PowerPoint Presentations and potentially interactive R Shiny dashboards for use within BNSSG CCG and the wider BNSSG NHS-funded system (including acute hospitals, community and mental health providers, and GPs).

Results may also be used in research articles for peer reviewed academic journals and be presented at healthcare or academic conferences. For example, the M&A team has previously published results of local analytics work in academic journals (the Journal of the Operational Research Society, and Operations Research for Healthcare), and presented findings to groups including Bristol Health Partners (a collaboration between local NHS organisations and universities), the NHS Wales Modelling Collaborative, and the annual conference of the Operational Research Society, and the annual meeting of The Health Foundation, in order to share important practical results with the wider NHS and healthcare research community. Similarly, results may be shared with NHS colleagues through engagement with forums such as the NHS-R Community, the Association of Healthcare Analysts, and through sharing of reports on forums such as the NHS Future Collaboration message board and by informal correspondence with colleagues at NHS England/Improvement and other CCGs/ICSs.

Sharing with other CCGs

A number of distribution channels for such outputs are currently in various stages of maturation:

The NHS-R Community (a national project backed by the Health Foundation and NHS England/Improvement which brings analysts working with the statistical software together through conferences, workshops, regional networks and a website, with long term intentions to create a shared computer code repository accessible to NHS analysts and partner organisations). NHS Bristol, North Somerset and South Gloucestershire CCG is an active participant in this network and is running workshops at its forthcoming annual conference, and would anticipate doing the same at future events, featuring the outputs of the work completed using the HES data.

Local and regional NHS/healthcare analytics networks.
NHS Bristol, North Somerset and South Gloucestershire CCG is a leader in a local west of England analytics network being developed by Bristol Health Partners and a range of providers and planners from their own area, including some with cross-boundary responsibilities. Part of the purpose of this network is to establish communications between analysts in separate organisations so they can directly share expertise, analytical outputs and computer code. NHS Bristol, North Somerset and South Gloucestershire CCG is also leading work setting up links which will form the basis of an extended network involving neighboring CCG areas (for example with Gloucestershire CCG). In addition, NHS Bristol, North Somerset and South Gloucestershire CCG is working with regional networks in other areas, for example it is running workshops for the West Midlands analytical network on novel population segmentation methods it has developed, including sharing computer code to perform the analysis, and would envisage doing the same with the results of the work done with the HES data.

There are already close working relationships with the University of Bath Management School Centre for Healthcare Innovation and Improvement (where one of the team is a visiting research fellow) and the University of Bristol Population Health Sciences Institute and Elizabeth Blackwell Institute, as well as the NIHR South-West Applied Research Collaboration (ARC). NHS Bristol, North Somerset and South Gloucestershire CCG would be able to share relevant high level outputs of the HES work with academics working in partnership with NHS organisations on defined projects. Availability of usable real-world data is well established a major barrier to realising practical benefits from research projects in academic statistical modelling and operational research (acknowledged in the work of the national Plethora project for example, which was set up in part to bridge the gap between academic knowledge/technique generation and practical application of those outputs by healthcare analysts at the NHS coalface).
Dissemination of findings would also include posting of relevant results and outputs on the future NHS Collaboration platform, in addition to posting of relevant code, reusable output summary data, documentation, and (high level aggregate) data (with small number suppression) to an NHS BNSSG Analytics GitHub repository (subject to IG approval).

Publication of practical studies performed using the HES data in academic journals. This could include case studies of simulation projects carried out using the data, or specific statistical analyses. For example, the applicant is currently working on an investigation into the effect of day of week of hospital discharge on total length of stay, and is subsequent association with other factors such as readmission rates. These are live questions in local policy formulation and are equally relevant to other organisations.

All outputs will only contain aggregated data with small number suppression applied in line with the HES analysis guide.

Processing:

All organisations party to this agreement must comply with the Data Sharing Framework Contract requirements, including those regarding the use (and purposes of that use) by “Personnel” (as defined within the Data Sharing Framework Contract i.e.: employees, agents and contractors of the Data Recipient who may have access to that data).

NHS Digital will provide HES APC (Admitted Patient Care), A&E (Accident and Emergency) and OP (Outpatient) data to BNSSG CCG, via NHS South Central and West Commissioning Support Unit (SCWCSU), which provides IT and database administration support services to the CCG. The data controller will be the BNSSG CCG, and they will also be the main data processor. SCWCSU will also be a data processor, but its activities will be restricted to download and storage of the data on behalf of BNSSG CCG. The CSU/CCG infrastructure is within the NHS’s secure N3 network and the HES data will be stored in a secure MS SQL Server data warehouse within that infrastructure. Access to that data will be restricted, through Microsoft Active Directory access control, solely to permanent staff members of BNSSG CCG - specifically the M&A team. SCWCSU will act only upon specific instruction from BNSSG CCG M&A on this project, and the data will be stored only at those addresses listed in the storage addresses section of this application. There will be no flow of data into NHS Digital. All substantive analysis and use of the record level data will be restricted to the BNSSG CCG M&A team - all of who are substantive employees of the organisation.

Data minimisation has been applied through restricting the timeframe of the data requested to the most recent three years only, by requesting only pseudonymised data, and by excluding those fields classified as “sensitive”. Further minimisation, such as filtering by age or geography or excluding specific data fields from the request, were considered but deemed incompatible with the purposes specified under “objective for processing” above.

The HES data will not be available to third parties and will not be taken out of BNSSG CCG offices. Record level data will not be shared outside the BNSSG CCG. Results of analysis will be at an aggregate level, and will be shared only at that level (in the form of graphs, summary statistics, and parameters to derived probability distributions), with small number suppression applied as per HES guidelines. It will not be possible to identify specific patients, clinicians or other individuals from these outputs.

The data will not be onwardly linked with any other record-level patient data. The narrative descriptions of coded data values in the HES extract (e.g. treatment function codes, administrative category codes etc.) may be looked up in the NHS Data Dictionary.

Data will relate to the whole of England, not just the registered population of BNSSG CCG – this is an explicit requirement of the project, as explained above in the “objective for processing” section.

Results of the analysis will be aggregated to service/pathway level, incorporating small number suppression as per the HES guidelines where necessary, and will not identify (or allow the identification of) specific patients, clinicians or other individuals.

Project 4 — DARS-NIC-163839-T7M7M

Type of data: information not disclosed for TRE projects

Opt outs honoured: Yes - patient objections upheld ()

Legal basis: National Health Service Act 2006 - s251 - 'Control of patient information'.

Purposes: ()

Sensitive: Sensitive

When:2018.10 — 2019.04.

Access method: Frequent Adhoc Flow

Data-controller type:

Sublicensing allowed:

Datasets:

  1. SUS for Commissioners

Objectives:

Invoice Validation
Invoice validation is part of a process by which providers of care or services get paid for the work they do.
Invoices are submitted to the Clinical Commissioning Group (CCG) so they are able to ensure that the activity claimed for each patient is their responsibility. This is done by processing and analysing Secondary User Services (SUS+) data, which is received into a secure Controlled Environment for Finance (CEfF). The SUS+ data is identifiable at the level of NHS number. The NHS number is only used to confirm the accuracy of backing-data sets and will not be used further.
The legal basis for this to occur is under Section 251 of NHS Act 2006.
Invoice Validation with be conducted by the CCG.

Expected Benefits:

Invoice Validation
1. Financial validation of activity
2. CCG Budget control
3. Commissioning and performance management
4. Meeting commissioning objectives without compromising patient confidentiality
5. The avoidance of misappropriation of public funds to ensure the ongoing delivery of patient care

Outputs:

Invoice Validation
1. Addressing poor data quality issues
2. Production of reports for business intelligence
3. Budget reporting
4. Validation of invoices for non-contracted events

Processing:

Data must only be used as stipulated within this Data Sharing Agreement.

Data Processors must only act upon specific instructions from the Data Controller.

Data can only be stored at the addresses listed under storage addresses.

The Data Controller and any Data Processor will only have access to records of patients of residence and registration within the CCG.

Patient level data will not be shared outside of the CCG unless it is for the purpose of Direct Care, where it may be shared only with those health professionals who have a legitimate relationship with the patient and a legitimate reason to access the data.

No patient level data will be linked other than as specifically detailed within this agreement. Data will only be shared with those parties listed and will only be used for the purposes laid out in the application/agreement. The data to be released from NHS Digital will not be national data, but only that data relating to the specific locality of interest of the applicant.
The DSCRO (part of NHS Digital) will apply Type 2 objections before any identifiable data leaves the DSCRO.
NHS Digital reminds all organisations party to this agreement of the need to comply with the Data Sharing Framework Contract requirements, including those regarding the use (and purposes of that use) by “Personnel” (as defined within the Data Sharing Framework Contract ie: employees, agents and contractors of the Data Recipient who may have access to that data)

Segregation
Where the Data Processor and/or the Data Controller hold both identifiable and pseudonymised data, the data will be held separately so data cannot be linked.
All access to data is audited

Data for the purpose of Invoice Validation is kept within the CEfF, and only used by staff properly trained and authorised for the activity. Only CEfF staff are able to access data in the CEfF and only CEfF staff operate the invoice validation process within the CEfF. Data flows directly in to the CEfF from the DSCRO and from the providers – it does not flow through any other processors.

Invoice Validation
1. SUS+ Data is obtained from the SUS+ Repository by the Data Services for Commissioners Regional Office (DSCRO).
2. The DSCRO pushes a one-way data flow of SUS+ data into the Controlled Environment for Finance (CEfF) located in the CCG.
3. The CEfF conduct the following processing activities for invoice validation purposes:
a. Checking the individual is registered to the Clinical Commissioning Group (CCG) by using the derived commissioner field in SUS+ and associated with an invoice from the SUS data flow to validate the corresponding record in the backing data flow
b. Once the backing information is received, this will be checked against national NHS and local commissioning policies as well as being checked against system access and reports provided by NHS Digital to confirm the payments are:
i. In line with Payment by Results tariffs
ii. In relation to a patient registered with the CCG GP or resident within the CCG area.
iii. The health care provided should be paid by the CCG in line with CCG guidance. 
4. The CCG are notified by the CEfF that the invoice has been validated and can be paid. Any discrepancies or non-validated invoices are investigated and resolved

Project 5 — DARS-NIC-163830-B6X7B

Type of data: information not disclosed for TRE projects

Opt outs honoured: Yes - patient objections upheld ()

Legal basis: National Health Service Act 2006 - s251 - 'Control of patient information'.

Purposes: ()

Sensitive: Sensitive

When:2018.10 — 2019.04.

Access method: Frequent Adhoc Flow

Data-controller type:

Sublicensing allowed:

Datasets:

  1. SUS for Commissioners

Objectives:

Invoice Validation
Invoice validation is part of a process by which providers of care or services get paid for the work they do.

Invoices are submitted to the Clinical Commissioning Group (CCG) so they are able to ensure that the activity claimed for each patient is their responsibility. This is done by processing and analysing Secondary User Services (SUS+) data, which is received into a secure Controlled Environment for Finance (CEfF). The SUS+ data is identifiable at the level of NHS number. The NHS number is only used to confirm the accuracy of backing-data sets and will not be used further.

The legal basis for this to occur is under Section 251 of NHS Act 2006.

Invoice Validation with be conducted by the CCG.

Expected Benefits:

Invoice Validation
1. Financial validation of activity
2. CCG Budget control
3. Commissioning and performance management
4. Meeting commissioning objectives without compromising patient confidentiality
5. The avoidance of misappropriation of public funds to ensure the ongoing delivery of patient care

Outputs:

Invoice Validation
1. Addressing poor data quality issues
2. Production of reports for business intelligence
3. Budget reporting
4. Validation of invoices for non-contracted events

Processing:

Data must only be used as stipulated within this Data Sharing Agreement.

Data Processors must only act upon specific instructions from the Data Controller.

Data can only be stored at the addresses listed under storage addresses.

The Data Controller and any Data Processor will only have access to records of patients of residence and registration within the CCG.

Patient level data will not be shared outside of the CCG unless it is for the purpose of Direct Care, where it may be shared only with those health professionals who have a legitimate relationship with the patient and a legitimate reason to access the data.

No patient level data will be linked other than as specifically detailed within this agreement. Data will only be shared with those parties listed and will only be used for the purposes laid out in the application/agreement. The data to be released from NHS Digital will not be national data, but only that data relating to the specific locality of interest of the applicant.
The DSCRO (part of NHS Digital) will apply Type 2 objections before any identifiable data leaves the DSCRO.
NHS Digital reminds all organisations party to this agreement of the need to comply with the Data Sharing Framework Contract requirements, including those regarding the use (and purposes of that use) by “Personnel” (as defined within the Data Sharing Framework Contract ie: employees, agents and contractors of the Data Recipient who may have access to that data)

Segregation
Where the Data Processor and/or the Data Controller hold both identifiable and pseudonymised data, the data will be held separately so data cannot be linked.
All access to data is audited

Data for the purpose of Invoice Validation is kept within the CEfF, and only used by staff properly trained and authorised for the activity. Only CEfF staff are able to access data in the CEfF and only CEfF staff operate the invoice validation process within the CEfF. Data flows directly in to the CEfF from the DSCRO and from the providers – it does not flow through any other processors.

Invoice Validation
1. SUS+ Data is obtained from the SUS+ Repository by the Data Services for Commissioners Regional Office (DSCRO).
2. The DSCRO pushes a one-way data flow of SUS+ data into the Controlled Environment for Finance (CEfF) located in the CCG.
3. The CEfF conduct the following processing activities for invoice validation purposes:
a. Checking the individual is registered to the Clinical Commissioning Group (CCG) by using the derived commissioner field in SUS+ and associated with an invoice from the SUS data flow to validate the corresponding record in the backing data flow
b. Once the backing information is received, this will be checked against national NHS and local commissioning policies as well as being checked against system access and reports provided by NHS Digital to confirm the payments are:
i. In line with Payment by Results tariffs
ii. In relation to a patient registered with the CCG GP or resident within the CCG area.
iii. The health care provided should be paid by the CCG in line with CCG guidance. 
4. The CCG are notified by the CEfF that the invoice has been validated and can be paid. Any discrepancies or non-validated invoices are investigated and resolved

Project 6 — DARS-NIC-163816-G8W9C

Type of data: information not disclosed for TRE projects

Opt outs honoured: Yes - patient objections upheld ()

Legal basis: National Health Service Act 2006 - s251 - 'Control of patient information'.

Purposes: ()

Sensitive: Sensitive

When:2018.10 — 2019.04.

Access method: Frequent Adhoc Flow

Data-controller type:

Sublicensing allowed:

Datasets:

  1. SUS for Commissioners

Objectives:

Invoice Validation
Invoice validation is part of a process by which providers of care or services get paid for the work they do.
Invoices are submitted to the Clinical Commissioning Group (CCG) so they are able to ensure that the activity claimed for each patient is their responsibility. This is done by processing and analysing Secondary User Services (SUS+) data, which is received into a secure Controlled Environment for Finance (CEfF). The SUS+ data is identifiable at the level of NHS number. The NHS number is only used to confirm the accuracy of backing-data sets and will not be used further.
The legal basis for this to occur is under Section 251 of NHS Act 2006.
Invoice Validation with be conducted by the CCG.

Expected Benefits:

Invoice Validation
1. Financial validation of activity
2. CCG Budget control
3. Commissioning and performance management
4. Meeting commissioning objectives without compromising patient confidentiality
5. The avoidance of misappropriation of public funds to ensure the ongoing delivery of patient care

Outputs:

Invoice Validation
1. Addressing poor data quality issues
2. Production of reports for business intelligence
3. Budget reporting
4. Validation of invoices for non-contracted events

Processing:

Data must only be used as stipulated within this Data Sharing Agreement.

Data Processors must only act upon specific instructions from the Data Controller.

Data can only be stored at the addresses listed under storage addresses.

The Data Controller and any Data Processor will only have access to records of patients of residence and registration within the CCG.

Patient level data will not be shared outside of the CCG unless it is for the purpose of Direct Care, where it may be shared only with those health professionals who have a legitimate relationship with the patient and a legitimate reason to access the data.

No patient level data will be linked other than as specifically detailed within this agreement. Data will only be shared with those parties listed and will only be used for the purposes laid out in the application/agreement. The data to be released from NHS Digital will not be national data, but only that data relating to the specific locality of interest of the applicant.
The DSCRO (part of NHS Digital) will apply Type 2 objections before any identifiable data leaves the DSCRO.
NHS Digital reminds all organisations party to this agreement of the need to comply with the Data Sharing Framework Contract requirements, including those regarding the use (and purposes of that use) by “Personnel” (as defined within the Data Sharing Framework Contract ie: employees, agents and contractors of the Data Recipient who may have access to that data)

Segregation
Where the Data Processor and/or the Data Controller hold both identifiable and pseudonymised data, the data will be held separately so data cannot be linked.
All access to data is audited

Data for the purpose of Invoice Validation is kept within the CEfF, and only used by staff properly trained and authorised for the activity. Only CEfF staff are able to access data in the CEfF and only CEfF staff operate the invoice validation process within the CEfF. Data flows directly in to the CEfF from the DSCRO and from the providers – it does not flow through any other processors.

Invoice Validation
1. SUS+ Data is obtained from the SUS+ Repository by the Data Services for Commissioners Regional Office (DSCRO).
2. The DSCRO pushes a one-way data flow of SUS+ data into the Controlled Environment for Finance (CEfF) located in the CCG.
3. The CEfF conduct the following processing activities for invoice validation purposes:
a. Checking the individual is registered to the Clinical Commissioning Group (CCG) by using the derived commissioner field in SUS+ and associated with an invoice from the SUS data flow to validate the corresponding record in the backing data flow
b. Once the backing information is received, this will be checked against national NHS and local commissioning policies as well as being checked against system access and reports provided by NHS Digital to confirm the payments are:
i. In line with Payment by Results tariffs
ii. In relation to a patient registered with the CCG GP or resident within the CCG area.
iii. The health care provided should be paid by the CCG in line with CCG guidance. 
4. The CCG are notified by the CEfF that the invoice has been validated and can be paid. Any discrepancies or non-validated invoices are investigated and resolved .

Project 7 — DARS-NIC-160394-Q0X1C

Type of data: information not disclosed for TRE projects

Opt outs honoured: No - data flow is not identifiable ()

Legal basis: Health and Social Care Act 2012 – s261(1) and s261(2)(b)(ii)

Purposes: ()

Sensitive: Sensitive

When:2018.10 — 2019.04.

Access method: Frequent Adhoc Flow

Data-controller type:

Sublicensing allowed:

Datasets:

  1. Acute-Local Provider Flows
  2. Ambulance-Local Provider Flows
  3. Children and Young People Health
  4. Community Services Data Set
  5. Community-Local Provider Flows
  6. Demand for Service-Local Provider Flows
  7. Diagnostic Imaging Dataset
  8. Diagnostic Services-Local Provider Flows
  9. Emergency Care-Local Provider Flows
  10. Experience, Quality and Outcomes-Local Provider Flows
  11. Improving Access to Psychological Therapies Data Set
  12. Maternity Services Data Set
  13. Mental Health and Learning Disabilities Data Set
  14. Mental Health Minimum Data Set
  15. Mental Health Services Data Set
  16. Mental Health-Local Provider Flows
  17. Other Not Elsewhere Classified (NEC)-Local Provider Flows
  18. Population Data-Local Provider Flows
  19. Primary Care Services-Local Provider Flows
  20. Public Health and Screening Services-Local Provider Flows
  21. SUS for Commissioners

Objectives:


Commissioning
To use pseudonymised data to provide intelligence to support the commissioning of health services. The data (containing both clinical and financial information) is analysed so that health care provision can be planned to support the needs of the population within the CCG area.
The CCGs commission services from a range of providers covering a wide array of services. Each of the data flow categories requested supports the commissioned activity of one or more providers.

The CCG is part of the Bristol, North Somerset and South Gloucestershire Sustainable Transformation Partnership. The STP is responsible for implementing large parts of the 5 year forward view from NHS England. The STP is implementing several initiatives:

- Putting the patient at the heart of the health system
- Working across organisational boundaries to deliver care and including social care, public Health, providers and GPs as well as CCGs
- Reviewing patient pathways to improve patient experience whilst reducing costs e.g. reduce the number of standard tests a patient may have and only have the ones they need
- Planning the demand and capacity across the healthcare system across 3 CCGs to ensure we have the right buildings, services and staff to cope with demand whilst reducing the impact on costs
- Working to prevent or capture conditions early as they are cheaper to treat
- Introduce initiatives to change behaviours e.g. move more care into the community
- Patient pathway planning for the above


To ensure the patient is at the heart of care, the STP is focussing on where services are required across the geographical region. This assists to ensure delivery of care in the right place for patients who may move and change services across CCGs.

The CCG will work proactively and collaboratively with the other CCGs in the STP to redesign services across boundaries to integrate services. Collaborative sharing is required for CCGs to understand these requirements.

The following pseudonymised datasets are required to provide intelligence to support commissioning of health services:
- Secondary Uses Service (SUS)
- Local Provider Flows
o Acute
o Ambulance
o Community
o Demand for Service
o Diagnostic Service
o Emergency Care
o Experience, Quality and Outcomes
o Mental Health
o Other Not Elsewhere Classified
o Population Data
o Primary Care Services
o Public Health Screening
- Mental Health Minimum Data Set (MHMDS)
- Mental Health Learning Disability Data Set (MHLDDS)
- Mental Health Services Data Set (MHSDS)
- Maternity Services Data Set (MSDS)
- Improving Access to Psychological Therapy (IAPT)
- Child and Young People Health Service (CYPHS)
- Community Services Data Set (CSDS)
- Diagnostic Imaging Data Set (DIDS)
The pseudonymised data is required to for the following purposes:
 Population health management:
• Understanding the interdependency of care services
• Targeting care more effectively
• Using value as the redesign principle
• Ensuring we do what we should
 Data Quality and Validation – allowing data quality checks on the submitted data
 Thoroughly investigating the needs of the population, to ensure the right services are available for individuals when and where they need them
 Understanding cohorts of residents who are at risk of becoming users of some of the more expensive services, to better understand and manage those needs
 Monitoring population health and care interactions to understand where people may slip through the net, or where the provision of care may be being duplicated
 Modelling activity across all data sets to understand how services interact with each other, and to understand how changes in one service may affect flows through another
 Service redesign
 Health Needs Assessment – identification of underlying disease prevalence within the local population
 Patient stratification and predictive modelling - to identify specific patients at risk of requiring hospital admission and other avoidable factors such as risk of falls, computed using algorithms executed against linked de-identified data, and identification of future service delivery models

The pseudonymised data is required to ensure that analysis of health care provision can be completed to support the needs of the health profile of the population within the CCG area based on the full analysis of multiple pseudonymised datasets.
Processing for commissioning will be conducted by
- South, Central and West Commissioning Support Unit
- Outcomes Based Health

Expected Benefits:


Commissioning
1. Supporting Quality Innovation Productivity and Prevention (QIPP) to review demand management, integrated care and pathways.
a. Analysis to support full business cases.
b. Develop business models.
c. Monitor In year projects.
2. Supporting Joint Strategic Needs Assessment (JSNA) for specific disease types.
3. Health economic modelling using:
a. Analysis on provider performance against 18 weeks wait targets.
b. Learning from and predicting likely patient pathways for certain conditions, in order to influence early interventions and other treatments for patients.
c. Analysis of outcome measures for differential treatments, accounting for the full patient pathway.
d. Analysis to understand emergency care and linking A&E and Emergency Urgent Care Flows (EUCC).
4. Commissioning cycle support for grouping and re-costing previous activity.
5. Enables monitoring of:
a. CCG outcome indicators.
b. Financial and Non-financial validation of activity.
c. Successful delivery of integrated care within the CCG.
d. Checking frequent or multiple attendances to improve early intervention and avoid admissions.
e. Case management.
f. Care service planning.
g. Commissioning and performance management.
h. List size verification by GP practices.
i. Understanding the care of patients in nursing homes.
6. Feedback to NHS service providers on data quality at an aggregate and individual record level – only on data initially provided by the service providers.
7. Improved planning by better understanding patient flows through the healthcare system, thus allowing commissioners to design appropriate pathways to improve patient flow and allowing commissioners to identify priorities and identify plans to address these.
8. Improved quality of services through reduced emergency readmissions, especially avoidable emergency admissions. This is achieved through mapping of frequent users of emergency services and early intervention of appropriate care.
9. Improved access to services by identifying which services may be in demand but have poor access, and from this identify areas where improvement is required.
10. Potentially reduced premature mortality by more targeted intervention in primary care, which supports the commissioner to meets its requirement to reduce premature mortality in line with the CCG Outcome Framework.
11. Better understanding of the health of and the variations in health outcomes within the population to help understand local population characteristics.
12. Better understanding of contract requirements, contract execution, and required services for management of existing contracts, and to assist with identification and planning of future contracts
13. Insights into patient outcomes, and identification of the possible efficacy of outcomes-based contracting opportunities.
14. Reviewing current service provision
a. Cost-benefit analysis and service impact assessments to underpin service transformation across health economy
b. Service planning and re-design (development of NMoC and integrated care pathways, new partnerships, working with new providers etc.)
c. Impact analysis for different models or productivity measures, efficiency and experience
d. Service and pathway review
e. Service utilisation review
15. Ensuring compliance with evidence and guidance
a. Testing approaches with evidence and compliance with guidance.
16. Monitoring outcomes
a. Analysis of variation in outcomes across population group
17. Understanding how services impact across the health economy
a. Service evaluation
b. Programme reviews
c. Analysis of productivity, outcomes, experience, plan, targets and actuals
d. Assessing value for money and efficiency gains
e. Understanding impact of services on health inequalities
18. Understanding how services impact on the health of the population and patient cohorts
a. Measuring and assessing improvement in service provision, patient experience & outcomes and the cost to achieve this
b. Propensity matching and scoring
c. Triple aim analysis
19. Understanding future drivers for change across health economy
a. Forecasting health and care needs for population and population cohorts across STPs
b. Identifying changes in disease trends and prevalence
c. Efficiencies that can be gained from procuring services across wider footprints, from new innovations
d. Predictive modelling
20. Delivering services that meet changing needs of population
a. Analysis to support policy development
b. Ethical and equality impact assessments
c. Implementation of NMOC
d. What do next years contracts need to include?
e. Workforce planning
21. Maximising services and outcomes within financial envelopes across health economy
a. What-if analysis
b. Cost-benefit analysis
c. Health economics analysis
d. Scenario planning and modelling
e. Investment and disinvestment in services analysis
f. Opportunity analysis

Outcomes Based Healthcare:

Outcomes are often described as those things that matter to people, and are typically the end results of care across complete care pathways. Access to a population-level view of all people with relevant long-term conditions, in near real-time, is the essential starting point for accurate outcome measurement. It is also an essential enabler supporting quality process improvement within care pathways.

Outcomes measured using existing clinical and administrative data typically measure the reduction in illness, disease and complications, and their severity, in addition to system activity metrics.

Measuring outcomes aims to refocus providers (including health and social care providers) to work together to reduce the burden of disease. By setting longer-term targets and improvement trajectories for each outcome, providers can focus their efforts on improving these outcomes, for specific population groups, over a period of years.

Outputs:


Commissioning
1. Commissioner reporting:
a. Summary by provider view - plan & actuals year to date (YTD).
b. Summary by Patient Outcome Data (POD) view - plan & actuals YTD.
c. Summary by provider view - activity & finance variance by POD.
d. Planned care by provider view - activity & finance plan & actuals YTD.
e. Planned care by POD view - activity plan & actuals YTD.
f. Provider reporting.
g. Statutory returns.
h. Statutory returns - monthly activity return.
i. Statutory returns - quarterly activity return.
j. Delayed discharges.
k. Quality & performance referral to treatment reporting.
2. Readmissions analysis.
3. Production of aggregate reports for CCG Business Intelligence.
4. Production of project / programme level dashboards.
5. Monitoring of acute / community / mental health quality matrix.
6. Clinical coding reviews / audits.
7. Budget reporting down to individual GP Practice level.
8. GP Practice level dashboard reports include high flyers.
9. Comparators of CCG performance with similar CCGs as set out by a specific range of care quality and performance measures detailed activity and cost reports
10. Data Quality and Validation measures allowing data quality checks on the submitted data
11. Contract Management and Modelling
12. Patient Stratification, such as:
o Patients at highest risk of admission
o Most expensive patients (top 15%)
o Frail and elderly
o Patients that are currently in hospital
o Patients with most referrals to secondary care
o Patients with most emergency activity
o Patients with most expensive prescriptions
o Patients recently moving from one care setting to another
i. Discharged from hospital
ii. Discharged from community
13. Profiling population health and wider determinants to identify and target those most in need
a. Understanding population profile and demographics
b. Identify patient cohorts with specific needs or who may benefit from interventions
c. Identifying disease prevalence. health and care needs for population cohorts
d. Contributing to Joint Strategic Needs Assessment (JSNA)
e. Geographical mapping and analysis
14. Identifying and managing preventable and existing conditions
a. Identifying types of individuals and population cohorts at risk of non-elective re-admission
b. Risk stratification to identify populations suitable for case management
c. Risk profiling and predictive modelling
d. Risk stratification for planning services for population cohorts
e. Identification of disease incidence and diagnosis stratification
15. Reducing health inequalities
a. Identifying cohorts of patients who have worse health outcomes typically deprived, ethnic groups, homeless, travellers etc. to enable services to proactively target their needs
b. Socio-demographic analysis
16. Managing demand
a. Waiting times analysis
b. Service demand and supply modelling
c. Understanding cross-border and overseas visitor
d. Winter planning
e. Emergency preparedness, business continuity, recovery and contingency planning
17. Care co-ordination and planning
a. Planning packages of care
b. Service planning
c. Planning care co-ordination
18. Monitoring individual patient health, service utilisation, pathway compliance experience & outcomes across the heath and care system
a. Patient pathway analysis across health and care
b. Outcomes & experience analysis
c. Analysis to support anti-terror initiatives
d. Analysis to identify vulnerable patients with potential safeguarding issues
e. Understanding equity of care and unwarranted variation
f. Modelling patient flow
g. Tracking patient pathways
h. Monitoring to support NMoC, ACOs, STPs
i. Identifying duplications in care
j. Identifying gaps in care, missed diagnoses and triple fail events
k. Analysing individual and aggregated timelines
19. Undertaking budget planning, management and reporting
a. Tracking financial performance against plans
b. Budget reporting
c. Tariff development
d. Developing and monitoring capitated budgets
e. Developing and monitoring individual-level budgets
f. Future budget planning and forecasting
g. Paying for care of overseas visitors and cross-border flow
20. Monitoring the value for money
a. Service-level costing & comparisons
b. Identification of cost pressures
c. Cost benefit analysis
d. Equity of spend across services and population cohorts
e. Finance impact assessment
21. Comparing population groups, peers, national and international best practice
a. Identification of variation in productivity, cost, outcomes, quality, experience, compared with peers, national and international & best practice
b. Benchmarking against other parts of the country
c. Identifying unwarranted variations
22. Comparing expected levels
a. Standardised comparisons for prevalence, activity, cost, quality, experience, outcomes for given populations
23. Comparing local targets & plan
a. Monitoring of local variation in productivity, cost, outcomes, quality and experience
b. Local performance dashboards by service provider, commissioner, geography, NMOC, STPs
24. Monitoring activity and cost compliance against contract and agreed plans
a. Contract monitoring
b. Contract reconciliation and challenge
c. Invoice validation
25. Monitoring provider quality, demand, experience and outcomes against contract and agreed plans
a. Performance dashboards
b. CQUIN reporting
c. Clinical audit
d. Patient experience surveys
e. Demand, supply, outcome & experience analysis
f. Monitoring cross-border flows and overseas visitor activity
26. Improving provider data quality
a. Coding audit
b. Data quality validation and review
c. Checking validity of patient identity and commissioner assignment

Outcomes Based Healthcare:
The main outputs are aggregated monthly values for each outcome (with small number suppression, including any values under 5). The aggregated monthly outcomes data will be made available through OBH’s online tool (available to named individuals in the CCG and CCG commissioned providers only) via a secure login.
This enables the CCGs and to:
- visualise baselines for each outcome
- set improvement trajectories
- monitor outcomes on an on-going basis.
In addition, the CCGs will receive an information schedule describing the outcomes to be monitored, the technical description, and annual baseline data for each outcome.
All outputs will be delivered within the timescales of the contract between OBH and the CCGs.

Processing:


Data must only be used as stipulated within this Data Sharing Agreement.

Data Processors must only act upon specific instructions from the Data Controller.

Data can only be stored at the addresses listed under storage addresses.

The Data Controller and any Data Processor will only have access to records of patients of residence and registration within the Bristol, North Somerset and South Gloucestershire Sustainable Transformation Partnership.

No patient level data will be linked other than as specifically detailed within this agreement. Data will only be shared with those parties listed and will only be used for the purposes laid out in the application/agreement. The data to be released from NHS Digital will not be national data, but only that data relating to the specific locality of interest of the applicant.
NHS Digital reminds all organisations party to this agreement of the need to comply with the Data Sharing Framework Contract requirements, including those regarding the use (and purposes of that use) by “Personnel” (as defined within the Data Sharing Framework Contract ie: employees, agents and contractors of the Data Recipient who may have access to that data)

Segregation
Where the Data Processor and/or the Data Controller hold both identifiable and pseudonymised data, the data will be held separately so data cannot be linked.
All access to data is audited


Commissioning
The Data Services for Commissioners Regional Office (DSCRO) obtains the following data sets:
1. SUS
2. Local Provider Flows (received directly from providers)
o Acute
o Ambulance
o Community
o Demand for Service
o Diagnostic Service
o Emergency Care
o Experience, Quality and Outcomes
o Mental Health
o Other Not Elsewhere Classified
o Population Data
o Primary Care Services
o Public Health Screening
3. Mental Health Minimum Data Set (MHMDS)
4. Mental Health Learning Disability Data Set (MHLDDS)
5. Mental Health Services Data Set (MHSDS)
6. Maternity Services Data Set (MSDS)
7. Improving Access to Psychological Therapy (IAPT)
8. Child and Young People Health Service (CYPHS)
9. Community Services Data Set (CSDS)
10. Diagnostic Imaging Data Set (DIDS)
Data quality management and pseudonymisation is completed within the DSCRO and is then disseminated as follows:
Data Processor 1 - South, Central and West Commissioning Support Unit
1. Pseudonymised SUS, Local Provider data, Mental Health data (MHSDS, MHMDS, MHLDDS), Maternity data (MSDS), Improving Access to Psychological Therapies data (IAPT), Child and Young People’s Health data (CYPHS), Community Services Data Set (CSDS) and Diagnostic Imaging data (DIDS) only is held until points 2 – 8 are completed.
2. South, Central and West Commissioning Support Unit receives GP data. GP Data is received as follows:
o Identifiable GP data is submitted to South Central and West Commissioning Support Unit.
o The identifiable data lands in a ring-fenced area for GP data only.
o The GP data is pseudonymised using a pseudonymisation tool, different to that used by the DSCRO.
o There is a Data Processing Agreement in place between the GP and South Central and West Commissioning Support Unit. A specific named individual within South Central and West Commissioning Support Unit acts on behalf of the GP.
o This individual has access to a black box. The pseudonymised data is passed through the black box process where the pseudonymisation is mapped to the pseudonymisation used by the DSCRO.
o Once mapped, the data is passed into South Central and West Commissioning Support, but before South Central and West Commissioning Support Unit will receive the data from the ring-fenced area, they require confirmation that the identifiable data has been deleted.
o South Central and West Commissioning Support Unit are then sent the pseudonymised GP data with the pseudo algorithm specific to them.
3. South, Central and West Commissioning Support Unit also receive a flow of social care data. Social Care data is received in one of the following 2 ways:
o Pseudonymised:
 Social Care data is pseudonymised within the provider using a pseudonymisation tool, different to that used by the DSCRO. The provider requests a pseudonymisation key from the DSCRO. The key can only be used once. The key is specific to the Local Authority and to that specific date.
 The pseudonymised data lands in a ring-fenced area for social care data only.
 There is a Data Processing Agreement in place between the Provider and South Central and West Commissioning Support Unit. A specific named individual within South Central and West Commissioning Support Unit acts on behalf of the Provider.
 This individual has access to a black box. The pseudonymised data is passed through the black box process where the pseudonymisation is mapped to the pseudonymisation used by the DSCRO.
 The data is then passed into the non-ringfenced area with the pseudo algorithm specific to them.
o Identifiable:
 Identifiable social care data is submitted to South Central and West Commissioning Support Unit.
 The identifiable data lands in a ring-fenced area for social care data only.
 The social care data is pseudonymised using a pseudonymisation tool, different to that used by the DSCRO.
 There is a Data Processing Agreement in place between the Local Authority and South Central and West Commissioning Support Unit. A specific named individual within South Central and West Commissioning Support Unit acts on behalf of the provider.
 This individual has access to a black box. The pseudonymised data is passed through the black box process where the pseudonymisation is mapped to the pseudonymisation used by the DSCRO.
 Once mapped, the data is passed into South Central and West Commissioning Support, but before South Central and West Commissioning Support Unit will receive the data from the ring-fenced area, they require confirmation that the identifiable data has been deleted.
 South Central and West Commissioning Support Unit are then sent the pseudonymised social care data with the pseudo algorithm specific to them.
4. Once the pseudonymised GP data and social care data is received, South, Central and West Commissioning Support Unit make a request to the DSCRO.
5. The DSCRO check the dates of the key generation (Point 2d and 3aii/3biv).
6. The DSCRO then send a mapping table to South, Central and West Commissioning Support Unit
7. South, Central and West Commissioning Support Unit then overwrite the organisation specific keys with the DSCRO key.
8. The mapping table is then deleted.
9. The DSCRO pass the pseudonymised SUS, local provider data, Mental Health (MHSDS, MHMDS, MHLDDS), Maternity (MSDS), Improving Access to Psychological Therapies (IAPT), Child and Young People’s Health (CYPHS) and Diagnostic Imaging (DIDS) securely to South, Central and West Commissioning Support Unit for the addition of derived fields, linkage of data sets and analysis
10. Social care data is then linked to the data sets listed within point 9.
11. South, Central and West Commissioning Support Unit then pass the processed, pseudonymised and linked data to the CCG.
12. Aggregation of required data for CCG management use will be completed by South, Central and West Commissioning Support Unit as instructed by the CCG.
13. Patient level data will not be shared outside of the CCG and will only be shared within the CCG on a need to know basis, as per the purposes stipulated within the Data Sharing Agreement. External aggregated reports only with small number suppression can be shared as set out within NHS Digital guidance applicable to each data set.


Data Processor 2 - Outcomes Based Health
1. Pseudonymised SUS+ (pseudonymised using an encryption key’ specific to this project), only is securely transferred from the DSCRO to South, Central and West Commissioning Support Unit..
2. South, Central and West Commissioning Support Unit add derived fields and securely transfer the data to the CCG.
3. The CCG passes the pseudonymised data to Outcomes Based Health
4. Outcomes Based Health then analyse the data to:
o Develop outcomes, baselining and monitoring of individual outcomes
o Provide detailed analysis relating to outcomes
5. Aggregation of required data for CCG management use will be completed by Outcomes Based Health or the CCG as instructed by the CCG.
6. Patient level data will not be shared outside of the CCG and will only be shared within the CCG on a need to know basis, as per the purposes stipulated within the Data Sharing Agreement. External aggregated reports only with small number suppression can be shared as set out within NHS Digital guidance applicable to each data set.

Project 8 — NIC-163839-T7M7M

Type of data: information not disclosed for TRE projects

Opt outs honoured: Y ()

Legal basis: Section 251 approval is in place for the flow of identifiable data

Purposes: ()

Sensitive: Sensitive

When:2018.03 — 2018.05.

Access method: Ongoing

Data-controller type:

Sublicensing allowed:

Datasets:

  1. SUS for Commissioners

Objectives:

Invoice Validation
Invoice validation is part of a process by which providers of care or services get paid for the work they do.
Invoices are submitted to the Clinical Commissioning Group (CCG) so they are able to ensure that the activity claimed for each patient is their responsibility. This is done by processing and analysing Secondary User Services (SUS+) data, which is received into a secure Controlled Environment for Finance (CEfF). The SUS+ data is identifiable at the level of NHS number. The NHS number is only used to confirm the accuracy of backing-data sets and will not be used further.
The legal basis for this to occur is under Section 251 of NHS Act 2006.
Invoice Validation with be conducted by the CCG.

Expected Benefits:

Invoice Validation
1. Financial validation of activity
2. CCG Budget control
3. Commissioning and performance management
4. Meeting commissioning objectives without compromising patient confidentiality
5. The avoidance of misappropriation of public funds to ensure the ongoing delivery of patient care

Outputs:

Invoice Validation
1. Addressing poor data quality issues
2. Production of reports for business intelligence
3. Budget reporting
4. Validation of invoices for non-contracted events

Processing:

Data must only be used as stipulated within this Data Sharing Agreement.

Data Processors must only act upon specific instructions from the Data Controller.

Data can only be stored at the addresses listed under storage addresses.

The Data Controller and any Data Processor will only have access to records of patients of residence and registration within the CCG.

Patient level data will not be shared outside of the CCG unless it is for the purpose of Direct Care, where it may be shared only with those health professionals who have a legitimate relationship with the patient and a legitimate reason to access the data.

No patient level data will be linked other than as specifically detailed within this agreement. Data will only be shared with those parties listed and will only be used for the purposes laid out in the application/agreement. The data to be released from NHS Digital will not be national data, but only that data relating to the specific locality of interest of the applicant.
The DSCRO (part of NHS Digital) will apply Type 2 objections before any identifiable data leaves the DSCRO.
NHS Digital reminds all organisations party to this agreement of the need to comply with the Data Sharing Framework Contract requirements, including those regarding the use (and purposes of that use) by “Personnel” (as defined within the Data Sharing Framework Contract ie: employees, agents and contractors of the Data Recipient who may have access to that data)

Segregation
Where the Data Processor and/or the Data Controller hold both identifiable and pseudonymised data, the data will be held separately so data cannot be linked.
All access to data is audited

Data for the purpose of Invoice Validation is kept within the CEfF, and only used by staff properly trained and authorised for the activity. Only CEfF staff are able to access data in the CEfF and only CEfF staff operate the invoice validation process within the CEfF. Data flows directly in to the CEfF from the DSCRO and from the providers – it does not flow through any other processors.

Invoice Validation
1. SUS+ Data is obtained from the SUS+ Repository by the Data Services for Commissioners Regional Office (DSCRO).
2. The DSCRO pushes a one-way data flow of SUS+ data into the Controlled Environment for Finance (CEfF) located in the CCG.
3. The CEfF conduct the following processing activities for invoice validation purposes:
a. Checking the individual is registered to the Clinical Commissioning Group (CCG) by using the derived commissioner field in SUS+ and associated with an invoice from the SUS data flow to validate the corresponding record in the backing data flow
b. Once the backing information is received, this will be checked against national NHS and local commissioning policies as well as being checked against system access and reports provided by NHS Digital to confirm the payments are:
i. In line with Payment by Results tariffs
ii. In relation to a patient registered with the CCG GP or resident within the CCG area.
iii. The health care provided should be paid by the CCG in line with CCG guidance. 
4. The CCG are notified by the CEfF that the invoice has been validated and can be paid. Any discrepancies or non-validated invoices are investigated and resolved

Project 9 — NIC-160394-Q0X1C

Type of data: information not disclosed for TRE projects

Opt outs honoured: N ()

Legal basis: Health and Social Care Act 2012

Purposes: ()

Sensitive: Sensitive

When:2018.03 — 2018.05.

Access method: Ongoing

Data-controller type:

Sublicensing allowed:

Datasets:

  1. SUS for Commissioners
  2. Public Health and Screening Services-Local Provider Flows
  3. Primary Care Services-Local Provider Flows
  4. Population Data-Local Provider Flows
  5. Other Not Elsewhere Classified (NEC)-Local Provider Flows
  6. Mental Health-Local Provider Flows
  7. Mental Health Services Data Set
  8. Mental Health Minimum Data Set
  9. Mental Health and Learning Disabilities Data Set
  10. Maternity Services Data Set
  11. Improving Access to Psychological Therapies Data Set
  12. Experience, Quality and Outcomes-Local Provider Flows
  13. Emergency Care-Local Provider Flows
  14. Diagnostic Services-Local Provider Flows
  15. Diagnostic Imaging Dataset
  16. Demand for Service-Local Provider Flows
  17. Community-Local Provider Flows
  18. Community Services Data Set
  19. Children and Young People Health
  20. Ambulance-Local Provider Flows
  21. Acute-Local Provider Flows

Objectives:


Commissioning
To use pseudonymised data to provide intelligence to support the commissioning of health services. The data (containing both clinical and financial information) is analysed so that health care provision can be planned to support the needs of the population within the CCG area.
The CCGs commission services from a range of providers covering a wide array of services. Each of the data flow categories requested supports the commissioned activity of one or more providers.

The CCG is part of the Bristol, North Somerset and South Gloucestershire Sustainable Transformation Partnership. The STP is responsible for implementing large parts of the 5 year forward view from NHS England. The STP is implementing several initiatives:

- Putting the patient at the heart of the health system
- Working across organisational boundaries to deliver care and including social care, public Health, providers and GPs as well as CCGs
- Reviewing patient pathways to improve patient experience whilst reducing costs e.g. reduce the number of standard tests a patient may have and only have the ones they need
- Planning the demand and capacity across the healthcare system across 3 CCGs to ensure we have the right buildings, services and staff to cope with demand whilst reducing the impact on costs
- Working to prevent or capture conditions early as they are cheaper to treat
- Introduce initiatives to change behaviours e.g. move more care into the community
- Patient pathway planning for the above


To ensure the patient is at the heart of care, the STP is focussing on where services are required across the geographical region. This assists to ensure delivery of care in the right place for patients who may move and change services across CCGs.

The CCG will work proactively and collaboratively with the other CCGs in the STP to redesign services across boundaries to integrate services. Collaborative sharing is required for CCGs to understand these requirements.

The following pseudonymised datasets are required to provide intelligence to support commissioning of health services:
- Secondary Uses Service (SUS)
- Local Provider Flows
o Acute
o Ambulance
o Community
o Demand for Service
o Diagnostic Service
o Emergency Care
o Experience, Quality and Outcomes
o Mental Health
o Other Not Elsewhere Classified
o Population Data
o Primary Care Services
o Public Health Screening
- Mental Health Minimum Data Set (MHMDS)
- Mental Health Learning Disability Data Set (MHLDDS)
- Mental Health Services Data Set (MHSDS)
- Maternity Services Data Set (MSDS)
- Improving Access to Psychological Therapy (IAPT)
- Child and Young People Health Service (CYPHS)
- Community Services Data Set (CSDS)
- Diagnostic Imaging Data Set (DIDS)
The pseudonymised data is required to for the following purposes:
 Population health management:
• Understanding the interdependency of care services
• Targeting care more effectively
• Using value as the redesign principle
• Ensuring we do what we should
 Data Quality and Validation – allowing data quality checks on the submitted data
 Thoroughly investigating the needs of the population, to ensure the right services are available for individuals when and where they need them
 Understanding cohorts of residents who are at risk of becoming users of some of the more expensive services, to better understand and manage those needs
 Monitoring population health and care interactions to understand where people may slip through the net, or where the provision of care may be being duplicated
 Modelling activity across all data sets to understand how services interact with each other, and to understand how changes in one service may affect flows through another
 Service redesign
 Health Needs Assessment – identification of underlying disease prevalence within the local population
 Patient stratification and predictive modelling - to identify specific patients at risk of requiring hospital admission and other avoidable factors such as risk of falls, computed using algorithms executed against linked de-identified data, and identification of future service delivery models

The pseudonymised data is required to ensure that analysis of health care provision can be completed to support the needs of the health profile of the population within the CCG area based on the full analysis of multiple pseudonymised datasets.
Processing for commissioning will be conducted by
- South, Central and West Commissioning Support Unit
- Outcomes Based Health

Expected Benefits:


Commissioning
1. Supporting Quality Innovation Productivity and Prevention (QIPP) to review demand management, integrated care and pathways.
a. Analysis to support full business cases.
b. Develop business models.
c. Monitor In year projects.
2. Supporting Joint Strategic Needs Assessment (JSNA) for specific disease types.
3. Health economic modelling using:
a. Analysis on provider performance against 18 weeks wait targets.
b. Learning from and predicting likely patient pathways for certain conditions, in order to influence early interventions and other treatments for patients.
c. Analysis of outcome measures for differential treatments, accounting for the full patient pathway.
d. Analysis to understand emergency care and linking A&E and Emergency Urgent Care Flows (EUCC).
4. Commissioning cycle support for grouping and re-costing previous activity.
5. Enables monitoring of:
a. CCG outcome indicators.
b. Financial and Non-financial validation of activity.
c. Successful delivery of integrated care within the CCG.
d. Checking frequent or multiple attendances to improve early intervention and avoid admissions.
e. Case management.
f. Care service planning.
g. Commissioning and performance management.
h. List size verification by GP practices.
i. Understanding the care of patients in nursing homes.
6. Feedback to NHS service providers on data quality at an aggregate and individual record level – only on data initially provided by the service providers.
7. Improved planning by better understanding patient flows through the healthcare system, thus allowing commissioners to design appropriate pathways to improve patient flow and allowing commissioners to identify priorities and identify plans to address these.
8. Improved quality of services through reduced emergency readmissions, especially avoidable emergency admissions. This is achieved through mapping of frequent users of emergency services and early intervention of appropriate care.
9. Improved access to services by identifying which services may be in demand but have poor access, and from this identify areas where improvement is required.
10. Potentially reduced premature mortality by more targeted intervention in primary care, which supports the commissioner to meets its requirement to reduce premature mortality in line with the CCG Outcome Framework.
11. Better understanding of the health of and the variations in health outcomes within the population to help understand local population characteristics.
12. Better understanding of contract requirements, contract execution, and required services for management of existing contracts, and to assist with identification and planning of future contracts
13. Insights into patient outcomes, and identification of the possible efficacy of outcomes-based contracting opportunities.
14. Reviewing current service provision
a. Cost-benefit analysis and service impact assessments to underpin service transformation across health economy
b. Service planning and re-design (development of NMoC and integrated care pathways, new partnerships, working with new providers etc.)
c. Impact analysis for different models or productivity measures, efficiency and experience
d. Service and pathway review
e. Service utilisation review
15. Ensuring compliance with evidence and guidance
a. Testing approaches with evidence and compliance with guidance.
16. Monitoring outcomes
a. Analysis of variation in outcomes across population group
17. Understanding how services impact across the health economy
a. Service evaluation
b. Programme reviews
c. Analysis of productivity, outcomes, experience, plan, targets and actuals
d. Assessing value for money and efficiency gains
e. Understanding impact of services on health inequalities
18. Understanding how services impact on the health of the population and patient cohorts
a. Measuring and assessing improvement in service provision, patient experience & outcomes and the cost to achieve this
b. Propensity matching and scoring
c. Triple aim analysis
19. Understanding future drivers for change across health economy
a. Forecasting health and care needs for population and population cohorts across STPs
b. Identifying changes in disease trends and prevalence
c. Efficiencies that can be gained from procuring services across wider footprints, from new innovations
d. Predictive modelling
20. Delivering services that meet changing needs of population
a. Analysis to support policy development
b. Ethical and equality impact assessments
c. Implementation of NMOC
d. What do next years contracts need to include?
e. Workforce planning
21. Maximising services and outcomes within financial envelopes across health economy
a. What-if analysis
b. Cost-benefit analysis
c. Health economics analysis
d. Scenario planning and modelling
e. Investment and disinvestment in services analysis
f. Opportunity analysis

Outcomes Based Healthcare:

Outcomes are often described as those things that matter to people, and are typically the end results of care across complete care pathways. Access to a population-level view of all people with relevant long-term conditions, in near real-time, is the essential starting point for accurate outcome measurement. It is also an essential enabler supporting quality process improvement within care pathways.

Outcomes measured using existing clinical and administrative data typically measure the reduction in illness, disease and complications, and their severity, in addition to system activity metrics.

Measuring outcomes aims to refocus providers (including health and social care providers) to work together to reduce the burden of disease. By setting longer-term targets and improvement trajectories for each outcome, providers can focus their efforts on improving these outcomes, for specific population groups, over a period of years.

Outputs:


Commissioning
1. Commissioner reporting:
a. Summary by provider view - plan & actuals year to date (YTD).
b. Summary by Patient Outcome Data (POD) view - plan & actuals YTD.
c. Summary by provider view - activity & finance variance by POD.
d. Planned care by provider view - activity & finance plan & actuals YTD.
e. Planned care by POD view - activity plan & actuals YTD.
f. Provider reporting.
g. Statutory returns.
h. Statutory returns - monthly activity return.
i. Statutory returns - quarterly activity return.
j. Delayed discharges.
k. Quality & performance referral to treatment reporting.
2. Readmissions analysis.
3. Production of aggregate reports for CCG Business Intelligence.
4. Production of project / programme level dashboards.
5. Monitoring of acute / community / mental health quality matrix.
6. Clinical coding reviews / audits.
7. Budget reporting down to individual GP Practice level.
8. GP Practice level dashboard reports include high flyers.
9. Comparators of CCG performance with similar CCGs as set out by a specific range of care quality and performance measures detailed activity and cost reports
10. Data Quality and Validation measures allowing data quality checks on the submitted data
11. Contract Management and Modelling
12. Patient Stratification, such as:
o Patients at highest risk of admission
o Most expensive patients (top 15%)
o Frail and elderly
o Patients that are currently in hospital
o Patients with most referrals to secondary care
o Patients with most emergency activity
o Patients with most expensive prescriptions
o Patients recently moving from one care setting to another
i. Discharged from hospital
ii. Discharged from community
13. Profiling population health and wider determinants to identify and target those most in need
a. Understanding population profile and demographics
b. Identify patient cohorts with specific needs or who may benefit from interventions
c. Identifying disease prevalence. health and care needs for population cohorts
d. Contributing to Joint Strategic Needs Assessment (JSNA)
e. Geographical mapping and analysis
14. Identifying and managing preventable and existing conditions
a. Identifying types of individuals and population cohorts at risk of non-elective re-admission
b. Risk stratification to identify populations suitable for case management
c. Risk profiling and predictive modelling
d. Risk stratification for planning services for population cohorts
e. Identification of disease incidence and diagnosis stratification
15. Reducing health inequalities
a. Identifying cohorts of patients who have worse health outcomes typically deprived, ethnic groups, homeless, travellers etc. to enable services to proactively target their needs
b. Socio-demographic analysis
16. Managing demand
a. Waiting times analysis
b. Service demand and supply modelling
c. Understanding cross-border and overseas visitor
d. Winter planning
e. Emergency preparedness, business continuity, recovery and contingency planning
17. Care co-ordination and planning
a. Planning packages of care
b. Service planning
c. Planning care co-ordination
18. Monitoring individual patient health, service utilisation, pathway compliance experience & outcomes across the heath and care system
a. Patient pathway analysis across health and care
b. Outcomes & experience analysis
c. Analysis to support anti-terror initiatives
d. Analysis to identify vulnerable patients with potential safeguarding issues
e. Understanding equity of care and unwarranted variation
f. Modelling patient flow
g. Tracking patient pathways
h. Monitoring to support NMoC, ACOs, STPs
i. Identifying duplications in care
j. Identifying gaps in care, missed diagnoses and triple fail events
k. Analysing individual and aggregated timelines
19. Undertaking budget planning, management and reporting
a. Tracking financial performance against plans
b. Budget reporting
c. Tariff development
d. Developing and monitoring capitated budgets
e. Developing and monitoring individual-level budgets
f. Future budget planning and forecasting
g. Paying for care of overseas visitors and cross-border flow
20. Monitoring the value for money
a. Service-level costing & comparisons
b. Identification of cost pressures
c. Cost benefit analysis
d. Equity of spend across services and population cohorts
e. Finance impact assessment
21. Comparing population groups, peers, national and international best practice
a. Identification of variation in productivity, cost, outcomes, quality, experience, compared with peers, national and international & best practice
b. Benchmarking against other parts of the country
c. Identifying unwarranted variations
22. Comparing expected levels
a. Standardised comparisons for prevalence, activity, cost, quality, experience, outcomes for given populations
23. Comparing local targets & plan
a. Monitoring of local variation in productivity, cost, outcomes, quality and experience
b. Local performance dashboards by service provider, commissioner, geography, NMOC, STPs
24. Monitoring activity and cost compliance against contract and agreed plans
a. Contract monitoring
b. Contract reconciliation and challenge
c. Invoice validation
25. Monitoring provider quality, demand, experience and outcomes against contract and agreed plans
a. Performance dashboards
b. CQUIN reporting
c. Clinical audit
d. Patient experience surveys
e. Demand, supply, outcome & experience analysis
f. Monitoring cross-border flows and overseas visitor activity
26. Improving provider data quality
a. Coding audit
b. Data quality validation and review
c. Checking validity of patient identity and commissioner assignment

Outcomes Based Healthcare:
The main outputs are aggregated monthly values for each outcome (with small number suppression, including any values under 5). The aggregated monthly outcomes data will be made available through OBH’s online tool (available to named individuals in the CCG and CCG commissioned providers only) via a secure login.
This enables the CCGs and to:
- visualise baselines for each outcome
- set improvement trajectories
- monitor outcomes on an on-going basis.
In addition, the CCGs will receive an information schedule describing the outcomes to be monitored, the technical description, and annual baseline data for each outcome.
All outputs will be delivered within the timescales of the contract between OBH and the CCGs.

Processing:


Data must only be used as stipulated within this Data Sharing Agreement.

Data Processors must only act upon specific instructions from the Data Controller.

Data can only be stored at the addresses listed under storage addresses.

The Data Controller and any Data Processor will only have access to records of patients of residence and registration within the Bristol, North Somerset and South Gloucestershire Sustainable Transformation Partnership.

No patient level data will be linked other than as specifically detailed within this agreement. Data will only be shared with those parties listed and will only be used for the purposes laid out in the application/agreement. The data to be released from NHS Digital will not be national data, but only that data relating to the specific locality of interest of the applicant.
NHS Digital reminds all organisations party to this agreement of the need to comply with the Data Sharing Framework Contract requirements, including those regarding the use (and purposes of that use) by “Personnel” (as defined within the Data Sharing Framework Contract ie: employees, agents and contractors of the Data Recipient who may have access to that data)

Segregation
Where the Data Processor and/or the Data Controller hold both identifiable and pseudonymised data, the data will be held separately so data cannot be linked.
All access to data is audited


Commissioning
The Data Services for Commissioners Regional Office (DSCRO) obtains the following data sets:
1. SUS
2. Local Provider Flows (received directly from providers)
o Acute
o Ambulance
o Community
o Demand for Service
o Diagnostic Service
o Emergency Care
o Experience, Quality and Outcomes
o Mental Health
o Other Not Elsewhere Classified
o Population Data
o Primary Care Services
o Public Health Screening
3. Mental Health Minimum Data Set (MHMDS)
4. Mental Health Learning Disability Data Set (MHLDDS)
5. Mental Health Services Data Set (MHSDS)
6. Maternity Services Data Set (MSDS)
7. Improving Access to Psychological Therapy (IAPT)
8. Child and Young People Health Service (CYPHS)
9. Community Services Data Set (CSDS)
10. Diagnostic Imaging Data Set (DIDS)
Data quality management and pseudonymisation is completed within the DSCRO and is then disseminated as follows:
Data Processor 1 - South, Central and West Commissioning Support Unit
1. Pseudonymised SUS, Local Provider data, Mental Health data (MHSDS, MHMDS, MHLDDS), Maternity data (MSDS), Improving Access to Psychological Therapies data (IAPT), Child and Young People’s Health data (CYPHS), Community Services Data Set (CSDS) and Diagnostic Imaging data (DIDS) only is held until points 2 – 8 are completed.
2. South, Central and West Commissioning Support Unit receives GP data. GP Data is received as follows:
o Identifiable GP data is submitted to South Central and West Commissioning Support Unit.
o The identifiable data lands in a ring-fenced area for GP data only.
o The GP data is pseudonymised using a pseudonymisation tool, different to that used by the DSCRO.
o There is a Data Processing Agreement in place between the GP and South Central and West Commissioning Support Unit. A specific named individual within South Central and West Commissioning Support Unit acts on behalf of the GP.
o This individual has access to a black box. The pseudonymised data is passed through the black box process where the pseudonymisation is mapped to the pseudonymisation used by the DSCRO.
o Once mapped, the data is passed into South Central and West Commissioning Support, but before South Central and West Commissioning Support Unit will receive the data from the ring-fenced area, they require confirmation that the identifiable data has been deleted.
o South Central and West Commissioning Support Unit are then sent the pseudonymised GP data with the pseudo algorithm specific to them.
3. South, Central and West Commissioning Support Unit also receive a flow of social care data. Social Care data is received in one of the following 2 ways:
o Pseudonymised:
 Social Care data is pseudonymised within the provider using a pseudonymisation tool, different to that used by the DSCRO. The provider requests a pseudonymisation key from the DSCRO. The key can only be used once. The key is specific to the Local Authority and to that specific date.
 The pseudonymised data lands in a ring-fenced area for social care data only.
 There is a Data Processing Agreement in place between the Provider and South Central and West Commissioning Support Unit. A specific named individual within South Central and West Commissioning Support Unit acts on behalf of the Provider.
 This individual has access to a black box. The pseudonymised data is passed through the black box process where the pseudonymisation is mapped to the pseudonymisation used by the DSCRO.
 The data is then passed into the non-ringfenced area with the pseudo algorithm specific to them.
o Identifiable:
 Identifiable social care data is submitted to South Central and West Commissioning Support Unit.
 The identifiable data lands in a ring-fenced area for social care data only.
 The social care data is pseudonymised using a pseudonymisation tool, different to that used by the DSCRO.
 There is a Data Processing Agreement in place between the Local Authority and South Central and West Commissioning Support Unit. A specific named individual within South Central and West Commissioning Support Unit acts on behalf of the provider.
 This individual has access to a black box. The pseudonymised data is passed through the black box process where the pseudonymisation is mapped to the pseudonymisation used by the DSCRO.
 Once mapped, the data is passed into South Central and West Commissioning Support, but before South Central and West Commissioning Support Unit will receive the data from the ring-fenced area, they require confirmation that the identifiable data has been deleted.
 South Central and West Commissioning Support Unit are then sent the pseudonymised social care data with the pseudo algorithm specific to them.
4. Once the pseudonymised GP data and social care data is received, South, Central and West Commissioning Support Unit make a request to the DSCRO.
5. The DSCRO check the dates of the key generation (Point 2d and 3aii/3biv).
6. The DSCRO then send a mapping table to South, Central and West Commissioning Support Unit
7. South, Central and West Commissioning Support Unit then overwrite the organisation specific keys with the DSCRO key.
8. The mapping table is then deleted.
9. The DSCRO pass the pseudonymised SUS, local provider data, Mental Health (MHSDS, MHMDS, MHLDDS), Maternity (MSDS), Improving Access to Psychological Therapies (IAPT), Child and Young People’s Health (CYPHS) and Diagnostic Imaging (DIDS) securely to South, Central and West Commissioning Support Unit for the addition of derived fields, linkage of data sets and analysis
10. Social care data is then linked to the data sets listed within point 9.
11. South, Central and West Commissioning Support Unit then pass the processed, pseudonymised and linked data to the CCG.
12. Aggregation of required data for CCG management use will be completed by South, Central and West Commissioning Support Unit as instructed by the CCG.
13. Patient level data will not be shared outside of the CCG and will only be shared within the CCG on a need to know basis, as per the purposes stipulated within the Data Sharing Agreement. External aggregated reports only with small number suppression can be shared as set out within NHS Digital guidance applicable to each data set.


Data Processor 2 - Outcomes Based Health
1. Pseudonymised SUS+ (pseudonymised using an encryption key’ specific to this project), only is securely transferred from the DSCRO to South, Central and West Commissioning Support Unit..
2. South, Central and West Commissioning Support Unit add derived fields and securely transfer the data to the CCG.
3. The CCG passes the pseudonymised data to Outcomes Based Health
4. Outcomes Based Health then analyse the data to:
o Develop outcomes, baselining and monitoring of individual outcomes
o Provide detailed analysis relating to outcomes
5. Aggregation of required data for CCG management use will be completed by Outcomes Based Health or the CCG as instructed by the CCG.
6. Patient level data will not be shared outside of the CCG and will only be shared within the CCG on a need to know basis, as per the purposes stipulated within the Data Sharing Agreement. External aggregated reports only with small number suppression can be shared as set out within NHS Digital guidance applicable to each data set.

Project 10 — NIC-163830-B6X7B

Type of data: information not disclosed for TRE projects

Opt outs honoured: Y ()

Legal basis: Section 251 approval is in place for the flow of identifiable data

Purposes: ()

Sensitive: Sensitive

When:2018.03 — 2018.05.

Access method: Ongoing

Data-controller type:

Sublicensing allowed:

Datasets:

  1. SUS for Commissioners

Objectives:

Invoice Validation
Invoice validation is part of a process by which providers of care or services get paid for the work they do.

Invoices are submitted to the Clinical Commissioning Group (CCG) so they are able to ensure that the activity claimed for each patient is their responsibility. This is done by processing and analysing Secondary User Services (SUS+) data, which is received into a secure Controlled Environment for Finance (CEfF). The SUS+ data is identifiable at the level of NHS number. The NHS number is only used to confirm the accuracy of backing-data sets and will not be used further.

The legal basis for this to occur is under Section 251 of NHS Act 2006.

Invoice Validation with be conducted by the CCG.

Expected Benefits:

Invoice Validation
1. Financial validation of activity
2. CCG Budget control
3. Commissioning and performance management
4. Meeting commissioning objectives without compromising patient confidentiality
5. The avoidance of misappropriation of public funds to ensure the ongoing delivery of patient care

Outputs:

Invoice Validation
1. Addressing poor data quality issues
2. Production of reports for business intelligence
3. Budget reporting
4. Validation of invoices for non-contracted events

Processing:

Data must only be used as stipulated within this Data Sharing Agreement.

Data Processors must only act upon specific instructions from the Data Controller.

Data can only be stored at the addresses listed under storage addresses.

The Data Controller and any Data Processor will only have access to records of patients of residence and registration within the CCG.

Patient level data will not be shared outside of the CCG unless it is for the purpose of Direct Care, where it may be shared only with those health professionals who have a legitimate relationship with the patient and a legitimate reason to access the data.

No patient level data will be linked other than as specifically detailed within this agreement. Data will only be shared with those parties listed and will only be used for the purposes laid out in the application/agreement. The data to be released from NHS Digital will not be national data, but only that data relating to the specific locality of interest of the applicant.
The DSCRO (part of NHS Digital) will apply Type 2 objections before any identifiable data leaves the DSCRO.
NHS Digital reminds all organisations party to this agreement of the need to comply with the Data Sharing Framework Contract requirements, including those regarding the use (and purposes of that use) by “Personnel” (as defined within the Data Sharing Framework Contract ie: employees, agents and contractors of the Data Recipient who may have access to that data)

Segregation
Where the Data Processor and/or the Data Controller hold both identifiable and pseudonymised data, the data will be held separately so data cannot be linked.
All access to data is audited

Data for the purpose of Invoice Validation is kept within the CEfF, and only used by staff properly trained and authorised for the activity. Only CEfF staff are able to access data in the CEfF and only CEfF staff operate the invoice validation process within the CEfF. Data flows directly in to the CEfF from the DSCRO and from the providers – it does not flow through any other processors.

Invoice Validation
1. SUS+ Data is obtained from the SUS+ Repository by the Data Services for Commissioners Regional Office (DSCRO).
2. The DSCRO pushes a one-way data flow of SUS+ data into the Controlled Environment for Finance (CEfF) located in the CCG.
3. The CEfF conduct the following processing activities for invoice validation purposes:
a. Checking the individual is registered to the Clinical Commissioning Group (CCG) by using the derived commissioner field in SUS+ and associated with an invoice from the SUS data flow to validate the corresponding record in the backing data flow
b. Once the backing information is received, this will be checked against national NHS and local commissioning policies as well as being checked against system access and reports provided by NHS Digital to confirm the payments are:
i. In line with Payment by Results tariffs
ii. In relation to a patient registered with the CCG GP or resident within the CCG area.
iii. The health care provided should be paid by the CCG in line with CCG guidance. 
4. The CCG are notified by the CEfF that the invoice has been validated and can be paid. Any discrepancies or non-validated invoices are investigated and resolved

Project 11 — NIC-163816-G8W9C

Type of data: information not disclosed for TRE projects

Opt outs honoured: Y ()

Legal basis: Section 251 approval is in place for the flow of identifiable data

Purposes: ()

Sensitive: Sensitive

When:2018.03 — 2018.05.

Access method: Ongoing

Data-controller type:

Sublicensing allowed:

Datasets:

  1. SUS for Commissioners

Objectives:

Invoice Validation
Invoice validation is part of a process by which providers of care or services get paid for the work they do.
Invoices are submitted to the Clinical Commissioning Group (CCG) so they are able to ensure that the activity claimed for each patient is their responsibility. This is done by processing and analysing Secondary User Services (SUS+) data, which is received into a secure Controlled Environment for Finance (CEfF). The SUS+ data is identifiable at the level of NHS number. The NHS number is only used to confirm the accuracy of backing-data sets and will not be used further.
The legal basis for this to occur is under Section 251 of NHS Act 2006.
Invoice Validation with be conducted by the CCG.

Expected Benefits:

Invoice Validation
1. Financial validation of activity
2. CCG Budget control
3. Commissioning and performance management
4. Meeting commissioning objectives without compromising patient confidentiality
5. The avoidance of misappropriation of public funds to ensure the ongoing delivery of patient care

Outputs:

Invoice Validation
1. Addressing poor data quality issues
2. Production of reports for business intelligence
3. Budget reporting
4. Validation of invoices for non-contracted events

Processing:

Data must only be used as stipulated within this Data Sharing Agreement.

Data Processors must only act upon specific instructions from the Data Controller.

Data can only be stored at the addresses listed under storage addresses.

The Data Controller and any Data Processor will only have access to records of patients of residence and registration within the CCG.

Patient level data will not be shared outside of the CCG unless it is for the purpose of Direct Care, where it may be shared only with those health professionals who have a legitimate relationship with the patient and a legitimate reason to access the data.

No patient level data will be linked other than as specifically detailed within this agreement. Data will only be shared with those parties listed and will only be used for the purposes laid out in the application/agreement. The data to be released from NHS Digital will not be national data, but only that data relating to the specific locality of interest of the applicant.
The DSCRO (part of NHS Digital) will apply Type 2 objections before any identifiable data leaves the DSCRO.
NHS Digital reminds all organisations party to this agreement of the need to comply with the Data Sharing Framework Contract requirements, including those regarding the use (and purposes of that use) by “Personnel” (as defined within the Data Sharing Framework Contract ie: employees, agents and contractors of the Data Recipient who may have access to that data)

Segregation
Where the Data Processor and/or the Data Controller hold both identifiable and pseudonymised data, the data will be held separately so data cannot be linked.
All access to data is audited

Data for the purpose of Invoice Validation is kept within the CEfF, and only used by staff properly trained and authorised for the activity. Only CEfF staff are able to access data in the CEfF and only CEfF staff operate the invoice validation process within the CEfF. Data flows directly in to the CEfF from the DSCRO and from the providers – it does not flow through any other processors.

Invoice Validation
1. SUS+ Data is obtained from the SUS+ Repository by the Data Services for Commissioners Regional Office (DSCRO).
2. The DSCRO pushes a one-way data flow of SUS+ data into the Controlled Environment for Finance (CEfF) located in the CCG.
3. The CEfF conduct the following processing activities for invoice validation purposes:
a. Checking the individual is registered to the Clinical Commissioning Group (CCG) by using the derived commissioner field in SUS+ and associated with an invoice from the SUS data flow to validate the corresponding record in the backing data flow
b. Once the backing information is received, this will be checked against national NHS and local commissioning policies as well as being checked against system access and reports provided by NHS Digital to confirm the payments are:
i. In line with Payment by Results tariffs
ii. In relation to a patient registered with the CCG GP or resident within the CCG area.
iii. The health care provided should be paid by the CCG in line with CCG guidance. 
4. The CCG are notified by the CEfF that the invoice has been validated and can be paid. Any discrepancies or non-validated invoices are investigated and resolved .

Project 12 — NIC-186885-Q1T3D

Type of data: information not disclosed for TRE projects

Opt outs honoured: N, Y ()

Legal basis: Health and Social Care Act 2012, Section 251 approval is in place for the flow of identifiable data

Purposes: ()

Sensitive: Sensitive

When:2018.03 — 2018.05.

Access method: Ongoing

Data-controller type:

Sublicensing allowed:

Datasets:

  1. SUS for Commissioners
  2. Public Health and Screening Services-Local Provider Flows
  3. Primary Care Services-Local Provider Flows
  4. Population Data-Local Provider Flows
  5. Other Not Elsewhere Classified (NEC)-Local Provider Flows
  6. National Cancer Waiting Times Monitoring DataSet
  7. Mental Health-Local Provider Flows
  8. Mental Health Services Data Set
  9. Mental Health Minimum Data Set
  10. Mental Health and Learning Disabilities Data Set
  11. Maternity Services Data Set
  12. Improving Access to Psychological Therapies Data Set
  13. Experience, Quality and Outcomes-Local Provider Flows
  14. Emergency Care-Local Provider Flows
  15. Diagnostic Services-Local Provider Flows
  16. Diagnostic Imaging Dataset
  17. Demand for Service-Local Provider Flows
  18. Community-Local Provider Flows
  19. Community Services Data Set
  20. Children and Young People Health
  21. Ambulance-Local Provider Flows
  22. Acute-Local Provider Flows

Objectives:


Invoice Validation
Invoice validation is part of a process by which providers of care or services get paid for the work they do.
Invoices are submitted to the Clinical Commissioning Group (CCG) so they are able to ensure that the activity claimed for each patient is their responsibility. This is done by processing and analysing Secondary User Services (SUS+) data, which is received into a secure Controlled Environment for Finance (CEfF). The SUS+ data is identifiable at the level of NHS number. The NHS number is only used to confirm the accuracy of backing-data sets and will not be used further.
The legal basis for this to occur is under Section 251 of NHS Act 2006.
Invoice Validation with be conducted by NHS Bristol, North Somerset and South Gloucestershire CCG
The CCG are advised by NHS Bristol, North Somerset and South Gloucestershire CCG whether payment for invoices can be made or not.

Risk Stratification
Risk stratification is a tool for identifying and predicting which patients are at high risk or are likely to be at high risk and prioritising the management of their care in order to prevent worse outcomes.
To conduct risk stratification Secondary User Services (SUS+) data, identifiable at the level of NHS number is linked with Primary Care data (from GPs) and an algorithm is applied to produce risk scores. Risk Stratification provides a forecast of future demand by identifying high risk patients. Commissioners can then prepare plans for patients who may require high levels of care. Risk Stratification also enables General Practitioners (GPs) to better target intervention in Primary Care.
The legal basis for this to occur is under Section 251 of NHS Act 2006 (CAG 7-04(a)).
Risk Stratification will be conducted by South Central and West Commissioning Support Unit

Commissioning
The NHS and local councils have come together in 44 areas covering all of England to develop proposals to improve health and care. They have formed new partnerships – known as sustainability and transformation partnerships – to plan jointly for the next few years.

Sustainability and transformation partnerships build on collaborative work that began under the NHS Shared Planning Guidance for 2016/17 – 2020/21, to support implementation of the Five Year Forward View. They are supported by six national health and care bodies: NHS England; NHS Improvement; the Care Quality Commission (CQC); Health Education England (HEE); Public Health England (PHE) and the National Institute for Health and Care Excellence (NICE).

The CCG is part of the Bristol, North Somerset and South Gloucestershire Sustainable Transformation Partnership. The STP is responsible for implementing large parts of the 5 year forward view from NHS England. The STP is implementing several initiatives:

- Putting the patient at the heart of the health system
- Working across organisational boundaries to deliver care and including social care, public Health, providers and GPs as well as CCGs
- Reviewing patient pathways to improve patient experience whilst reducing costs e.g. reduce the number of standard tests a patient may have and only have the ones they need
- Planning the demand and capacity across the healthcare system across 2 CCGs to ensure we have the right buildings, services and staff to cope with demand whilst reducing the impact on costs
- Working to prevent or capture conditions early as they are cheaper to treat
- Introduce initiatives to change behaviours e.g. move more care into the community
- Patient pathway planning for the above


To ensure the patient is at the heart of care, the STP is focussing on where services are required across the geographical region. This assists to ensure delivery of care in the right place for patients who may move and change services across the CCG.

The CCG will work proactively and collaboratively in the STP to redesign services across boundaries to integrate services.

The CCG will use pseudonymised data to provide intelligence to support the commissioning of health services. The data (containing both clinical and financial information) is analysed so that health care provision can be planned to support the needs of the population within the STP area.
The CCG commissions services from a range of providers covering a wide array of services. Each of the data flow categories requested supports the commissioned activity of one or more providers.
The following pseudonymised datasets are required to provide intelligence to support commissioning of health services:
- Secondary Uses Service (SUS+)
- Local Provider Flows
o Acute
o Ambulance
o Community
o Demand for Service
o Diagnostic Service
o Emergency Care
o Experience, Quality and Outcomes
o Mental Health
o Other Not Elsewhere Classified
o Population Data
o Primary Care Services
o Public Health Screening
- Mental Health Minimum Data Set (MHMDS)
- Mental Health Learning Disability Data Set (MHLDDS)
- Mental Health Services Data Set (MHSDS)
- Maternity Services Data Set (MSDS)
- Improving Access to Psychological Therapy (IAPT)
- Child and Young People Health Service (CYPHS)
- Community Services Data Set (CSDS)
- Diagnostic Imaging Data Set (DIDS)
- National Cancer Waiting Times Monitoring Data Set (CWT)
The pseudonymised data is required to for the following purposes:
 Population health management:
• Understanding the interdependency of care services
• Targeting care more effectively
• Using value as the redesign principle
• Ensuring we do what we should
 Data Quality and Validation – allowing data quality checks on the submitted data
 Thoroughly investigating the needs of the population, to ensure the right services are available for individuals when and where they need them
 Understanding cohorts of residents who are at risk of becoming users of some of the more expensive services, to better understand and manage those needs
 Monitoring population health and care interactions to understand where people may slip through the net, or where the provision of care may be being duplicated
 Modelling activity across all data sets to understand how services interact with each other, and to understand how changes in one service may affect flows through another
 Service redesign
 Health Needs Assessment – identification of underlying disease prevalence within the local population
 Patient stratification and predictive modelling - to identify specific patients at risk of requiring hospital admission and other avoidable factors such as risk of falls, computed using algorithms executed against linked de-identified data, and identification of future service delivery models

The pseudonymised data is required to ensure that analysis of health care provision can be completed to support the needs of the health profile of the population within the CCG area based on the full analysis of multiple pseudonymised datasets.
Processing for commissioning will be conducted by South Central and West Commissioning Support Unit and Outcomes Based Health (OBH)

Expected Benefits:


Invoice Validation
1. Financial validation of activity
2. CCG Budget control
3. Commissioning and performance management
4. Meeting commissioning objectives without compromising patient confidentiality
5. The avoidance of misappropriation of public funds to ensure the ongoing delivery of patient care

Risk Stratification
Risk stratification promotes improved case management in primary care and will lead to the following benefits being realised:
1. Improved planning by better understanding patient flows through the healthcare system, thus allowing commissioners to design appropriate pathways to improve patient flow and allowing commissioners to identify priorities and identify plans to address these.
2. Improved quality of services through reduced emergency readmissions, especially avoidable emergency admissions. This is achieved through mapping of frequent users of emergency services thus allowing early intervention.
3. Improved access to services by identifying which services may be in demand but have poor access, and from this identify areas where improvement is required.
4. Supports the commissioner to meets its requirement to reduce premature mortality in line with the CCG Outcome Framework by allowing for more targeted intervention in primary care.
5. Better understanding of local population characteristics through analysis of their health and healthcare outcomes
All of the above lead to improved patient experience through more effective commissioning of services.

Commissioning
1. Supporting Quality Innovation Productivity and Prevention (QIPP) to review demand management, integrated care and pathways.
a. Analysis to support full business cases.
b. Develop business models.
c. Monitor In year projects.
2. Supporting Joint Strategic Needs Assessment (JSNA) for specific disease types.
3. Health economic modelling using:
a. Analysis on provider performance against 18 weeks wait targets.
b. Learning from and predicting likely patient pathways for certain conditions, in order to influence early interventions and other treatments for patients.
c. Analysis of outcome measures for differential treatments, accounting for the full patient pathway.
d. Analysis to understand emergency care and linking A&E and Emergency Urgent Care Flows (EUCC).
4. Commissioning cycle support for grouping and re-costing previous activity.
5. Enables monitoring of:
a. CCG outcome indicators.
b. Financial and Non-financial validation of activity.
c. Successful delivery of integrated care within the CCG.
d. Checking frequent or multiple attendances to improve early intervention and avoid admissions.
e. Case management.
f. Care service planning.
g. Commissioning and performance management.
h. List size verification by GP practices.
i. Understanding the care of patients in nursing homes.
6. Feedback to NHS service providers on data quality at an aggregate and individual record level – only on data initially provided by the service providers.
7. Improved planning by better understanding patient flows through the healthcare system, thus allowing commissioners to design appropriate pathways to improve patient flow and allowing commissioners to identify priorities and identify plans to address these.
8. Improved quality of services through reduced emergency readmissions, especially avoidable emergency admissions. This is achieved through mapping of frequent users of emergency services and early intervention of appropriate care.
9. Improved access to services by identifying which services may be in demand but have poor access, and from this identify areas where improvement is required.
10. Potentially reduced premature mortality by more targeted intervention in primary care, which supports the commissioner to meets its requirement to reduce premature mortality in line with the CCG Outcome Framework.
11. Better understanding of the health of and the variations in health outcomes within the population to help understand local population characteristics.
12. Better understanding of contract requirements, contract execution, and required services for management of existing contracts, and to assist with identification and planning of future contracts
13. Insights into patient outcomes, and identification of the possible efficacy of outcomes-based contracting opportunities.
14. Reviewing current service provision
a. Cost-benefit analysis and service impact assessments to underpin service transformation across health economy
b. Service planning and re-design (development of NMoC and integrated care pathways, new partnerships, working with new providers etc.)
c. Impact analysis for different models or productivity measures, efficiency and experience
d. Service and pathway review
e. Service utilisation review
15. Ensuring compliance with evidence and guidance
a. Testing approaches with evidence and compliance with guidance.
16. Monitoring outcomes
a. Analysis of variation in outcomes across population group
17. Understanding how services impact across the health economy
a. Service evaluation
b. Programme reviews
c. Analysis of productivity, outcomes, experience, plan, targets and actuals
d. Assessing value for money and efficiency gains
e. Understanding impact of services on health inequalities
18. Understanding how services impact on the health of the population and patient cohorts
a. Measuring and assessing improvement in service provision, patient experience & outcomes and the cost to achieve this
b. Propensity matching and scoring
c. Triple aim analysis
19. Understanding future drivers for change across health economy
a. Forecasting health and care needs for population and population cohorts across STPs
b. Identifying changes in disease trends and prevalence
c. Efficiencies that can be gained from procuring services across wider footprints, from new innovations
d. Predictive modelling
20. Delivering services that meet changing needs of population
a. Analysis to support policy development
b. Ethical and equality impact assessments
c. Implementation of NMOC
d. What do next years contracts need to include?
e. Workforce planning
21. Maximising services and outcomes within financial envelopes across health economy
a. What-if analysis
b. Cost-benefit analysis
c. Health economics analysis
d. Scenario planning and modelling
e. Investment and disinvestment in services analysis
f. Opportunity analysis

Outcomes Based Healthcare:

Outcomes are often described as those things that matter to people, and are typically the end results of care across complete care pathways. Access to a population-level view of all people with relevant long-term conditions, in near real-time, is the essential starting point for accurate outcome measurement. It is also an essential enabler supporting quality process improvement within care pathways.

Outcomes measured using existing clinical and administrative data typically measure the reduction in illness, disease and complications, and their severity, in addition to system activity metrics.

Measuring outcomes aims to refocus providers (including health and social care providers) to work together to reduce the burden of disease. By setting longer-term targets and improvement trajectories for each outcome, providers can focus their efforts on improving these outcomes, for specific population groups, over a period of years.

Outputs:


Invoice Validation
1. Addressing poor data quality issues
2. Production of reports for business intelligence
3. Budget reporting
4. Validation of invoices for non-contracted events

Risk Stratification
1. As part of the risk stratification processing activity detailed above, GPs have access to the risk stratification tool which highlights patients for whom the GP is responsible and have been classed as at risk. The only identifier available to GPs is the NHS numbers of their own patients. Any further identification of the patients will be completed by the GP on their own systems.
2. Output from the risk stratification tool will provide aggregate reporting of number and percentage of population found to be at risk.
3. Record level output will be available for commissioners (of the CCG), pseudonymised at patient level.
4. GP Practices will be able to view the risk scores for individual patients with the ability to display the underlying SUS+ data for the individual patients when it is required for direct care purposes by someone who has a legitimate relationship with the patient.
5. The CCG will be able to target specific patient groups and enable clinicians with the duty of care for the patient to offer appropriate interventions. The CCG will also be able to:
a. Stratify populations based on: disease profiles; conditions currently being treated; current service use; pharmacy use and risk of future overall cost
b. Plan work for commissioning services and contracts
c. Set up capitated budgets
d. Identify health determinants of risk of admission to hospital, or other adverse care outcomes.

Commissioning
1. Commissioner reporting:
a. Summary by provider view - plan & actuals year to date (YTD).
b. Summary by Patient Outcome Data (POD) view - plan & actuals YTD.
c. Summary by provider view - activity & finance variance by POD.
d. Planned care by provider view - activity & finance plan & actuals YTD.
e. Planned care by POD view - activity plan & actuals YTD.
f. Provider reporting.
g. Statutory returns.
h. Statutory returns - monthly activity return.
i. Statutory returns - quarterly activity return.
j. Delayed discharges.
k. Quality & performance referral to treatment reporting.
2. Readmissions analysis.
3. Production of aggregate reports for CCG Business Intelligence.
4. Production of project / programme level dashboards.
5. Monitoring of acute / community / mental health quality matrix.
6. Clinical coding reviews / audits.
7. Budget reporting down to individual GP Practice level.
8. GP Practice level dashboard reports include high flyers.
9. Comparators of CCG performance with similar CCGs as set out by a specific range of care quality and performance measures detailed activity and cost reports
10. Data Quality and Validation measures allowing data quality checks on the submitted data
11. Contract Management and Modelling
12. Patient Stratification, such as:
a. Patients at highest risk of admission
b. Most expensive patients (top 15%)
c. Frail and elderly
d. Patients that are currently in hospital
e. Patients with most referrals to secondary care
f. Patients with most emergency activity
g. Patients with most expensive prescriptions
h. Patients recently moving from one care setting to another
i. Discharged from hospital
ii. Discharged from community
13. Profiling population health and wider determinants to identify and target those most in need
a. Understanding population profile and demographics
b. Identify patient cohorts with specific needs or who may benefit from interventions
c. Identifying disease prevalence. health and care needs for population cohorts
d. Contributing to Joint Strategic Needs Assessment (JSNA)
e. Geographical mapping and analysis
14. Identifying and managing preventable and existing conditions
a. Identifying types of individuals and population cohorts at risk of non-elective re-admission
b. Risk stratification to identify populations suitable for case management
c. Risk profiling and predictive modelling
d. Risk stratification for planning services for population cohorts
e. Identification of disease incidence and diagnosis stratification
15. Reducing health inequalities
a. Identifying cohorts of patients who have worse health outcomes typically deprived, ethnic groups, homeless, travellers etc. to enable services to proactively target their needs
b. Socio-demographic analysis
16. Managing demand
a. Waiting times analysis
b. Service demand and supply modelling
c. Understanding cross-border and overseas visitor
d. Winter planning
e. Emergency preparedness, business continuity, recovery and contingency planning
17. Care co-ordination and planning
a. Planning packages of care
b. Service planning
c. Planning care co-ordination
18. Monitoring individual patient health, service utilisation, pathway compliance experience & outcomes across the heath and care system
a. Patient pathway analysis across health and care
b. Outcomes & experience analysis
c. Analysis to support anti-terror initiatives
d. Analysis to identify vulnerable patients with potential safeguarding issues
e. Understanding equity of care and unwarranted variation
f. Modelling patient flow
g. Tracking patient pathways
h. Monitoring to support NMoC, ACOs, STPs
i. Identifying duplications in care
j. Identifying gaps in care, missed diagnoses and triple fail events
k. Analysing individual and aggregated timelines
19. Undertaking budget planning, management and reporting
a. Tracking financial performance against plans
b. Budget reporting
c. Tariff development
d. Developing and monitoring capitated budgets
e. Developing and monitoring individual-level budgets
f. Future budget planning and forecasting
g. Paying for care of overseas visitors and cross-border flow
20. Monitoring the value for money
a. Service-level costing & comparisons
b. Identification of cost pressures
a. Cost benefit analysis
b. Equity of spend across services and population cohorts
c. Finance impact assessment
21. Comparing population groups, peers, national and international best practice
d. Identification of variation in productivity, cost, outcomes, quality, experience, compared with peers, national and international & best practice
e. Benchmarking against other parts of the country
f. Identifying unwarranted variations
22. Comparing expected levels
g. Standardised comparisons for prevalence, activity, cost, quality, experience, outcomes for given populations
23. Comparing local targets & plan
h. Monitoring of local variation in productivity, cost, outcomes, quality and experience
i. Local performance dashboards by service provider, commissioner, geography, NMOC, STPs
24. Monitoring activity and cost compliance against contract and agreed plans
j. Contract monitoring
k. Contract reconciliation and challenge
l. Invoice validation
25. Monitoring provider quality, demand, experience and outcomes against contract and agreed plans
m. Performance dashboards
n. CQUIN reporting
o. Clinical audit
p. Patient experience surveys
q. Demand, supply, outcome & experience analysis
r. Monitoring cross-border flows and overseas visitor activity
26. Improving provider data quality
s. Coding audit
t. Data quality validation and review
u. Checking validity of patient identity and commissioner assignment

Outcomes Based Healthcare:
The main outputs are aggregated monthly values for each outcome (with small number suppression, including any values under 5). The aggregated monthly outcomes data will be made available through OBH’s online tool (available to named individuals in the CCG and CCG commissioned providers only) via a secure login.
This enables the CCGs and to:
- visualise baselines for each outcome
- set improvement trajectories
- monitor outcomes on an on-going basis.
In addition, the CCGs will receive an information schedule describing the outcomes to be monitored, the technical description, and annual baseline data for each outcome.
All outputs will be delivered within the timescales of the contract between OBH and the CCGs.

Processing:

Data must only be used as stipulated within this Data Sharing Agreement.

Data Processors must only act upon specific instructions from the Data Controller.

Data can only be stored at the addresses listed under storage addresses.

The Data Controller and any Data Processor will only have access to records of patients of residence and registration within the CCG.

Patient level data will not be shared outside of the CCG unless it is for the purpose of Direct Care, where it may be shared only with those health professionals who have a legitimate relationship with the patient and a legitimate reason to access the data.
All access to data is managed under Roles-Based Access Controls

CCGs should work with general practices within their CCG to help them fulfil data controller responsibilities regarding flow of identifiable data into risk stratification tools.

No patient level data will be linked other than as specifically detailed within this agreement. Data will only be shared with those parties listed and will only be used for the purposes laid out in the application/agreement. The data to be released from NHS Digital will not be national data, but only that data relating to the specific locality and that data required by the applicant.
The DSCRO (part of NHS Digital) will apply Type 2 objections before any identifiable data leaves the DSCRO.
NHS Digital reminds all organisations party to this agreement of the need to comply with the Data Sharing Framework Contract requirements, including those regarding the use (and purposes of that use) by “Personnel” (as defined within the Data Sharing Framework Contract ie: employees, agents and contractors of the Data Recipient who may have access to that data)

Segregation
Where the Data Processor and/or the Data Controller hold both identifiable and pseudonymised data, the data will be held separately so data cannot be linked.

All access to data is auditable by NHS Digital.

Data for the purpose of Invoice Validation is kept within the CEfF, and only used by staff properly trained and authorised for the activity. Only CEfF staff are able to access data in the CEfF and only CEfF staff operate the invoice validation process within the CEfF. Data flows directly in to the CEfF from the DSCRO and from the providers – it does not flow through any other processors.


Invoice Validation
NHS Bristol, North Somerset and South Gloucestershire CCG
1. SUS+ Data is obtained from the SUS+ Repository by the Data Services for Commissioners Regional Office (DSCRO).
2. The DSCRO pushes a one-way data flow of SUS+ data into the Controlled Environment for Finance (CEfF) located in the CCG.
3. The CEfF conduct the following processing activities for invoice validation purposes:
a. Validating that the Clinical Commissioning Group is responsible for payment for the care of the individual by using SUS+ and/or backing flow data.
b. Once the backing information is received, this will be checked against national NHS and local commissioning policies as well as being checked against system access and reports provided by NHS Digital to confirm the payments are:
i. In line with Payment by Results tariffs
ii. In relation to a patient registered with the CCG GP or resident within the CCG area.
iii. The health care provided should be paid by the CCG in line with CCG guidance. 
4. The CCG are notified by the CEfF that the invoice has been validated and can be paid. Any discrepancies or non-validated invoices are investigated and resolved


Risk Stratification
South Central and West Commissioning Support Unit
1. Identifiable SUS+ data is obtained from the SUS Repository to the Data Services for Commissioners Regional Office (DSCRO).
2. Data quality management and standardisation of data is completed by the DSCRO and the data identifiable at the level of NHS number is transferred securely to South Central and West Commissioning Support Unit, who hold the SUS+ data within the secure Data Centre on N3.
3. Identifiable GP Data is securely sent from the GP system to South Central and West Commissioning Support Unit.
4. SUS+ data is linked to GP data in the risk stratification tool by the data processor.
5. As part of the risk stratification processing activity, GPs have access to the risk stratification tool within the data processor, which highlights patients with whom the GP has a legitimate relationship and have been classed as at risk. The only identifier available to GPs is the NHS numbers of their own patients. Any further identification of the patients will be completed by the GP on their own systems.
6. Once South Central and West Commissioning Support Unit has completed the processing, the CCG can access the online system via a secure connection to access the data pseudonymised at patient level.

Commissioning
The Data Services for Commissioners Regional Office (DSCRO) obtains the following data sets:
1. SUS+
2. Local Provider Flows (received directly from providers)
a. Acute
b. Ambulance
c. Community
d. Demand for Service
e. Diagnostic Service
f. Emergency Care
g. Experience, Quality and Outcomes
h. Mental Health
i. Other Not Elsewhere Classified
j. Population Data
k. Primary Care Services
l. Public Health Screening
3. Mental Health Minimum Data Set (MHMDS)
4. Mental Health Learning Disability Data Set (MHLDDS)
5. Mental Health Services Data Set (MHSDS)
6. Maternity Services Data Set (MSDS)
7. Improving Access to Psychological Therapy (IAPT)
8. Child and Young People Health Service (CYPHS)
9. Community Services Data Set (CSDS)
10. Diagnostic Imaging Data Set (DIDS)
11. National Cancer Waiting Times Monitoring Data Set (CWT)
Data quality management and pseudonymisation is completed within the DSCRO and is then disseminated as follows:
Data Processor 1 - South, Central and West Commissioning Support Unit

1. Pseudonymised SUS, Local Provider data, Mental Health data (MHSDS, MHMDS, MHLDDS), Maternity data (MSDS), Improving Access to Psychological Therapies data (IAPT), Child and Young People’s Health data (CYPHS), Community Services Data Set (CSDS), Diagnostic Imaging data (DIDS) and National Cancer Waiting Times Monitoring Data Set (CWT)
2. only is held until points 2 – 8 are completed.
3. South, Central and West Commissioning Support Unit receives GP data. GP Data is received as follows:
o Identifiable GP data is submitted to South Central and West Commissioning Support Unit.
o The identifiable data lands in a ring-fenced area for GP data only.
o The GP data is pseudonymised using a pseudonymisation tool, different to that used by the DSCRO.
o There is a Data Processing Agreement in place between the GP and South Central and West Commissioning Support Unit. A specific named individual within South Central and West Commissioning Support Unit acts on behalf of the GP.
o This individual has access to a black box. The pseudonymised data is passed through the black box process where the pseudonymisation is mapped to the pseudonymisation used by the DSCRO.
o Once mapped, the data is passed into South Central and West Commissioning Support, but before South Central and West Commissioning Support Unit will receive the data from the ring-fenced area, they require confirmation that the identifiable data has been deleted.
o South Central and West Commissioning Support Unit are then sent the pseudonymised GP data with the pseudo algorithm specific to them.
4. South, Central and West Commissioning Support Unit also receive a flow of social care data. Social Care data is received in one of the following 2 ways:
o Pseudonymised:
 Social Care data is pseudonymised within the provider using a pseudonymisation tool, different to that used by the DSCRO. The provider requests a pseudonymisation key from the DSCRO. The key can only be used once. The key is specific to the Local Authority and to that specific date.
 The pseudonymised data lands in a ring-fenced area for social care data only.
 There is a Data Processing Agreement in place between the Provider and South Central and West Commissioning Support Unit. A specific named individual within South Central and West Commissioning Support Unit acts on behalf of the Provider.
 This individual has access to a black box. The pseudonymised data is passed through the black box process where the pseudonymisation is mapped to the pseudonymisation used by the DSCRO.
 The data is then passed into the non-ringfenced area with the pseudo algorithm specific to them.
o Identifiable:
 Identifiable social care data is submitted to South Central and West Commissioning Support Unit.
 The identifiable data lands in a ring-fenced area for social care data only.
 The social care data is pseudonymised using a pseudonymisation tool, different to that used by the DSCRO.
 There is a Data Processing Agreement in place between the Local Authority and South Central and West Commissioning Support Unit. A specific named individual within South Central and West Commissioning Support Unit acts on behalf of the provider.
 This individual has access to a black box. The pseudonymised data is passed through the black box process where the pseudonymisation is mapped to the pseudonymisation used by the DSCRO.
 Once mapped, the data is passed into South Central and West Commissioning Support, but before South Central and West Commissioning Support Unit will receive the data from the ring-fenced area, they require confirmation that the identifiable data has been deleted.
 South Central and West Commissioning Support Unit are then sent the pseudonymised social care data with the pseudo algorithm specific to them.
5. Once the pseudonymised GP data and social care data is received, South, Central and West Commissioning Support Unit make a request to the DSCRO.
6. The DSCRO check the dates of the key generation (Point 2d and 3aii/3biv).
7. The DSCRO then send a mapping table to South, Central and West Commissioning Support Unit
8. South, Central and West Commissioning Support Unit then overwrite the organisation specific keys with the DSCRO key.
9. The mapping table is then deleted.
10. The DSCRO pass the pseudonymised SUS, local provider data, Mental Health (MHSDS, MHMDS, MHLDDS), Maternity (MSDS), Improving Access to Psychological Therapies (IAPT), Child and Young People’s Health (CYPHS) and Diagnostic Imaging (DIDS) securely to South, Central and West Commissioning Support Unit for the addition of derived fields, linkage of data sets and analysis
11. Social care data is then linked to the data sets listed within point 9.
12. South, Central and West Commissioning Support Unit then pass the processed, pseudonymised and linked data to the CCG.
13. Aggregation of required data for CCG management use will be completed by South, Central and West Commissioning Support Unit as instructed by the CCG.
14. Patient level data will not be shared outside of the CCG and will only be shared within the CCG on a need to know basis, as per the purposes stipulated within the Data Sharing Agreement. External aggregated reports only with small number suppression can be shared as set out within NHS Digital guidance applicable to each data set.

Data Processor 2 - Outcomes Based Health
1. Pseudonymised SUS+ (pseudonymised using an encryption key’ specific to this project), only is securely transferred from the DSCRO to South, Central and West Commissioning Support Unit..
2. South, Central and West Commissioning Support Unit add derived fields and securely transfer the data to the CCG.
3. The CCG passes the pseudonymised data to Outcomes Based Health
4. Outcomes Based Health then analyse the data to:
o Develop outcomes, baselining and monitoring of individual outcomes
o Provide detailed analysis relating to outcomes
5. Aggregation of required data for CCG management use will be completed by Outcomes Based Health or the CCG as instructed by the CCG.
6. Patient level data will not be shared outside of the CCG and will only be shared within the CCG on a need to know basis, as per the purposes stipulated within the Data Sharing Agreement. External aggregated reports only with small number suppression can be shared as set out within NHS Digital guidance applicable to each data set.

Project 13 — NIC-43358-L8W2Q

Type of data: information not disclosed for TRE projects

Opt outs honoured: N, Y ()

Legal basis: Health and Social Care Act 2012, Section 251 approval is in place for the flow of identifiable data

Purposes: ()

Sensitive: Sensitive

When:2017.06 — 2017.05.

Access method: Ongoing

Data-controller type:

Sublicensing allowed:

Datasets:

  1. Children and Young People's Health Services Data Set
  2. Improving Access to Psychological Therapies Data Set
  3. Local Provider Data - Acute
  4. Local Provider Data - Ambulance
  5. Local Provider Data - Community
  6. Local Provider Data - Mental Health
  7. Local Provider Data - Population data
  8. Mental Health and Learning Disabilities Data Set
  9. Mental Health Minimum Data Set
  10. Mental Health Services Data Set
  11. SUS Accident & Emergency data
  12. SUS Admitted Patient Care data
  13. SUS Outpatient data
  14. Local Provider Data - Demand for Service
  15. Local Provider Data - Diagnostic Services
  16. Local Provider Data - Emergency Care
  17. Local Provider Data - Other not elsewhere classified
  18. SUS data (Accident & Emergency, Admitted Patient Care & Outpatient)
  19. SUS for Commissioners
  20. Public Health and Screening Services-Local Provider Flows
  21. Primary Care Services-Local Provider Flows
  22. Population Data-Local Provider Flows
  23. Other Not Elsewhere Classified (NEC)-Local Provider Flows
  24. Mental Health-Local Provider Flows
  25. Maternity Services Data Set
  26. Experience, Quality and Outcomes-Local Provider Flows
  27. Emergency Care-Local Provider Flows
  28. Diagnostic Services-Local Provider Flows
  29. Diagnostic Imaging Dataset
  30. Demand for Service-Local Provider Flows
  31. Community-Local Provider Flows
  32. Children and Young People Health
  33. Ambulance-Local Provider Flows
  34. Acute-Local Provider Flows
  35. SUS (Accident & Emergency, Inpatient and Outpatient data)
  36. Local Provider Data - Acute, Ambulance, Community, Mental Health, Population data

Objectives:

Invoice Validation
As an approved Controlled Environment for Finance (CEfF), the data processor receives SUS data identifiable at the level of NHS number according to S.251 CAG 7-07(a) and (c)/2013, to undertake invoice validation on behalf of the CCG. NHS number is only used to confirm the accuracy of backing-data sets and will not be shared outside of the CEfF. The CCG are advised by the CSU whether payment for invoices can be made or not.

Risk Stratification
This is an application to use SUS data identifiable at the level of NHS number for the purpose of Risk Stratification. Risk Stratification provides a forecast of future demand by identifying high risk patients. This enables commissioners to initiate proactive management plans for patients that are potentially high service users.

Pseudonymised – SUS and Local Flows
Application for the CCG to use pseudonymised data to provide intelligence to support commissioning of health services. The pseudonymised data is required to ensure that analysis of health care provision can be completed to support the needs of the health profile of the population within the CCG area based on the full analysis of multiple pseudonymised datasets.

Pseudonymised – Mental Health, Maternity, IAPT, CYPHS and DIDS
Application for the CCG to use MHSDS, MHMDS, MHLDDS, MSDS, IAPT, CYPHS and DIDs pseudonymised data to provide intelligence to support commissioning of health services. The linked, pseudonymised data is required to ensure that analysis of health care provision can be completed to support the needs of the health profile of the population within the CCG area based on the full analysis of multiple pseudonymised datasets.


No record level data will be linked other than as specifically detailed within this application/agreement. Data will only be shared with those parties listed and will only be used for the purposes laid out in the application/agreement. The data to be released from the HSCIC will not be national data, but only that data relating to the specific locality of interest of the applicant.

Expected Benefits:

Invoice Validation
1. Financial validation of activity
2. CCG Budget control
3. Commissioning and performance management
4. Meeting commissioning objectives without compromising patient confidentiality
5. The avoidance of misappropriation of public funds to ensure the ongoing delivery of patient care

Risk Stratification
Risk stratification promotes improved case management in primary care and will lead to the following benefits being realised:
1. Improved planning by better understanding patient flows through the healthcare system, thus allowing commissioners to design appropriate pathways to improve patient flow and allowing commissioners to identify priorities and identify plans to address these.
2. Improved quality of services through reduced emergency readmissions, especially avoidable emergency admissions. This is achieved through mapping of frequent users of emergency services and early intervention of appropriate care.
3. Improved access to services by identifying which services may be in demand but have poor access, and from this identify areas where improvement is required.
4. Potentially reduced premature mortality by more targeted intervention in primary care, which supports the commissioner to meets its requirement to reduce premature mortality in line with the CCG Outcome Framework.
5. Better understanding of the health of and the variations in health outcomes within the population to help understand local population characteristics.
All of the above lead to improved patient experience through more effective commissioning of services.

Pseudonymised – SUS and Local Flows
1. Supporting Quality Innovation Productivity and Prevention (QIPP) to review demand management, integrated care and pathways.
a. Analysis to support full business cases.
b. Develop business models.
c. Monitor In year projects.
2. Supporting Joint Strategic Needs Assessment (JSNA) for specific disease types.
3. Health economic modelling using:
a. Analysis on provider performance against 18 weeks wait targets.
b. Learning from and predicting likely patient pathways for certain conditions, in order to influence early interventions and other treatments for patients.
c. Analysis of outcome measures for differential treatments, accounting for the full patient pathway.
d. Analysis to understand emergency care and linking A&E and Emergency Urgent Care Flows (EUCC).
4. Commissioning cycle support for grouping and re-costing previous activity.
5. Enables monitoring of:
a. CCG outcome indicators.
b. Non-financial validation of activity.
c. Successful delivery of integrated care within the CCG.
d. Checking frequent or multiple attendances to improve early intervention and avoid admissions.
e. Case management.
f. Care service planning.
g. Commissioning and performance management.
h. List size verification by GP practices.
i. Understanding the care of patients in nursing homes.
6. Feedback to NHS service providers on data quality at an aggregate and individual record level – only on data initially provided by the service providers.

Pseudonymised – Mental Health, Maternity, IAPT, CYPHS and DIDS
1. Supporting Quality Innovation Productivity and Prevention (QIPP) to review demand management, Integrated care and pathways.
a. Analysis to support full business cases.
b. Develop business models.
c. Monitor In year projects.
2. Supporting Joint Strategic Needs Assessment (JSNA) for specific disease types.
3. Health economic modelling using:
a. Analysis on provider performance against 18 weeks wait targets.
b. Learning from and predicting likely patient pathways for certain conditions, in order to influence early interventions and other treatments for patients.
c. Analysis of outcome measures for differential treatments, accounting for the full patient pathway.
4. Commissioning cycle support for grouping and re-costing previous activity.
5. Enables monitoring of:
a. CCG outcome indicators.
b. Non-financial validation of activity.
c. Successful delivery of integrated care within the CCG.
d. Checking frequent or multiple attendances to improve early intervention and avoid admissions.
e. Case management.
f. Care service planning.
g. Commissioning and performance management.
h. List size verification by GP practices.
i. Understanding the care of patients in nursing homes.
6. Feedback to NHS service providers on data quality at an aggregate and individual record level – only on data initially provided by the service providers.

Outputs:

Invoice Validation
1. Addressing poor data quality issues
2. Production of reports for business intelligence
3. Budget reporting
4. Validation of invoices for non-contracted events
Risk Stratification
1. As part of the risk stratification processing activity detailed above, GPs have access to the risk stratification tool which highlights patients for whom the GP is responsible and have been classed as at risk. The only identifier available to GPs is the NHS numbers of their own patients. Any further identification of the patients will be completed by the GP on their own systems.
2. Output from the risk stratification tool will provide aggregate reporting of number and percentage of population found to be at risk.
3. Record level output will be available for commissioners pseudonymised at patient level.
4. GP Practices will be able to view the risk scores for individual patients with the ability to display the underlying SUS data for the individual patients when it is required for direct care purposes by someone who has a legitimate relationship with the patient.
Pseudonymised – SUS and Local Flows
1. Commissioner reporting:
a. Summary by provider view - plan & actuals year to date (YTD).
b. Summary by Patient Outcome Data (POD) view - plan & actuals YTD.
c. Summary by provider view - activity & finance variance by POD.
d. Planned care by provider view - activity & finance plan & actuals YTD.
e. Planned care by POD view - activity plan & actuals YTD.
f. Provider reporting.
g. Statutory returns.
h. Statutory returns - monthly activity return.
i. Statutory returns - quarterly activity return.
j. Delayed discharges.
k. Quality & performance referral to treatment reporting.
2. Readmissions analysis.
3. Production of aggregate reports for CCG Business Intelligence.
4. Production of project / programme level dashboards.
5. Monitoring of acute / community / mental health quality matrix.
6. Clinical coding reviews / audits.
7. Budget reporting down to individual GP Practice level.
8. GP Practice level dashboard reports include high flyers.

Pseudonymised – Mental Health, Maternity, IAPT, CYPHS and DIDS
1. Commissioner reporting:
a. Summary by provider view - plan & actuals year to date (YTD).
b. Summary by Patient Outcome Data (POD) view - plan & actuals YTD.
c. Summary by provider view - activity & finance variance by POD.
d. Planned care by provider view - activity & finance plan & actuals YTD.
e. Planned care by POD view - activity plan & actuals YTD.
f. Provider reporting.
g. Statutory returns.
h. Statutory returns - monthly activity return.
i. Statutory returns - quarterly activity return.
j. Delayed discharges.
k. Quality & performance referral to treatment reporting.
2. Readmissions analysis.
3. Production of aggregate reports for CCG Business Intelligence.
4. Production of project / programme level dashboards.
5. Monitoring of mental health quality matrix.
6. Clinical coding reviews / audits.
7. Budget reporting down to individual GP Practice level.
8. GP Practice level dashboard reports include high flyers.
7) Budget reporting down to individual GP Practice level.

Processing:

Prior to the release of identifiable data by DSCRO South West, Type 2 objections will be applied and the relevant patient’s data redacted.
Invoice Validation
1. SUS Data is sent from the SUS Repository to South West DSCRO.
2. South West DSCRO pushes a one-way data flow of SUS data into the Controlled Environment for Finance (CEfF) in the South, Central and West CSU.
3. The CSU carry out the following processing activities within the CEfF for invoice validation purposes:
a. Checking the individual is registered to a particular Clinical Commissioning Group (CCG) and associated with an invoice from the national SUS data flow to validate the corresponding record in the backing data flow
b. Once the backing information is received, this will be checked against national NHS and local commissioning policies as well as being checked against system access and reports provided by the HSCIC to confirm the payments are:
i. In line with Payment by Results tariffs
ii. are in relation to a patient registered with a CCG GP or resident within the CCG area.
iii. The health care provided should be paid by the CCG in line with CCG guidance. 
4. The CCG are notified that the invoice has been validated and can be paid. Any discrepancies or non-validated invoices are investigated and resolved between the CSU CEfF team and the provider meaning that no identifiable data needs to be sent to the CCG. The CCG only receives notification to pay and management reporting detailing the total quantum of invoices received pending, processed etc.

Risk Stratification
1. Identifiable SUS data is sent from the SUS Repository to South West Data Services for Commissioners Regional Office (DSCRO).
2. Data quality management and standardisation of data is completed by South West DSCRO and the data identifiable at the level of NHS number is transferred securely to South, Central and West CSU, who hold the SUS data within the secure Data Centre on N3.
3. Identifiable GP Data is securely sent from the GP system to South, Central and West CSU.
4. SUS data is linked to GP data in the risk stratification tool by the data processor.
5. As part of the risk stratification processing activity, GPs have access to the risk stratification tool within the data processor, which highlights patients with whom the GP has a legitimate relationship and have been classed as at risk. The only identifier available to GPs is the NHS numbers of their own patients. Any further identification of the patients will be completed by the GP on their own systems.
6. South, Central and West CSU who hosts the risk stratification system that holds SUS data is limited to those administrative staff with authorised user accounts used for identification and authentication.
7. Once South, Central and West CSU has completed the processing, the CCG can access the online system via a secure N3 connection to access the data pseudonymised at patient level.

Pseudonymised – SUS and Local Flows
1. South West Data Services for Commissioners Regional Office (DSCRO) receives a flow of SUS identifiable data for the CCG from the SUS Repository. South West DSCRO also receives identifiable local provider data for the CCG directly from Providers.
2. Data quality management and pseudonymisation of data is completed by the DSCRO and the pseudonymised data is then passed securely to South, Central and West CSU for the addition of derived fields, linkage of data sets and analysis.
3. South, Central and West CSU then pass the processed, pseudonymised and linked data to the CCG who analyse the data to see patient journeys for pathways or service design, re-design and de-commissioning.
4. Aggregation of required data for CCG management use can be completed by the CSU or the CCG
5. Patient level data will not be shared outside of the CCG and will only be shared within the CCG on a need to know basis, as per the purposes stipulated within the Data Sharing Agreement. External aggregated reports only with small number suppression in line with the HES analysis guide.

Pseudonymised – Mental Health, MSDS, IAPT, CYPHS and DIDS
1. South West Data Services for Commissioners Regional Office (DSCRO) receives a flow of data identifiable at the level of NHS number for Mental Health (MHSDS, MHMDS, MHLDDS) and Maternity (MSDS). South West DSCRO also receive a flow of pseudonymised patient level data for each CCG for Improving Access to Psychological Therapies (IAPT), Child and Young People’s Health (CYPHS) and Diagnostic Imaging (DIDS) for commissioning purposes
2. Data quality management and pseudonymisation of data is completed by South West DSCRO and the pseudonymised data is then passed securely to South, Central and West CSU for the addition of derived fields, linkage of data sets and analysis.
3. South, Central and West CSU then pass the processed, pseudonymised and linked data to the CCG.
4. The CCG analyses the data to see patient journeys for pathway or service design, re-design and de-commissioning
5. Aggregation of required data for CCG management use can be completed by the CSU or the CCG
6. Patient level data will not be shared outside of the CCG and will only be shared within the CCG on a need to know basis, as per the purposes stipulated within the Data Sharing Agreement. External aggregated reports only with small number suppression.

Project 14 — NIC-43354-B7P4H

Type of data: information not disclosed for TRE projects

Opt outs honoured: N, Y ()

Legal basis: Health and Social Care Act 2012, Section 251 approval is in place for the flow of identifiable data

Purposes: ()

Sensitive: Sensitive

When:2017.06 — 2017.05.

Access method: Ongoing

Data-controller type:

Sublicensing allowed:

Datasets:

  1. Children and Young People's Health Services Data Set
  2. Improving Access to Psychological Therapies Data Set
  3. Local Provider Data - Acute
  4. Local Provider Data - Ambulance
  5. Local Provider Data - Community
  6. Local Provider Data - Mental Health
  7. Local Provider Data - Population data
  8. Mental Health and Learning Disabilities Data Set
  9. Mental Health Minimum Data Set
  10. Mental Health Services Data Set
  11. SUS Accident & Emergency data
  12. SUS Admitted Patient Care data
  13. SUS Outpatient data
  14. SUS data (Accident & Emergency, Admitted Patient Care & Outpatient)
  15. Local Provider Data - Other not elsewhere classified
  16. SUS for Commissioners
  17. Population Data-Local Provider Flows
  18. Other Not Elsewhere Classified (NEC)-Local Provider Flows
  19. Mental Health-Local Provider Flows
  20. Maternity Services Data Set
  21. Diagnostic Imaging Dataset
  22. Community-Local Provider Flows
  23. Children and Young People Health
  24. Ambulance-Local Provider Flows
  25. Acute-Local Provider Flows
  26. SUS (Accident & Emergency, Inpatient and Outpatient data)
  27. Local Provider Data - Acute, Ambulance, Community, Mental Health, Population data

Objectives:

Invoice Validation
As an approved Controlled Environment for Finance (CEfF), the data processor receives SUS data identifiable at the level of NHS number according to S.251 CAG 7-07(a) and (c)/2013, to undertake invoice validation on behalf of the CCG. NHS number is only used to confirm the accuracy of backing-data sets and will not be shared outside of the CEfF. The CCG are advised by the CSU whether payment for invoices can be made or not.

Risk Stratification
This is an application to use SUS data identifiable at the level of NHS number for the purpose of Risk Stratification. Risk Stratification provides a forecast of future demand by identifying high risk patients. This enables commissioners to initiate proactive management plans for patients that are potentially high service users.

Pseudonymised – SUS and Local Flows
Application for the CCG to use pseudonymised data to provide intelligence to support commissioning of health services. The pseudonymised data is required to ensure that analysis of health care provision can be completed to support the needs of the health profile of the population within the CCG area based on the full analysis of multiple pseudonymised datasets.

Pseudonymised – Mental Health, Maternity, IAPT, CYPHS and DIDS
Application for the CCG to use MHSDS, MHMDS, MHLDDS, MSDS, IAPT, CYPHS and DIDs pseudonymised data to provide intelligence to support commissioning of health services. The linked, pseudonymised data is required to ensure that analysis of health care provision can be completed to support the needs of the health profile of the population within the CCG area based on the full analysis of multiple pseudonymised datasets.


No record level data will be linked other than as specifically detailed within this application/agreement. Data will only be shared with those parties listed and will only be used for the purposes laid out in the application/agreement. The data to be released from the HSCIC will not be national data, but only that data relating to the specific locality of interest of the applicant.

Expected Benefits:

Invoice Validation
1. Financial validation of activity
2. CCG Budget control
3. Commissioning and performance management
4. Meeting commissioning objectives without compromising patient confidentiality
5. The avoidance of misappropriation of public funds to ensure the ongoing delivery of patient care

Risk Stratification
Risk stratification promotes improved case management in primary care and will lead to the following benefits being realised:
1. Improved planning by better understanding patient flows through the healthcare system, thus allowing commissioners to design appropriate pathways to improve patient flow and allowing commissioners to identify priorities and identify plans to address these.
2. Improved quality of services through reduced emergency readmissions, especially avoidable emergency admissions. This is achieved through mapping of frequent users of emergency services and early intervention of appropriate care.
3. Improved access to services by identifying which services may be in demand but have poor access, and from this identify areas where improvement is required.
4. Potentially reduced premature mortality by more targeted intervention in primary care, which supports the commissioner to meets its requirement to reduce premature mortality in line with the CCG Outcome Framework.
5. Better understanding of the health of and the variations in health outcomes within the population to help understand local population characteristics.
All of the above lead to improved patient experience through more effective commissioning of services.

Pseudonymised – SUS and Local Flows
1. Supporting Quality Innovation Productivity and Prevention (QIPP) to review demand management, integrated care and pathways.
a. Analysis to support full business cases.
b. Develop business models.
c. Monitor In year projects.
2. Supporting Joint Strategic Needs Assessment (JSNA) for specific disease types.
3. Health economic modelling using:
a. Analysis on provider performance against 18 weeks wait targets.
b. Learning from and predicting likely patient pathways for certain conditions, in order to influence early interventions and other treatments for patients.
c. Analysis of outcome measures for differential treatments, accounting for the full patient pathway.
d. Analysis to understand emergency care and linking A&E and Emergency Urgent Care Flows (EUCC).
4. Commissioning cycle support for grouping and re-costing previous activity.
5. Enables monitoring of:
a. CCG outcome indicators.
b. Non-financial validation of activity.
c. Successful delivery of integrated care within the CCG.
d. Checking frequent or multiple attendances to improve early intervention and avoid admissions.
e. Case management.
f. Care service planning.
g. Commissioning and performance management.
h. List size verification by GP practices.
i. Understanding the care of patients in nursing homes.
6. Feedback to NHS service providers on data quality at an aggregate and individual record level – only on data initially provided by the service providers.

Pseudonymised – Mental Health, Maternity, IAPT, CYPHS and DIDS
1. Supporting Quality Innovation Productivity and Prevention (QIPP) to review demand management, Integrated care and pathways.
a. Analysis to support full business cases.
b. Develop business models.
c. Monitor In year projects.
2. Supporting Joint Strategic Needs Assessment (JSNA) for specific disease types.
3. Health economic modelling using:
a. Analysis on provider performance against 18 weeks wait targets.
b. Learning from and predicting likely patient pathways for certain conditions, in order to influence early interventions and other treatments for patients.
c. Analysis of outcome measures for differential treatments, accounting for the full patient pathway.
4. Commissioning cycle support for grouping and re-costing previous activity.
5. Enables monitoring of:
a. CCG outcome indicators.
b. Non-financial validation of activity.
c. Successful delivery of integrated care within the CCG.
d. Checking frequent or multiple attendances to improve early intervention and avoid admissions.
e. Case management.
f. Care service planning.
g. Commissioning and performance management.
h. List size verification by GP practices.
i. Understanding the care of patients in nursing homes.
6. Feedback to NHS service providers on data quality at an aggregate and individual record level – only on data initially provided by the service providers.

Outputs:

Invoice Validation
1. Addressing poor data quality issues
2. Production of reports for business intelligence
3. Budget reporting
4. Validation of invoices for non-contracted events
Risk Stratification
1. As part of the risk stratification processing activity detailed above, GPs have access to the risk stratification tool which highlights patients for whom the GP is responsible and have been classed as at risk. The only identifier available to GPs is the NHS numbers of their own patients. Any further identification of the patients will be completed by the GP on their own systems.
2. Output from the risk stratification tool will provide aggregate reporting of number and percentage of population found to be at risk.
3. Record level output will be available for commissioners pseudonymised at patient level.
4. GP Practices will be able to view the risk scores for individual patients with the ability to display the underlying SUS data for the individual patients when it is required for direct care purposes by someone who has a legitimate relationship with the patient.
Pseudonymised – SUS and Local Flows
1. Commissioner reporting:
a. Summary by provider view - plan & actuals year to date (YTD).
b. Summary by Patient Outcome Data (POD) view - plan & actuals YTD.
c. Summary by provider view - activity & finance variance by POD.
d. Planned care by provider view - activity & finance plan & actuals YTD.
e. Planned care by POD view - activity plan & actuals YTD.
f. Provider reporting.
g. Statutory returns.
h. Statutory returns - monthly activity return.
i. Statutory returns - quarterly activity return.
j. Delayed discharges.
k. Quality & performance referral to treatment reporting.
2. Readmissions analysis.
3. Production of aggregate reports for CCG Business Intelligence.
4. Production of project / programme level dashboards.
5. Monitoring of acute / community / mental health quality matrix.
6. Clinical coding reviews / audits.
7. Budget reporting down to individual GP Practice level.
8. GP Practice level dashboard reports include high flyers.

Pseudonymised – Mental Health, Maternity, IAPT, CYPHS and DIDS
1. Commissioner reporting:
a. Summary by provider view - plan & actuals year to date (YTD).
b. Summary by Patient Outcome Data (POD) view - plan & actuals YTD.
c. Summary by provider view - activity & finance variance by POD.
d. Planned care by provider view - activity & finance plan & actuals YTD.
e. Planned care by POD view - activity plan & actuals YTD.
f. Provider reporting.
g. Statutory returns.
h. Statutory returns - monthly activity return.
i. Statutory returns - quarterly activity return.
j. Delayed discharges.
k. Quality & performance referral to treatment reporting.
2. Readmissions analysis.
3. Production of aggregate reports for CCG Business Intelligence.
4. Production of project / programme level dashboards.
5. Monitoring of mental health quality matrix.
6. Clinical coding reviews / audits.
7. Budget reporting down to individual GP Practice level.
8. GP Practice level dashboard reports include high flyers.
7) Budget reporting down to individual GP Practice level.

Processing:

Prior to the release of identifiable data by DSCRO South West, Type 2 objections will be applied and the relevant patient’s data redacted.
Invoice Validation
1. SUS Data is sent from the SUS Repository to South West DSCRO.
2. South West DSCRO pushes a one-way data flow of SUS data into the Controlled Environment for Finance (CEfF) in the South, Central and West CSU.
3. The CSU carry out the following processing activities within the CEfF for invoice validation purposes:
a. Checking the individual is registered to a particular Clinical Commissioning Group (CCG) and associated with an invoice from the national SUS data flow to validate the corresponding record in the backing data flow
b. Once the backing information is received, this will be checked against national NHS and local commissioning policies as well as being checked against system access and reports provided by the HSCIC to confirm the payments are:
i. In line with Payment by Results tariffs
ii. are in relation to a patient registered with a CCG GP or resident within the CCG area.
iii. The health care provided should be paid by the CCG in line with CCG guidance. 
4. The CCG are notified that the invoice has been validated and can be paid. Any discrepancies or non-validated invoices are investigated and resolved between the CSU CEfF team and the provider meaning that no identifiable data needs to be sent to the CCG. The CCG only receives notification to pay and management reporting detailing the total quantum of invoices received pending, processed etc.

Risk Stratification
1. Identifiable SUS data is sent from the SUS Repository to South West Data Services for Commissioners Regional Office (DSCRO).
2. Data quality management and standardisation of data is completed by South West DSCRO and the data identifiable at the level of NHS number is transferred securely to South, Central and West CSU, who hold the SUS data within the secure Data Centre on N3.
3. Identifiable GP Data is securely sent from the GP system to South, Central and West CSU.
4. SUS data is linked to GP data in the risk stratification tool by the data processor.
5. As part of the risk stratification processing activity, GPs have access to the risk stratification tool within the data processor, which highlights patients with whom the GP has a legitimate relationship and have been classed as at risk. The only identifier available to GPs is the NHS numbers of their own patients. Any further identification of the patients will be completed by the GP on their own systems.
6. South, Central and West CSU who hosts the risk stratification system that holds SUS data is limited to those administrative staff with authorised user accounts used for identification and authentication.
7. Once South, Central and West CSU has completed the processing, the CCG can access the online system via a secure N3 connection to access the data pseudonymised at patient level.

Pseudonymised – SUS and Local Flows
1. South West Data Services for Commissioners Regional Office (DSCRO) receives a flow of SUS identifiable data for the CCG from the SUS Repository. South West DSCRO also receives identifiable local provider data for the CCG directly from Providers.
2. Data quality management and pseudonymisation of data is completed by the DSCRO and the pseudonymised data is then passed securely to South, Central and West CSU for the addition of derived fields, linkage of data sets and analysis.
3. South, Central and West CSU then pass the processed, pseudonymised and linked data to the CCG who analyse the data to see patient journeys for pathways or service design, re-design and de-commissioning.
4. Aggregation of required data for CCG management use can be completed by the CSU or the CCG
5. Patient level data will not be shared outside of the CCG and will only be shared within the CCG on a need to know basis, as per the purposes stipulated within the Data Sharing Agreement. External aggregated reports only with small number suppression in line with the HES analysis guide.

Pseudonymised – Mental Health, MSDS, IAPT, CYPHS and DIDS
1. South West Data Services for Commissioners Regional Office (DSCRO) receives a flow of data identifiable at the level of NHS number for Mental Health (MHSDS, MHMDS, MHLDDS) and Maternity (MSDS). South West DSCRO also receive a flow of pseudonymised patient level data for each CCG for Improving Access to Psychological Therapies (IAPT), Child and Young People’s Health (CYPHS) and Diagnostic Imaging (DIDS) for commissioning purposes
2. Data quality management and pseudonymisation of data is completed by South West DSCRO and the pseudonymised data is then passed securely to South, Central and West CSU for the addition of derived fields, linkage of data sets and analysis.
3. South, Central and West CSU then pass the processed, pseudonymised and linked data to the CCG.
4. The CCG analyses the data to see patient journeys for pathway or service design, re-design and de-commissioning
5. Aggregation of required data for CCG management use can be completed by the CSU or the CCG
6. Patient level data will not be shared outside of the CCG and will only be shared within the CCG on a need to know basis, as per the purposes stipulated within the Data Sharing Agreement. External aggregated reports only with small number suppression.

Project 15 — NIC-43355-Q4R2Y

Type of data: information not disclosed for TRE projects

Opt outs honoured: N, Y ()

Legal basis: Health and Social Care Act 2012, Section 251 approval is in place for the flow of identifiable data

Purposes: ()

Sensitive: Sensitive

When:2017.06 — 2017.05.

Access method: Ongoing

Data-controller type:

Sublicensing allowed:

Datasets:

  1. Children and Young People's Health Services Data Set
  2. Improving Access to Psychological Therapies Data Set
  3. Local Provider Data - Acute
  4. Local Provider Data - Ambulance
  5. Local Provider Data - Community
  6. Local Provider Data - Mental Health
  7. Local Provider Data - Population data
  8. Mental Health and Learning Disabilities Data Set
  9. Mental Health Minimum Data Set
  10. Mental Health Services Data Set
  11. SUS Accident & Emergency data
  12. SUS Admitted Patient Care data
  13. SUS Outpatient data
  14. SUS data (Accident & Emergency, Admitted Patient Care & Outpatient)
  15. Local Provider Data - Other not elsewhere classified
  16. SUS for Commissioners
  17. Population Data-Local Provider Flows
  18. Other Not Elsewhere Classified (NEC)-Local Provider Flows
  19. Mental Health-Local Provider Flows
  20. Maternity Services Data Set
  21. Diagnostic Imaging Dataset
  22. Community-Local Provider Flows
  23. Children and Young People Health
  24. Ambulance-Local Provider Flows
  25. Acute-Local Provider Flows
  26. SUS (Accident & Emergency, Inpatient and Outpatient data)
  27. Local Provider Data - Acute, Ambulance, Community, Mental Health, Population data

Objectives:

Invoice Validation
As an approved Controlled Environment for Finance (CEfF), the data processor receives SUS data identifiable at the level of NHS number according to S.251 CAG 7-07(a) and (c)/2013, to undertake invoice validation on behalf of the CCG. NHS number is only used to confirm the accuracy of backing-data sets and will not be shared outside of the CEfF. The CCG are advised by the CSU whether payment for invoices can be made or not.

Risk Stratification
This is an application to use SUS data identifiable at the level of NHS number for the purpose of Risk Stratification. Risk Stratification provides a forecast of future demand by identifying high risk patients. This enables commissioners to initiate proactive management plans for patients that are potentially high service users.

Pseudonymised – SUS and Local Flows
Application for the CCG to use pseudonymised data to provide intelligence to support commissioning of health services. The pseudonymised data is required to ensure that analysis of health care provision can be completed to support the needs of the health profile of the population within the CCG area based on the full analysis of multiple pseudonymised datasets.

Pseudonymised – Mental Health, Maternity, IAPT, CYPHS and DIDS
Application for the CCG to use MHSDS, MHMDS, MHLDDS, MSDS, IAPT, CYPHS and DIDs pseudonymised data to provide intelligence to support commissioning of health services. The linked, pseudonymised data is required to ensure that analysis of health care provision can be completed to support the needs of the health profile of the population within the CCG area based on the full analysis of multiple pseudonymised datasets.


No record level data will be linked other than as specifically detailed within this application/agreement. Data will only be shared with those parties listed and will only be used for the purposes laid out in the application/agreement. The data to be released from the HSCIC will not be national data, but only that data relating to the specific locality of interest of the applicant.

Expected Benefits:

Invoice Validation
1. Financial validation of activity
2. CCG Budget control
3. Commissioning and performance management
4. Meeting commissioning objectives without compromising patient confidentiality
5. The avoidance of misappropriation of public funds to ensure the ongoing delivery of patient care

Risk Stratification
Risk stratification promotes improved case management in primary care and will lead to the following benefits being realised:
1. Improved planning by better understanding patient flows through the healthcare system, thus allowing commissioners to design appropriate pathways to improve patient flow and allowing commissioners to identify priorities and identify plans to address these.
2. Improved quality of services through reduced emergency readmissions, especially avoidable emergency admissions. This is achieved through mapping of frequent users of emergency services and early intervention of appropriate care.
3. Improved access to services by identifying which services may be in demand but have poor access, and from this identify areas where improvement is required.
4. Potentially reduced premature mortality by more targeted intervention in primary care, which supports the commissioner to meets its requirement to reduce premature mortality in line with the CCG Outcome Framework.
5. Better understanding of the health of and the variations in health outcomes within the population to help understand local population characteristics.
All of the above lead to improved patient experience through more effective commissioning of services.

Pseudonymised – SUS and Local Flows
1. Supporting Quality Innovation Productivity and Prevention (QIPP) to review demand management, integrated care and pathways.
a. Analysis to support full business cases.
b. Develop business models.
c. Monitor In year projects.
2. Supporting Joint Strategic Needs Assessment (JSNA) for specific disease types.
3. Health economic modelling using:
a. Analysis on provider performance against 18 weeks wait targets.
b. Learning from and predicting likely patient pathways for certain conditions, in order to influence early interventions and other treatments for patients.
c. Analysis of outcome measures for differential treatments, accounting for the full patient pathway.
d. Analysis to understand emergency care and linking A&E and Emergency Urgent Care Flows (EUCC).
4. Commissioning cycle support for grouping and re-costing previous activity.
5. Enables monitoring of:
a. CCG outcome indicators.
b. Non-financial validation of activity.
c. Successful delivery of integrated care within the CCG.
d. Checking frequent or multiple attendances to improve early intervention and avoid admissions.
e. Case management.
f. Care service planning.
g. Commissioning and performance management.
h. List size verification by GP practices.
i. Understanding the care of patients in nursing homes.
6. Feedback to NHS service providers on data quality at an aggregate and individual record level – only on data initially provided by the service providers.

Pseudonymised – Mental Health, Maternity, IAPT, CYPHS and DIDS
1. Supporting Quality Innovation Productivity and Prevention (QIPP) to review demand management, Integrated care and pathways.
a. Analysis to support full business cases.
b. Develop business models.
c. Monitor In year projects.
2. Supporting Joint Strategic Needs Assessment (JSNA) for specific disease types.
3. Health economic modelling using:
a. Analysis on provider performance against 18 weeks wait targets.
b. Learning from and predicting likely patient pathways for certain conditions, in order to influence early interventions and other treatments for patients.
c. Analysis of outcome measures for differential treatments, accounting for the full patient pathway.
4. Commissioning cycle support for grouping and re-costing previous activity.
5. Enables monitoring of:
a. CCG outcome indicators.
b. Non-financial validation of activity.
c. Successful delivery of integrated care within the CCG.
d. Checking frequent or multiple attendances to improve early intervention and avoid admissions.
e. Case management.
f. Care service planning.
g. Commissioning and performance management.
h. List size verification by GP practices.
i. Understanding the care of patients in nursing homes.
6. Feedback to NHS service providers on data quality at an aggregate and individual record level – only on data initially provided by the service providers.

Outputs:

Invoice Validation
1. Addressing poor data quality issues
2. Production of reports for business intelligence
3. Budget reporting
4. Validation of invoices for non-contracted events
Risk Stratification
1. As part of the risk stratification processing activity detailed above, GPs have access to the risk stratification tool which highlights patients for whom the GP is responsible and have been classed as at risk. The only identifier available to GPs is the NHS numbers of their own patients. Any further identification of the patients will be completed by the GP on their own systems.
2. Output from the risk stratification tool will provide aggregate reporting of number and percentage of population found to be at risk.
3. Record level output will be available for commissioners pseudonymised at patient level.
4. GP Practices will be able to view the risk scores for individual patients with the ability to display the underlying SUS data for the individual patients when it is required for direct care purposes by someone who has a legitimate relationship with the patient.
Pseudonymised – SUS and Local Flows
1. Commissioner reporting:
a. Summary by provider view - plan & actuals year to date (YTD).
b. Summary by Patient Outcome Data (POD) view - plan & actuals YTD.
c. Summary by provider view - activity & finance variance by POD.
d. Planned care by provider view - activity & finance plan & actuals YTD.
e. Planned care by POD view - activity plan & actuals YTD.
f. Provider reporting.
g. Statutory returns.
h. Statutory returns - monthly activity return.
i. Statutory returns - quarterly activity return.
j. Delayed discharges.
k. Quality & performance referral to treatment reporting.
2. Readmissions analysis.
3. Production of aggregate reports for CCG Business Intelligence.
4. Production of project / programme level dashboards.
5. Monitoring of acute / community / mental health quality matrix.
6. Clinical coding reviews / audits.
7. Budget reporting down to individual GP Practice level.
8. GP Practice level dashboard reports include high flyers.

Pseudonymised – Mental Health, Maternity, IAPT, CYPHS and DIDS
1. Commissioner reporting:
a. Summary by provider view - plan & actuals year to date (YTD).
b. Summary by Patient Outcome Data (POD) view - plan & actuals YTD.
c. Summary by provider view - activity & finance variance by POD.
d. Planned care by provider view - activity & finance plan & actuals YTD.
e. Planned care by POD view - activity plan & actuals YTD.
f. Provider reporting.
g. Statutory returns.
h. Statutory returns - monthly activity return.
i. Statutory returns - quarterly activity return.
j. Delayed discharges.
k. Quality & performance referral to treatment reporting.
2. Readmissions analysis.
3. Production of aggregate reports for CCG Business Intelligence.
4. Production of project / programme level dashboards.
5. Monitoring of mental health quality matrix.
6. Clinical coding reviews / audits.
7. Budget reporting down to individual GP Practice level.
8. GP Practice level dashboard reports include high flyers.
7) Budget reporting down to individual GP Practice level.

Processing:

Prior to the release of identifiable data by DSCRO South West, Type 2 objections will be applied and the relevant patient’s data redacted.
Invoice Validation
1. SUS Data is sent from the SUS Repository to South West DSCRO.
2. South West DSCRO pushes a one-way data flow of SUS data into the Controlled Environment for Finance (CEfF) in the South, Central and West CSU.
3. The CSU carry out the following processing activities within the CEfF for invoice validation purposes:
a. Checking the individual is registered to a particular Clinical Commissioning Group (CCG) and associated with an invoice from the national SUS data flow to validate the corresponding record in the backing data flow
b. Once the backing information is received, this will be checked against national NHS and local commissioning policies as well as being checked against system access and reports provided by the HSCIC to confirm the payments are:
i. In line with Payment by Results tariffs
ii. are in relation to a patient registered with a CCG GP or resident within the CCG area.
iii. The health care provided should be paid by the CCG in line with CCG guidance. 
4. The CCG are notified that the invoice has been validated and can be paid. Any discrepancies or non-validated invoices are investigated and resolved between the CSU CEfF team and the provider meaning that no identifiable data needs to be sent to the CCG. The CCG only receives notification to pay and management reporting detailing the total quantum of invoices received pending, processed etc.

Risk Stratification
1. Identifiable SUS data is sent from the SUS Repository to South West Data Services for Commissioners Regional Office (DSCRO).
2. Data quality management and standardisation of data is completed by South West DSCRO and the data identifiable at the level of NHS number is transferred securely to South, Central and West CSU, who hold the SUS data within the secure Data Centre on N3.
3. Identifiable GP Data is securely sent from the GP system to South, Central and West CSU.
4. SUS data is linked to GP data in the risk stratification tool by the data processor.
5. As part of the risk stratification processing activity, GPs have access to the risk stratification tool within the data processor, which highlights patients with whom the GP has a legitimate relationship and have been classed as at risk. The only identifier available to GPs is the NHS numbers of their own patients. Any further identification of the patients will be completed by the GP on their own systems.
6. South, Central and West CSU who hosts the risk stratification system that holds SUS data is limited to those administrative staff with authorised user accounts used for identification and authentication.
7. Once South, Central and West CSU has completed the processing, the CCG can access the online system via a secure N3 connection to access the data pseudonymised at patient level.

Pseudonymised – SUS and Local Flows
1. South West Data Services for Commissioners Regional Office (DSCRO) receives a flow of SUS identifiable data for the CCG from the SUS Repository. South West DSCRO also receives identifiable local provider data for the CCG directly from Providers.
2. Data quality management and pseudonymisation of data is completed by the DSCRO and the pseudonymised data is then passed securely to South, Central and West CSU for the addition of derived fields, linkage of data sets and analysis.
3. South, Central and West CSU then pass the processed, pseudonymised and linked data to the CCG who analyse the data to see patient journeys for pathways or service design, re-design and de-commissioning.
4. Aggregation of required data for CCG management use can be completed by the CSU or the CCG
5. Patient level data will not be shared outside of the CCG and will only be shared within the CCG on a need to know basis, as per the purposes stipulated within the Data Sharing Agreement. External aggregated reports only with small number suppression in line with the HES analysis guide.

Pseudonymised – Mental Health, MSDS, IAPT, CYPHS and DIDS
1. South West Data Services for Commissioners Regional Office (DSCRO) receives a flow of data identifiable at the level of NHS number for Mental Health (MHSDS, MHMDS, MHLDDS) and Maternity (MSDS). South West DSCRO also receive a flow of pseudonymised patient level data for each CCG for Improving Access to Psychological Therapies (IAPT), Child and Young People’s Health (CYPHS) and Diagnostic Imaging (DIDS) for commissioning purposes
2. Data quality management and pseudonymisation of data is completed by South West DSCRO and the pseudonymised data is then passed securely to South, Central and West CSU for the addition of derived fields, linkage of data sets and analysis.
3. South, Central and West CSU then pass the processed, pseudonymised and linked data to the CCG.
4. The CCG analyses the data to see patient journeys for pathway or service design, re-design and de-commissioning
5. Aggregation of required data for CCG management use can be completed by the CSU or the CCG
6. Patient level data will not be shared outside of the CCG and will only be shared within the CCG on a need to know basis, as per the purposes stipulated within the Data Sharing Agreement. External aggregated reports only with small number suppression.