NHS Digital Data Release Register - reformatted

NHS Stockport Ccg

Project 1 — NIC-110660-G9W6M

Opt outs honoured: Y, N

Sensitive: Sensitive

When: 2018/03 — 2018/05.

Repeats: Ongoing

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

Categories: Identifiable, Anonymised - ICO code compliant

Datasets:

  • SUS for Commissioners
  • Public Health and Screening Services-Local Provider Flows
  • Primary Care Services-Local Provider Flows
  • Population Data-Local Provider Flows
  • Other Not Elsewhere Classified (NEC)-Local Provider Flows
  • Mental Health-Local Provider Flows
  • Mental Health Services Data Set
  • Mental Health Minimum Data Set
  • Mental Health and Learning Disabilities Data Set
  • Maternity Services Data Set
  • Improving Access to Psychological Therapies Data Set
  • Experience, Quality and Outcomes-Local Provider Flows
  • Emergency Care-Local Provider Flows
  • Diagnostic Services-Local Provider Flows
  • Diagnostic Imaging Dataset
  • Demand for Service-Local Provider Flows
  • Community-Local Provider Flows
  • Children and Young People Health
  • Ambulance-Local Provider Flows
  • Acute-Local Provider Flows

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. 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. d. Pooled health and social care budget reporting 2. Supporting Joint Strategic Needs Assessment (JSNA) for specific disease types and patient groups 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 and social care. 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. New commissioning and service delivery models delivered via joint health and social care teams reducing duplication 8. Reduction in variation of outcomes and quality of care through increased understanding of primary and secondary care interaction. E.g. if cancer treatment outcomes are poor in one area does the GP data indicate a delayed referral? 9. A complete understanding of service utilisation to aid capacity/demand planning across health and social care 10. Early warning of likely pressures in the wider health and system following increased activity in primary and social care giving other providers a chance to plan and react

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: o Stratify populations based on: disease profiles; conditions currently being treated; current service use; pharmacy use and risk of future overall cost o Plan work for commissioning services and contracts o Set up capitated budgets o 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. All of the above segmented in to population groups 10. Analysis across health and social care by patient (outputs aggregated) providing a greater understand of service interdependencies and opportunities for a single service delivery model where overlap may exist currently 11. Variation reporting between primary and secondary care (e.g. where one care setting suggests the patient has a condition but the other does not potentially leading to inappropriate treatment) 12. Delayed transfers of care analysis

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 specified within the Data Minimisation Efforts within Annex A of the Data Sharing Agreement. Access is limited to those substantive employees with authorised user accounts used for identification and authentication. 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. 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 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 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. Invoice Validation Stockport CCG - The Data Services for Commissioners Regional Office (DSCRO), receives a flow of identifiable SUS data from the SUS Repository. - Data quality management and pseudonymisation of data is completed by the DSCRO and the pseudonymised data is then passed securely to Arden & GEM CSU for the addition of any derived fields. - Arden & GEM CSU then passes the pseudonymised data securely to the CCG. - The CCG conduct the following processing activities for invoice validation purposes: o Checking invoiced activity is registered to the Clinical Commissioning Group (CCG) by using the derived commissioner field in SUS and associated with an invoice from the national SUS data flow to validate corresponding records in the backing data flow o 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:  In line with Payment by Results tariffs  Are in relation to patients registered with the CCG GPs or resident within the CCG area.  The health care provided should be paid by the CCG in line with CCG guidance.  - The CCG are notified that the invoice has been validated and can be paid. Any discrepancies or non-validated invoices are investigated and resolved Risk Stratification Data Processor 1 – Arden and GEM CSU - SUS Data is sent from the SUS Repository to the Data Services for Commissioners Regional Office (DSCRO) to the data processor. - SUS data identifiable at the level of NHS number regarding hospital admissions, A&E attendances and outpatient attendances is delivered securely from the DSCRO to the data processor. - 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 Arden & GEM CSU, who hold the SUS data within the secure Data Centre on N3. - Identifiable GP Data is securely sent from the GP system to Arden & GEM CSU. - SUS data is linked to GP data in the risk stratification tool by the data processor. - Arden & GEM 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. - Once Arden & GEM CSU has completed the processing, the data is passed to the CCG in pseudonymised form at patient level and as aggregated reports. - 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 derived from SUS available to GPs is the NHS number of their own patients. Any further identification of the patients is derived from the GP data sourced from their own systems. Data Processor 5 - North of England Commissioning Support Unit - SUS Data is sent from the SUS Repository to the Data Services for Commissioners Regional Office (DSCRO) to the data processor. - SUS data identifiable at the level of NHS number regarding hospital admissions, A&E attendances and outpatient attendances is delivered securely from DSCRO North to the data processor following the upholding of patient objections - Identifiable GP Data is securely sent from the GP system to the data processor. - SUS data is linked to GP data in the risk stratification tool by the data processor. - North of England CSU (NECS) 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. - Once NECS has completed the processing, the data is made available to the CCG in pseudonymised form at patient level and as aggregated reports. - 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 derived from SUS available to GPs is the NHS number of their own patients. Any further identification of the patients is derived from the GP data sourced from their own systems. Data Processor 1 – Arden and GEM CSU 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) and Diagnostic Imaging data (DIDS) only is securely transferred from the DSCRO to Arden and GEM CSU. 2) Arden and GEM CSU add derived fields, link data and provide analysis. 3) Allowed linkage is between the data sets contained within point 1. 4) Arden and GEM CSU then pass the processed, pseudonymised and linked data to the CCG. The CCG analyse the data to see patient journeys for pathways or service design, re-design and de-commissioning. 5) Aggregation of required data for CCG management use will be completed by the CSU 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. Data Processor 2 – Greater Manchester Shared Services (GMSS) (via DP1): 1) Pseudonymised SUS, Local Provider data, Mental Health data (MHSDS, MHMDS, MHLDDS), Maternity data (MSDS) and Improving Access to Psychological Therapies data (IAPT) only is securely transferred from the DSCRO to Arden and GEM CSU. 2) Arden and GEM CSU add derived fields, link data and provide analysis. 3) Allowed linkage is between the data sets contained within point 1. 4) Arden and GEM CSU then pass the processed, pseudonymised and linked data to the Greater Manchester Shared Services (GMSS) hosted by NHS Oldham CCG. 5) GMSS analyse the data to see patient journeys for pathway or service design, re-design and de-commissioning. 6) GMSS then pass the processed pseudonymised data to the CCG 7) 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. Data Processor 3 – Advancing Quality Alliance (AQuA) (via DP1): 1) Pseudonymised SUS, Local Provider data and Mental Health data (MHSDS, MHMDS, MHLDDS) only is securely transferred from the DSCRO to Arden and GEM CSU. 2) Arden and GEM CSU add derived fields, link data and provide analysis. 3) Allowed linkage is between the data sets contained within point 1. 4) Arden and GEM CSU then pass the processed, pseudonymised and linked data to Advancing Quality Alliance (AQuA) to provide support for a range of quality improvement programmes including the NW Advancing Quality Programme. AQuA identifies cohorts of patients within specific disease groups for further analysis to help drive quality improvements across the region. 5) AQuA produces aggregate reports only with small number suppression. Only aggregate reports are sent to the CCG. Data Processor 4 – Greater Manchester The Academic Health Sciences Network (Utilisation Management Team) (SUS Only) (via DP1): 1) Pseudonymised SUS data only is securely transferred from the DSCRO to Arden and GEM CSU. 2) Arden and GEM CSU add derived fields, link data and provide analysis. 3) Allowed linkage is between the data sets contained within point 1. 4) Arden and GEM CSU then pass the processed, pseudonymised and linked data to the Greater Manchester The Academic Health Sciences Network (Utilisation Management Team) (AHSN UMT) 5) The AHSN UMT receive pseudonymised SUS data for Greater Manchester patients. They analyse the data to look at processes rather than patients, for example, A&E performance, process times, bed days as well as ‘deep dives’ to support clinical reviews for CCGs. 6) AHSN UMT produces aggregate reports only with small number suppression. Only aggregate reports are sent to the CCG. Data Processor 6 - Outcomes Based Healthcare (Via DP1): SUS Data The Data Services for Commissioners Regional Office (DSCRO) obtains the SUS. Data quality management and pseudonymisation is completed within the DSCRO. The SUS data is pseudonymised using an ‘Encryption Key’ that is specific to the Stockport Together (ST) project. The data is then disseminated as follows: 1) Pseudonymised SUS data is securely transferred from the DSCRO to the CCG. 2) The CCG analyse the data to see patient journeys for pathways or service design, re-design and de-commissioning. The CCG then pass the pseudonymised data to Outcomes Based Healthcare via secure FTP. Primary Care Data 1) Identifiable GP Data is securely sent from the GP systems to Arden and Greater East Midlands Commissioning Support Unit, which acts as data processor on behalf of the GP practices. 2) Arden and Greater East Midlands Commissioning Support Unit process the data to meet the requirements specified, in order to provide baseline and monitoring data for the Stockport Together Outcomes Framework. This includes addition of derived fields. 3) The data is pseudonymised by Arden and Greater East Midlands Commissioning Support Unit (acting on behalf of the GP practices) using the ‘Encryption Key’ which is specific to the ST project, provided by the DSCRO. 4) Arden and Greater East Midlands Commissioning Support Unit then pass the pseudonymised primary care data in consistently pseudonymised form at patient level to the CCG via secure FTP. 5) The CCG analyse the data to see patient journeys for pathways or service design, re-design and de-commissioning. The CCG then pass the pseudonymised data to Outcomes Based Healthcare via secure FTP. 6) GPs are able to access re-identified data for their own patients and only for the purpose of direct care. Social Care Data 1) Data quality management of Adult Social Care data is completed by Stockport Metropolitan Borough Council. 2) The Social Care data is pseudonymised at source using the ST specific ‘Encryption Key’, provided by the DSCRO. This consistently pseudonymised data is securely passed to the CCG using the ST shared area of the local secure network. 3) The CCG analyse the data to see patient journeys for pathways or service design, re-design and de-commissioning. The CCG then pass the pseudonymised data to Outcomes Based Healthcare via secure FTP. Outcomes Based Healthcare will undertake linkage of the pseudonymised data sets, using the consistent pseudonym to make the link. This will be done within a controlled environment by named members of staff. Outcomes Based Healthcare will make available on-line reports to the CCG to provide high level intelligence, based on a holistic view of care across the Stockport health and care system. Access to commissioning intelligence at pseudonymised record level will be available to 2 named members of staff in the CCG. Access to aggregate commissioning intelligence reports with small number suppression only will be available to a range of other named users in the Stockport Together partner organisations. The data will be used to analytically understand patient journeys for pathway and service re-design. Access to commissioning intelligence is governed by the organisation employee code of practice, data protection policies and information governance protocols. Additionally, access to the record level data will conform to a specific information access agreement which governs how the data will be handled and used. Outcomes Based Healthcare will be responsible for linking the data but will not have access to the pseudonymisation tool, which allows data to be pseudonymised using the Encryption key. The Encryption key will only be shared by the DSCRO with named individuals in the GP practices (or their data processors) and Stockport Metropolitan Borough Council (Adult Social Care). This is to enable the GP data and the Adult Social Care data to be pseudonymised at source. The key cannot be used to re-identify data as it only allows for one-way pseudonymisation. Access to the pseudonymised data is provided to Outcome Based Healthcare and the CCG only and will only be used for the purposes specified. The data will not be transferred, shared or otherwise made available to any third party. Re-identification can only occur for GPs who have a legitimate relationship with the patient and only for the purpose of direct care. 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. Diagnostic Imaging Data Set (DIDS) Data quality management and pseudonymisation is completed within the DSCRO and is then disseminated as follows: Data Processor 1 – North of England Commissioning Support Unit (CSU) 1. Data quality management and pseudonymisation of data is completed by the DSCRO and the pseudonymised data (Flow 1, 2 and 3) is then held until completion of points 2 – 7. 2. North of England CSU also receive GP Data. It is received as follows: a. Identifiable GP data is submitted to the CSU. b. The data lands in a ring fenced area for GP data only . c. There is a Data Processing Agreement in place between the GP and the CSU. A specific named individual within the CSU acts on behalf on the GP. This person has been issued with a black box. d. The individual requests a pseudonymisation key from the DSCRO to the black box. The key can only be used once. The key is specific to that GP and the pseudonymisation request. The individual does not have access to the data once it has been passed on to the CSU. e. The GP data is then pseudonymised using the black box and DSCRO issued key – the clear data is then deleted from the ring fenced area. f. The CSU are then sent the identifiable GP data with the pseudo key specific to them. 3. North of England CSU also receive a pseudonymised flow of social care data. Social Care data is received as follows: a. The social care organisation is issued with their own black box solution. b. The social care organisation requests a pseudonymisation key from the DSCRO to the black box. The key can only be used once. The key is specific to that organisation and the pseudonymisation request. c. The social care organisation submit the pseudonymised social care data to the CSU with the pseudo algorithm specific to them. 4. Once the pseudonymised GP data and social care data is received, the CSU make a request to the DSCRO. 5. The DSCRO then send a mapping table to the CSU 6. The CSU then overwrite the organisation specific keys with the DSCRO key. 7. The mapping table is then deleted. 8. The DSCRO then 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 North of England CSU for the addition of derived fields, linkage of data sets and analysis. 9. Social care and GP data is then linked to the data sets listed within point 9 in the CSU utilising algorithms and analysis 10. Aggregation of required data for CCG management use will be completed by the CSU as instructed by the CCG. 11. Patient level data will not be shared outside of the Data Processor/Controller and will only be shared within the Data Processors 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

Objectives:

Risk Stratification To use SUS data identifiable at the level of NHS number according to S.251 CAG 7-04(a) (and Primary Care Data) 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. Risk Stratification enables General Practitioners (GPs) to better target intervention in Primary Care. Risk Stratification will be conducted by: - Data Processor 1 – Arden and Greater East Midlands (AGEM) Commissioning Support Unit (CSU) conduct Risk Stratification. The Risk Stratification is done using the Kings Fund Combine Predictive Model and the data are made available to the CCG as extracts, which can be analysed using local tools. GP practices are able to view identifiable data. The Risk Stratification data from AGEM CSU will be sent to the CCG through the same mechanism as other datasets received directly from AGEM data management. All data will therefore be presented/accessed using the same tools enabling consistent use, comparisons and linkage of data at the appropriate level. As AGEM provide a data management service, data are typically provided as data extracts (or by providing access to such data), enabling more complex, bespoke analysis. - Data Processor 5 - North of England Commissioning Support Unit (NECS) The NECS Risk Stratification data are presented to GPs through a Business Intelligence (BI) Tool ‘RAIDR’, and the same tool is also used to present the data to the CCG as interactive reports. All data/reports from NECS are provided in a consistent manner through this same mechanism; the RAIDR BI Tool. RAIDR typically presents reports derived from data extracts through a more user-friendly interface, allowing users to undertake some analysis and report presentation. The CCG can only view aggregate reports but practice users can see identifiable data for their patients. GPs will be able to access re-identified data for their own patients GMSS deliver a range of services including; - effective use of resources; - data quality; - information governance; - market management; - provider contract & performance management; To enable GMSS to support these services a team within the GMSS have controlled access to SUS data at a pseudonymised level. Access to the data is controlled by AGEM CSU using users’ roles to ensure only appropriate users gain access to pseudonymised data. Data can then be used for reporting to support the range of services being offered to CCGs, and CCGs receive aggregate level reports from GMSS. GMSS staff are separate from Oldham CCG staff and accordingly have separate functions and roles. - Data Processor 3 - Advancing Quality Alliance (AQuA) provide support for a range of quality improvement programmes including the NW Advancing Quality Programme. They will identify cohorts of patients within specific disease groups for further analysis to help drive quality improvements across the region. - Data Processor 4 - Greater Manchester The Academic Health Sciences Network (Utilisation Management Team) receive Pseudonymised SUS data for Greater Manchester patients. They analyse the data to look at processes rather than patients, for example, A&E performance, process times, bed days as well as ‘deep dives’ to support clinical reviews for CCGs. Advancing Quality Alliance (AQuA) and the Academic Health Science Network are hosted by Salford Royal NHS Foundation Trust who are the legal entity for both. - Data Processor 6 - Outcomes Based Healthcare use pseudonymised SUS, primary care and social care data to support construction of a local outcomes framework for Stockport Together. This involves linking the local pseudonymised data sets (primary care and SUS) and reconciling information between them to enable reliable population segmentation and outcomes measurement. Stockport Together is a transformational programme for health and social care in Stockport. It is one of 50 national ‘vanguards’, selected by the Department of Health to take a lead on the development of new care models and to provide the blueprints for the NHS moving forward. Stockport Together is a partnership between local health and care organisations - Stockport NHS Foundation Trust, NHS Stockport Clinical Commissioning Group, Pennine Care NHS Foundation Trust, Stockport Metropolitan Borough Council and Stockport’s GP Federation - Viaduct Health. Working alongside GPs and voluntary organisations, the aim of Stockport Together is to ensure the best possible outcomes for local people at a time of growing demand and restricted funding. To achieve this, the programme is proposing to fundamentally reform the way health and social care is delivered in Stockport, providing more integrated forms of care, underpinned by significant investment in out of hospital services. Objective for processing: Invoice Validation As an approved Controlled Environment for Finance (CEfF), North of England Commissioning Support Unit (CSU) 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 To use SUS data identifiable at the level of NHS number according to S.251 CAG 7-04(a) (and Primary Care Data) 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. Risk Stratification enables General Practitioners (GPs) to better target intervention in Primary Care. Risk Stratification will be conducted by North of England Commissioning Support Unit (CSU) Commissioning 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. 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 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) - Diagnostic Imaging Data Set (DIDS) 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 North of England Commissioning Support Unit (CSU) In addition, North of England Commissioning Support Unit also receive pseudonymised GP data, Social Care data and Consented Data. This is pseudonymised either at source or within North of England Commissioning Support Unit. This pseudonymisation tool is different to that held within the DSCRO. Also, each data source will use a variation of this tool so there is no linkage between these data until a common pseudonym has been applied via the DSCRO. Invoice Validation Identifiable SUS Data is obtained from the SUS Repository to the Data Services for Commissioners Regional Office (DSCRO). 1. The DSCRO pushes a one-way data flow of SUS data into the Controlled Environment for Finance (CEfF) in the North of England Commissioning Support Unit (CSU). 2. 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 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. 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.  3. 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 North of England 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.


Project 2 — NIC-110660-G9W6M 

Opt outs honoured: N, Y

Sensitive: Sensitive

When: 2017/12 — 2018/02.

Repeats: Ongoing

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

Categories: Anonymised - ICO code compliant, Identifiable

Datasets:

  • SUS data (Accident & Emergency, Admitted Patient Care & Outpatient)
  • Improving Access to Psychological Therapies Data Set
  • Mental Health Minimum Data Set
  • Mental Health and Learning Disabilities Data Set
  • Children and Young People's Health Services Data Set
  • Maternity Services Dataset
  • Mental Health Services Data Set
  • Local Provider Data - Acute
  • Local Provider Data - Ambulance
  • Local Provider Data - Community
  • Local Provider Data - Demand for Service
  • Local Provider Data - Diagnostic Services
  • Local Provider Data - Emergency Care
  • Local Provider Data - Experience Quality and Outcomes
  • Local Provider Data - Mental Health
  • Local Provider Data - Other not elsewhere classified
  • Local Provider Data - Population Data

Benefits:

Expected measurable benefits to health and/or social care including target date: 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. 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. 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. Commissioning with primary care and adult social care data There are several key benefits of having a linked dataset: 1. Better forecasting and demand modelling: Having a linked dataset enables more robust demand modelling through having a joined up picture of service usage. A linked dataset will inform and enable decisions about adapting and prioritising the delivery of services between individual providers within Stockport Together. 2. Financial planning: A linked dataset will allow better analysis of the costs of different types of care that are currently being delivered. This will allow the most efficient and optimal mix of care to be planned and subsequently provided. It will also support the delivery of new contract types, covering multiple providers. 3. Improved risk stratification: Linked data will provide a more accurate picture of health and care service usage by individuals across the Stockport Together partners, thus enabling services to be adapted and delivered in a way which targets those at greatest risk and aims to reduce their likelihood of having a significant health event or deteriorating. 4. Better monitoring of performance: A linked dataset will enable more accurate monitoring of the overall health and care system across Stockport, from a patient / service user perspective. This will ensure that patients / service-users are at the centre of the health and care system and also enable alternative measures of performance and more effective monitoring to be put in place. 5. Performance monitoring for the Outcomes Framework: A linked data set will support the key Stockport Together objective of moving towards a whole-population Outcomes Framework. In order to contract and monitor achievement of outcomes accurately, measurement of outcomes must be robust but, without a linked dataset, outcomes can only be partially captured based on a specific service or care setting. This means that outcomes measurement will not be as robust. All of the above will contribute towards the delivery of a more joined up, more efficient and higher quality care service and improved population outcomes.

Outputs:

Specific outputs expected, including target date: 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 and aggregate with small number suppression. 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: o Stratify populations based on: disease profiles; conditions currently being treated; current service use; pharmacy use and risk of future overall cost o Plan work for commissioning services and contracts o Set up capitated budgets o 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. Commissioning with primary care and adult social care data Commissioner reporting: o Reablement o Emergency admissions analysis o Reduction in length of stay and transfer of care delays o Planned care by POD view - activity plan & actuals YTD. o Identify baselines to measure future new service o Production of aggregated reports for integrated business intelligence o Production of project / programme level dashboards o Patient population profiling 1. Supporting Quality Innovation Productivity and Prevention (QIPP) to review demand management and pathways 2. Supporting Joint Strategic Needs Assessment (JSNA) for specific disease types 3. Health economic modelling using: o Analysis on provider performance against 18 weeks wait targets o Learning from and predicting likely patient pathways for certain conditions, in order to influence early interventions and other treatments for patients. o Analysis of outcome measures for differential treatments, accounting for the full patient pathway. o Analysis to understand emergency care and linking A&E and Emergency Urgent Care Flows (EUCC). 4. Enables monitoring of: o CCG and Adult Social Care outcome measures o Non-financial validation of activity o Successful delivery of integrated care o Checking frequent or multiple attendances to improve early intervention and avoid admission o Case Management o Care service planning o Commissioning and performance management o List size verification by GP provider o Understanding care of patients in nursing homes

Processing:

Processing activities: 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 specified within the Data Minimisation Efforts within Annex A of the Data Sharing Agreement. Access is limited to those substantive employees with authorised user accounts used for identification and authentication. 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. 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 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 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. Invoice Validation Stockport CCG - The Data Services for Commissioners Regional Office (DSCRO), receives a flow of identifiable SUS data from the SUS Repository. - Data quality management and pseudonymisation of data is completed by the DSCRO and the pseudonymised data is then passed securely to Arden & GEM CSU for the addition of any derived fields. - Arden & GEM CSU then passes the pseudonymised data securely to the CCG. - The CCG conduct the following processing activities for invoice validation purposes: o Checking invoiced activity is registered to the Clinical Commissioning Group (CCG) by using the derived commissioner field in SUS and associated with an invoice from the national SUS data flow to validate corresponding records in the backing data flow o 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:  In line with Payment by Results tariffs  Are in relation to patients registered with the CCG GPs or resident within the CCG area.  The health care provided should be paid by the CCG in line with CCG guidance.  - The CCG are notified that the invoice has been validated and can be paid. Any discrepancies or non-validated invoices are investigated and resolved Risk Stratification Data Processor 1 – Arden and GEM CSU - SUS Data is sent from the SUS Repository to the Data Services for Commissioners Regional Office (DSCRO) to the data processor. - SUS data identifiable at the level of NHS number regarding hospital admissions, A&E attendances and outpatient attendances is delivered securely from the DSCRO to the data processor. - 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 Arden & GEM CSU, who hold the SUS data within the secure Data Centre on N3. - Identifiable GP Data is securely sent from the GP system to Arden & GEM CSU. - SUS data is linked to GP data in the risk stratification tool by the data processor. - Arden & GEM 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. - Once Arden & GEM CSU has completed the processing, the data is passed to the CCG in pseudonymised form at patient level and as aggregated reports. - 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 derived from SUS available to GPs is the NHS number of their own patients. Any further identification of the patients is derived from the GP data sourced from their own systems. Data Processor 5 - North of England Commissioning Support Unit - SUS Data is sent from the SUS Repository to the Data Services for Commissioners Regional Office (DSCRO) to the data processor. - SUS data identifiable at the level of NHS number regarding hospital admissions, A&E attendances and outpatient attendances is delivered securely from DSCRO North to the data processor following the upholding of patient objections - Identifiable GP Data is securely sent from the GP system to the data processor. - SUS data is linked to GP data in the risk stratification tool by the data processor. - North of England CSU (NECS) 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. - Once NECS has completed the processing, the data is made available to the CCG in pseudonymised form at patient level and as aggregated reports. - 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 derived from SUS available to GPs is the NHS number of their own patients. Any further identification of the patients is derived from the GP data sourced from their own systems. Commissioning The Data Services for Commissioners Regional Office (DSCRO) obtains the following data sets: - SUS - 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 - 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) - Diagnostic Imaging Data Set (DIDS) Data quality management and pseudonymisation is completed within the DSCRO and is then disseminated as follows: Data Processor 1 – Arden and GEM CSU 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) and Diagnostic Imaging data (DIDS) only is securely transferred from the DSCRO to Arden and GEM CSU. 2) Arden and GEM CSU add derived fields, link data and provide analysis. 3) Allowed linkage is between the data sets contained within point 1. 4) Arden and GEM CSU then pass the processed, pseudonymised and linked data to the CCG. The CCG analyse the data to see patient journeys for pathways or service design, re-design and de-commissioning. 5) Aggregation of required data for CCG management use will be completed by the CSU 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. Data Processor 2 – Greater Manchester Shared Services (GMSS) (via DP1): 1) Pseudonymised SUS, Local Provider data, Mental Health data (MHSDS, MHMDS, MHLDDS), Maternity data (MSDS) and Improving Access to Psychological Therapies data (IAPT) only is securely transferred from the DSCRO to Arden and GEM CSU. 2) Arden and GEM CSU add derived fields, link data and provide analysis. 3) Allowed linkage is between the data sets contained within point 1. 4) Arden and GEM CSU then pass the processed, pseudonymised and linked data to the Greater Manchester Shared Services (GMSS) hosted by NHS Oldham CCG. 5) GMSS analyse the data to see patient journeys for pathway or service design, re-design and de-commissioning. 6) GMSS then pass the processed pseudonymised data to the CCG 7) 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. Data Processor 3 – Advancing Quality Alliance (AQuA) (via DP1): 1) Pseudonymised SUS, Local Provider data and Mental Health data (MHSDS, MHMDS, MHLDDS) only is securely transferred from the DSCRO to Arden and GEM CSU. 2) Arden and GEM CSU add derived fields, link data and provide analysis. 3) Allowed linkage is between the data sets contained within point 1. 4) Arden and GEM CSU then pass the processed, pseudonymised and linked data to Advancing Quality Alliance (AQuA) to provide support for a range of quality improvement programmes including the NW Advancing Quality Programme. AQuA identifies cohorts of patients within specific disease groups for further analysis to help drive quality improvements across the region. 5) AQuA produces aggregate reports only with small number suppression. Only aggregate reports are sent to the CCG. Data Processor 4 – Greater Manchester The Academic Health Sciences Network (Utilisation Management Team) (SUS Only) (via DP1): 1) Pseudonymised SUS data only is securely transferred from the DSCRO to Arden and GEM CSU. 2) Arden and GEM CSU add derived fields, link data and provide analysis. 3) Allowed linkage is between the data sets contained within point 1. 4) Arden and GEM CSU then pass the processed, pseudonymised and linked data to the Greater Manchester The Academic Health Sciences Network (Utilisation Management Team) (AHSN UMT) 5) The AHSN UMT receive pseudonymised SUS data for Greater Manchester patients. They analyse the data to look at processes rather than patients, for example, A&E performance, process times, bed days as well as ‘deep dives’ to support clinical reviews for CCGs. 6) AHSN UMT produces aggregate reports only with small number suppression. Only aggregate reports are sent to the CCG. Data Processor 6 - Outcomes Based Healthcare (Via DP1): SUS Data The Data Services for Commissioners Regional Office (DSCRO) obtains the SUS. Data quality management and pseudonymisation is completed within the DSCRO. The SUS data is pseudonymised using an ‘Encryption Key’ that is specific to the Stockport Together (ST) project. The data is then disseminated as follows: 1) Pseudonymised SUS data is securely transferred from the DSCRO to the CCG. 2) The CCG analyse the data to see patient journeys for pathways or service design, re-design and de-commissioning. The CCG then pass the pseudonymised data to Outcomes Based Healthcare via secure FTP. Primary Care Data 1) Identifiable GP Data is securely sent from the GP systems to Arden and Greater East Midlands Commissioning Support Unit, which acts as data processor on behalf of the GP practices. 2) Arden and Greater East Midlands Commissioning Support Unit process the data to meet the requirements specified, in order to provide baseline and monitoring data for the Stockport Together Outcomes Framework. This includes addition of derived fields. 3) The data is pseudonymised by Arden and Greater East Midlands Commissioning Support Unit (acting on behalf of the GP practices) using the ‘Encryption Key’ which is specific to the ST project, provided by the DSCRO. 4) Arden and Greater East Midlands Commissioning Support Unit then pass the pseudonymised primary care data in consistently pseudonymised form at patient level to the CCG via secure FTP. 5) The CCG analyse the data to see patient journeys for pathways or service design, re-design and de-commissioning. The CCG then pass the pseudonymised data to Outcomes Based Healthcare via secure FTP. 6) GPs are able to access re-identified data for their own patients and only for the purpose of direct care. Social Care Data 1) Data quality management of Adult Social Care data is completed by Stockport Metropolitan Borough Council. 2) The Social Care data is pseudonymised at source using the ST specific ‘Encryption Key’, provided by the DSCRO. This consistently pseudonymised data is securely passed to the CCG using the ST shared area of the local secure network. 3) The CCG analyse the data to see patient journeys for pathways or service design, re-design and de-commissioning. The CCG then pass the pseudonymised data to Outcomes Based Healthcare via secure FTP. Outcomes Based Healthcare will undertake linkage of the pseudonymised data sets, using the consistent pseudonym to make the link. This will be done within a controlled environment by named members of staff. Outcomes Based Healthcare will make available on-line reports to the CCG to provide high level intelligence, based on a holistic view of care across the Stockport health and care system. Access to commissioning intelligence at pseudonymised record level will be available to 2 named members of staff in the CCG. Access to aggregate commissioning intelligence reports with small number suppression only will be available to a range of other named users in the Stockport Together partner organisations. The data will be used to analytically understand patient journeys for pathway and service re-design. Access to commissioning intelligence is governed by the organisation employee code of practice, data protection policies and information governance protocols. Additionally, access to the record level data will conform to a specific information access agreement which governs how the data will be handled and used. Outcomes Based Healthcare will be responsible for linking the data but will not have access to the pseudonymisation tool, which allows data to be pseudonymised using the Encryption key. The Encryption key will only be shared by the DSCRO with named individuals in the GP practices (or their data processors) and Stockport Metropolitan Borough Council (Adult Social Care). This is to enable the GP data and the Adult Social Care data to be pseudonymised at source. The key cannot be used to re-identify data as it only allows for one-way pseudonymisation. Access to the pseudonymised data is provided to Outcome Based Healthcare and the CCG only and will only be used for the purposes specified. The data will not be transferred, shared or otherwise made available to any third party. Re-identification can only occur for GPs who have a legitimate relationship with the patient and only for the purpose of direct care.

Objectives:

Objective for processing: Invoice Validation The Clinical Commissioning Group (CCG) receives pseudonymised SUS and local provider flows data. These data are required for the purpose of invoice validation and will be used to confirm the accuracy of backing-data sets and will not be shared outside of the CCG. Data cannot be matched on NHS Number as this is not present in the data, but can be used to validate invoices to a level that is acceptable to the CCG. If there is no data in SUS or local provider flows data that can be used to validate the invoice, another data set is used from providers which shows practice / area codes to confirm the patient is from the CCG area in order to pay an invoice. Invoice Validation is conducted by Stockport CCG using pseudonymised SUS and local provider flows. Risk Stratification To use SUS data identifiable at the level of NHS number according to S.251 CAG 7-04(a) (and Primary Care Data) 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. Risk Stratification enables General Practitioners (GPs) to better target intervention in Primary Care. Risk Stratification will be conducted by: - Data Processor 1 – Arden and Greater East Midlands (AGEM) Commissioning Support Unit (CSU) conduct Risk Stratification. The Risk Stratification is done using the Kings Fund Combine Predictive Model and the data are made available to the CCG as extracts, which can be analysed using local tools. GP practices are able to view identifiable data. The Risk Stratification data from AGEM CSU will be sent to the CCG through the same mechanism as other datasets received directly from AGEM data management. All data will therefore be presented/accessed using the same tools enabling consistent use, comparisons and linkage of data at the appropriate level. As AGEM provide a data management service, data are typically provided as data extracts (or by providing access to such data), enabling more complex, bespoke analysis. - Data Processor 5 - North of England Commissioning Support Unit (NECS) The NECS Risk Stratification data are presented to GPs through a Business Intelligence (BI) Tool ‘RAIDR’, and the same tool is also used to present the data to the CCG as interactive reports. All data/reports from NECS are provided in a consistent manner through this same mechanism; the RAIDR BI Tool. RAIDR typically presents reports derived from data extracts through a more user-friendly interface, allowing users to undertake some analysis and report presentation. The CCG can only view aggregate reports but practice users can see identifiable data for their patients. GPs will be able to access re-identified data for their own patients Commissioning 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. 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 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) - Diagnostic Imaging Data Set (DIDS) 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: - Data Processor 1 – Arden and GEM CSU conduct Risk Stratification as instructed by the CCG. The CSU also processes SUS, Local Provider flows, mental health, IAPT, MSDS, CYPHS and DIDS for the purpose of commissioning. - Data Processor 2 - Greater Manchester Shared Services (GMSS) have taken BI services in house and are now hosted by Oldham CCG. AGEM CSU flow data to a small team within GMSS. Access to the data is restricted to this team who access and manage the data. These BI services were previously provided by North West CSU. GMSS deliver a range of services including; - effective use of resources; - data quality; - information governance; - market management; - provider contract & performance management; To enable GMSS to support these services a team within the GMSS have controlled access to SUS data at a pseudonymised level. Access to the data is controlled by AGEM CSU using users’ roles to ensure only appropriate users gain access to pseudonymised data. Data can then be used for reporting to support the range of services being offered to CCGs, and CCGs receive aggregate level reports from GMSS. GMSS staff are separate from Oldham CCG staff and accordingly have separate functions and roles. - Data Processor 3 - Advancing Quality Alliance (AQuA) provide support for a range of quality improvement programmes including the NW Advancing Quality Programme. They will identify cohorts of patients within specific disease groups for further analysis to help drive quality improvements across the region. - Data Processor 4 - Greater Manchester The Academic Health Sciences Network (Utilisation Management Team) receive Pseudonymised SUS data for Greater Manchester patients. They analyse the data to look at processes rather than patients, for example, A&E performance, process times, bed days as well as ‘deep dives’ to support clinical reviews for CCGs. Advancing Quality Alliance (AQuA) and the Academic Health Science Network are hosted by Salford Royal NHS Foundation Trust who are the legal entity for both. - Data Processor 6 - Outcomes Based Healthcare use pseudonymised SUS, primary care and social care data to support construction of a local outcomes framework for Stockport Together. This involves linking the local pseudonymised data sets (primary care and SUS) and reconciling information between them to enable reliable population segmentation and outcomes measurement. Stockport Together is a transformational programme for health and social care in Stockport. It is one of 50 national ‘vanguards’, selected by the Department of Health to take a lead on the development of new care models and to provide the blueprints for the NHS moving forward. Stockport Together is a partnership between local health and care organisations - Stockport NHS Foundation Trust, NHS Stockport Clinical Commissioning Group, Pennine Care NHS Foundation Trust, Stockport Metropolitan Borough Council and Stockport’s GP Federation - Viaduct Health. Working alongside GPs and voluntary organisations, the aim of Stockport Together is to ensure the best possible outcomes for local people at a time of growing demand and restricted funding. To achieve this, the programme is proposing to fundamentally reform the way health and social care is delivered in Stockport, providing more integrated forms of care, underpinned by significant investment in out of hospital services.


Project 3 — NIC-47205-Q7R1C

Opt outs honoured: Y, N

Sensitive: Sensitive

When: 2016/12 — 2017/11.

Repeats: Ongoing

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

Categories: Identifiable, Anonymised - ICO code compliant

Datasets:

  • SUS (Accident & Emergency, Inpatient and Outpatient data)
  • Local Provider Data - Acute, Ambulance, Community, Demand for Service, Diagnostic Services, Emergency Care, Experience Quality and Outcomes, Mental Health, Other not elsewhere classified, Population Data, Primary Care
  • Mental Health Minimum Data Set
  • Mental Health and Learning Disabilities Data Set
  • Mental Health Services Data Set
  • Improving Access to Psychological Therapies Data Set
  • Children and Young People's Health Services Data Set
  • Local Provider Data - Acute
  • Local Provider Data - Ambulance
  • Local Provider Data - Community
  • Local Provider Data - Demand for Service
  • Local Provider Data - Diagnostic Services
  • Local Provider Data - Emergency Care
  • Local Provider Data - Experience Quality and Outcomes
  • Local Provider Data - Mental Health
  • Local Provider Data - Other not elsewhere classified
  • Local Provider Data - Population Data
  • Local Provider Data - Public Health & Screening services
  • SUS Accident & Emergency data
  • SUS Admitted Patient Care data
  • SUS Outpatient data
  • Maternity Services Dataset

Benefits:

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. 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.

Outputs:

Risk Stratification 1. 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 risk stratification presents pseudonymised data to the GPs. GPs are able to re-identify information only for their own patients for the purpose of direct care. 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 and aggregated reports. 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.

Processing:

Prior to the release of identifiable data by North West DSCRO, Type 2 objections will be applied and the relevant patient’s data redacted. Risk Stratification 1. SUS Data is sent from the SUS Repository to North West Data Services for Commissioners Regional Office (DSCRO) to the data processor. 2. SUS data identifiable at the level of NHS number regarding hospital admissions, A&E attendances and outpatient attendances is delivered securely from North West DSCRO to the data processor. 3. Data quality management and standardisation of data is completed by North West DSCRO and the data identifiable at the level of NHS number is transferred securely to Arden & GEM CSU, who hold the SUS data within the secure Data Centre on N3. 4. Identifiable GP Data is securely sent from the GP system to Arden & GEM CSU. 5. SUS data is linked to GP data in the risk stratification tool by the data processor. 6. Arden & GEM 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 Arden & GEM CSU has completed the processing, the data is passed to the CCG in pseudonymised form at patient level and as aggregated reports. Pseudonymised – SUS and Local Flows Data Processor 2 – GMSS (via DP1): 1. North West Data Services for Commissioners Regional Office (DSCRO) receives a flow of SUS identifiable data for the CCG from the SUS Repository. North 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 North West DSCRO and the pseudonymised data is then passed securely to Arden & GEM CSU for the addition of derived fields, linkage of data sets and analysis. 3. Arden & GEM CSU then passes the pseudonymised data securely to the Greater Manchester Shared Services (GMSS). 4. GMSS analyse the data to see patient journeys for pathway or service design, re-design and de-commissioning. 5. GMSS then pass the processed pseudonymised data to 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 in line with the HES analysis guide. Data Processor 4 – AQuA (via DP1): 1. North West Data Services for Commissioners Regional Office (DSCRO) receives a flow of SUS identifiable data for the CCG from the SUS Repository. North 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 North West DSCRO and the pseudonymised data is then passed securely to Arden & GEM CSU for the addition of derived fields, linkage of data sets and analysis. 3. Arden & GEM CSU then passes the pseudonymised data securely to AQuA to provide support for a range of quality improvement programmes including the NW Advancing Quality Programme. AQuA identifies cohorts of patients within specific disease groups for further analysis to help drive quality improvements across the region. 4. AQuA produces aggregate reports only with small number suppression in line with the HES analysis guide. Only aggregate reports are sent to the CCG. Data Processor 5 – Academic Health Sciences Network (Utilisation Management Team) (SUS Only) (via DP1):: 1. North West Data Services for Commissioners Regional Office (DSCRO) receives a flow of SUS identifiable data for the CCG from the SUS Repository. 2. Data quality management and pseudonymisation of data is completed by North West DSCRO and the pseudonymised data is then passed securely to Arden & GEM CSU for the addition of derived fields, linkage of data and analysis. 3. Arden & GEM CSU then passes the pseudonymised data securely to the Academic Health Service (Utilisation Management Team) (AHSN UMT) 4. The AHSN UMT receive pseudonymised SUS data for Greater Manchester patients. They analyse the data to look at processes rather than patients, for example, A&E performance, process times, bed days as well as ‘deep dives’ to support clinical reviews for CCGs. 5. AHSN UMT produces aggregate reports only with small number suppression in line with the HES analysis guide. Only aggregate reports are sent to the CCG. NHS Bury CCG, NHS Heywood, Middleton and Rochdale CCG, NHS North Manchester CCG and NHS Oldham CCG have a collaborative information sharing agreement in place to share pseudonymised SLAM and SLAM Backup data between these CCGs only. SLAM data is included under Local Flows and is available under the Health and Social Care Act 2012. Pseudonymised – Mental Health and IAPT Data Processor 1 – Arden & GEM CSU 1. North 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 MSDS. North 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 1. Data quality management and pseudonymisation of data is completed by North West DSCRO and the pseudonymised data is then passed securely to Arden & GEM CSU for the addition of derived fields, linkage of data sets and analysis. 2. Arden & GEM 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. 3. The CCG analyses the data to see patient journeys for pathway 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. Data Processor 2 – GMSS (via DP1): Greater Manchester Shared Services (GMSS) have taken BI services in house and are now hosted by Oldham CCG. AGEM CSU flow data to a small team within GMSS. Access to the data is restricted to this team who access and manage the data. These BI services were previously provided by North West CSU. GMSS deliver a range of services including; • effective use of resources; • data quality; • information governance; • market management; • provider contract & performance management; To enable GMSS to support these services a team within the GMSS have controlled access to SUS data at a pseudonymised level. Access to the data is controlled by AGEM CSU using users’ roles to ensure only appropriate users gain access to pseudonymised data. Data can then be used for reporting to support the range of services being offered to CCGs, and CCGs receive aggregate level reports from GMSS. GMSS staff are separate from Oldham CCG staff and accordingly have separate functions and roles. 1. North 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) North West DSCRO also receive a flow of pseudonymised patient level data for each CCG for Improving Access to Psychological Therapies (IAPT) for commissioning purposes 2. The pseudonymised data is securely transferred from North West DSCRO to Arden & GEM CSU for the addition of derived fields, linkage of data sets and analysis. 3. Arden & GEM CSU then pass the processed, pseudonymised and linked data to the Greater Manchester Shared Services (GMSS) 4. GMSS analyse and conduct the BI function and then send the Pseudonymised data to 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. Data Processor 4 - Advancing Quality Alliance (AQuA) (via DP1): 1. North 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). 2. Data quality management and pseudonymisation of data is completed by North West DSCRO and the pseudonymised data is then passed securely to Arden & GEM CSU for the addition of derived fields, linkage of data sets and analysis. 3. Arden & GEM CSU then passes the pseudonymised data securely to Advancing Quality Alliance (AQuA). 4. AQuA receives pseudonymised SUS data for Greater Manchester patients. They analyse the data to look at processes rather than patients, for example, A&E performance, process times, bed days as well as ‘deep dives’ to support clinical reviews for CCGs. 5. AQuA produces aggregate reports only with small number suppression in line with the HES analysis guide. Only aggregate reports are sent to the CCG.

Objectives:

Risk Stratification To use SUS data identifiable at the level of NHS number according to S.251 CAG 7-04(a) (and Primary Care Data) 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. Risk Stratification enables GPs to better target intervention in Primary Care Pseudonymised – SUS and Local Flows 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. 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. Pseudonymised – Mental Health, Maternity, IAPT, CYPHS and DIDS To use pseudonymised data for the following datasets to provide intelligence to support commissioning of health services : - 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) - Diagnostic Imaging Data Set (DIDS) 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. 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.