NHS Digital Data Release Register - reformatted

NHS Midlands and Lancashire Commissioning Support Unit

Project 1 — DARS-NIC-317048-N8P0R

Opt outs honoured: No - data flow is not identifiable (Does not include the flow of confidential data)

Sensitive: Sensitive

When: 2020/02 — 2020/02.

Repeats: Frequent Adhoc Flow

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

Categories: Anonymised - ICO code compliant

Datasets:

  • Acute-Local Provider Flows
  • Ambulance-Local Provider Flows
  • Children and Young People Health
  • Civil Registration - Births
  • Civil Registration - Deaths
  • Community Services Data Set
  • Community-Local Provider Flows
  • Demand for Service-Local Provider Flows
  • Diagnostic Imaging Dataset
  • Diagnostic Services-Local Provider Flows
  • Emergency Care-Local Provider Flows
  • Experience, Quality and Outcomes-Local Provider Flows
  • Improving Access to Psychological Therapies Data Set
  • Maternity Services Data Set
  • Mental Health and Learning Disabilities Data Set
  • Mental Health Minimum Data Set
  • Mental Health Services Data Set
  • Mental Health-Local Provider Flows
  • National Cancer Waiting Times Monitoring DataSet (CWT)
  • National Diabetes Audit
  • Other Not Elsewhere Classified (NEC)-Local Provider Flows
  • Patient Reported Outcome Measures
  • Population Data-Local Provider Flows
  • Primary Care Services-Local Provider Flows
  • Public Health and Screening Services-Local Provider Flows
  • SUS for Commissioners

Objectives:

Commissioning The Worcestershire and Hereford CCG's have come together to improve health and care, and are currently moving to one senior management board and plan to formally join at some point in the future. The four CCG's are as follows: NHS Herefordshire CCG, NHS Redditch and Bromsgrove CCG, NHS South Worcestershire CCG, NHS Wyre Forest CCG The joint collaboration will be responsible for implementing large parts of the 5 year forward view from NHS England. The collaboration will be 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 the 4 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 collaboration 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's will work proactively and collaboratively to redesign services across boundaries to integrate services. Collaborative sharing is required for CCGs to understand these requirements. The CCGs 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 geographical area of Worcestershire and Herefordshire. 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) - Community Services Data Set (CSDS) - Diagnostic Imaging Data Set (DIDS) - National Cancer Waiting Times Monitoring Data Set (CWT) - Civil Registries Data (CRD) (Births) - Civil Registries Data (CRD) (Deaths) - National Diabetes Audit (NDA) - Patient Reported Outcome Measures (PROMs)   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 § 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 highlight 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 Midlands and Lancashire Commissioning Support Unit.

Expected Benefits:

i. Benefits Type: ii. Expected Measurable Benefits to Health and/or Social Care Including Target Date: 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 ʹonly 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 15. Cost-benefit analysis and service impact assessments to underpin service transformation across health economy a. Service planning and re-design (development of NMoC and integrated care pathways, new partnerships, working with new providers etc.) b. Impact analysis for different models or productivity measures, efficiency and experiencec. Service and pathway review d. Service utilisation review 16. Ensuring compliance with evidence and guidance a. Testing approaches with evidence and compliance with guidance. 17. Monitoring outcomes a. Analysis of variation in outcomes across population group 18. 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 19. 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 20. 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 innovationsd. Predictive modelling 21. 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 22. 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

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 Modelling12. 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 hospitalii. 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 planning17. 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 services to react to terror situations 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 New Models of Care (NMOC), Accountable Care Organisations (ACO), Sustainable Transformation Partnerships (STP) 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, STPs24. 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 activity26. Improving provider data quality a. Coding audit b. Data quality validation and review c. Checking validity of patient identity and commissioner assignment

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.   Data can only be stored at the addresses listed under storage addresses.   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. Data released will only be shared with those parties listed and will only be used for the purposes laid out in the application/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 ie: employees, agents and contractors of the Data Recipient who may have access to that data) ONWARD SHARING: 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.   Aggregated reports only with small number suppression can be shared externally as set out within NHS Digital guidance applicable to each data set.     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.   Where the Data Processor and/or the Data Controller hold identifiable data with opt outs applied and identifiable data with opt outs not applied, the data will be held separately so data cannot be linked.   All access to data is auditable by NHS Digital. DATA MINIMISATION: Data Minimisation in relation to the data sets listed within the application are listed below. This also includes the purpose on which they would be applied - For the purpose of Commissioning: • Patients who are normally registered and/or resident within the NHS Herefordshire CCG, NHS Redditch and Bromsgrove CCG, NHS South Worcestershire CCG and NHS Wyre Forest CCG region (including historical activity where the patient was previously registered or resident in another commissioner). and/or • Patients treated by a provider where NHS Herefordshire CCG, NHS Redditch and Bromsgrove CCG, NHS South Worcestershire CCG and NHS Wyre Forest CCG are the host/co-ordinating commissioners and/or have the primary responsibility for the provider services in the local health economy – this is only for commissioning and relates to both national and local flows. and/or • Activity identified by the provider and recorded as such within national systems (such as SUS+) as for the attention of NHS Herefordshire CCG, NHS Redditch and Bromsgrove CCG, NHS South Worcestershire CCG and NHS Wyre Forest CCG - this is only for commissioning and relates to both national and local flows. LIMA Networks Ltd supply IT infrastructure and are therefore listed as a data processor. They supply support to the system, but do not access data. Therefore, any access to the data held under this agreement would be considered a breach of the agreement. This includes granting of access to the database[s] containing the data. 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) 12. Civil Registries Data ʹBirths and Deaths (CRD) Data quality management and pseudonymisation is completed within the DSCRO and is then disseminated as follows: Midlands and Lancashire 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 People͛s Health data (CYPHS), Community Services Data Set (CSDS). Diagnostic Imaging data (DIDS), National Cancer Waiting Times Monitoring Data Set (CWT), Civil Registries Data ʹBirths and Deaths (CRD), National Diabetes Audit (NDA) and Patient Reported Outcome Measures (PROMs) only is securely transferred from the DSCRO to Midlands and Lancashire Commissioning Support Unit. 2. Midlands and Lancashire Commissioning Support Unit will add derived fields, link data and provide analysis to: a. See patient journeys for pathways or service design, re-design and de-commissioning b. Check recorded activity against contracts or invoices and facilitate discussions with providers. c. Undertake population health management d. Undertake data quality and validation checks e. Thoroughly investigate the needs of the population f. Understand cohorts of residents who are at risk g. Conduct Health Needs Assessments 3. Allowed linkage is between the data sets contained within point 1. 4. Midlands and Lancashire Commissioning Support Unit will then pass the processed, pseudonymised and linked data to the CCG. 5. Aggregation of required data for CCG management use will be completed by Midlands & Lancashire Commissioning Support Unit 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 2 — DARS-NIC-234915-J3K4V

Opt outs honoured: No - data flow is not identifiable (Does not include the flow of confidential data)

Sensitive: Sensitive

When: 2020/02 — 2020/02.

Repeats: Frequent Adhoc Flow

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

Categories: Anonymised - ICO code compliant

Datasets:

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

Objectives:

Commissioning The Staffordshire CCG's have come together to improve health and care, currently they have moved to one senior management board and plan to formally join as one entity in 2020. The Six CCG's are as follows: NHS East Staffordshire CCG NHS Cannock Chase CCG NHS North Staffordshire CCG NHS South East Staffs & Seisdon Peninsula CCG NHS Stafford and Surrounds CCG NHS Stoke on Trent CCG 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 joint collaboration will be responsible for implementing large parts of the 5 year forward view from NHS England. The collaboration will be 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 the 6 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 collaboration 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's will work proactively and collaboratively to redesign services across boundaries to integrate services. Collaborative sharing is required for CCGs to understand these requirements. The CCGs 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 geographical area of Staffordshire. 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) - Community Services Data Set (CSDS) - Diagnostic Imaging Data Set (DIDS) - National Cancer Waiting Times Monitoring Data Set (CWT) - Civil Registration Births and Deaths Data (CRD) 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  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 Midlands and Lancashire Commissioning Support Unit.

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 15. Cost-benefit analysis and service impact assessments to underpin service transformation across health economy a. Service planning and re-design (development of NMoC and integrated care pathways, new partnerships, working with new providers etc.) b. Impact analysis for different models or productivity measures, efficiency and experience c. Service and pathway review d. Service utilisation review 16. Ensuring compliance with evidence and guidance a. Testing approaches with evidence and compliance with guidance. 17. Monitoring outcomes a. Analysis of variation in outcomes across population group 18. 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 19. 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 20. 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 21. 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 22. 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

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: 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 services to react to terror situations 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 New Models of Care (NMOC), Accountable Care Organisations (ACO), Sustainable Transformation Partnerships (STP) 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

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.   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   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.   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 Minimisation Data Minimisation in relation to the data sets listed within section 3 are listed below. This also includes the purpose on which they would be applied - • Patients who are normally registered and/or resident within NHS East Staffordshire CCG, NHS Cannock Chase CCG, NHS North Staffordshire CCG, NHS South East Staffs & Seisdon Peninsula CCG, NHS Stafford and Surrounds CCG and NHS Stoke on Trent CCG (including historical activity where the patient was previously registered or resident in another commissioner). and/or • Patients treated by a provider where NHS East Staffordshire CCG, NHS Cannock Chase CCG, NHS North Staffordshire CCG, NHS South East Staffs & Seisdon Peninsula CCG, NHS Stafford and Surrounds CCG and NHS Stoke on Trent CCG is the host/co-ordinating commissioner and/or has the primary responsibility for the provider services in the local health economy – this is only for commissioning and relates to both national and local flows. and/or • Activity identified by the provider and recorded as such within national systems (such as SUS+) as for the attention of NHS East Staffordshire CCG, NHS Cannock Chase CCG, NHS North Staffordshire CCG, NHS South East Staffs & Seisdon Peninsula CCG, NHS Stafford and Surrounds CCG and NHS Stoke on Trent CCG - this is only for commissioning and relates to both national and local flows. For clarity, any access by LIMA and Blackpool Teaching Hospitals to data held under this agreement would be considered a breach of the agreement. This includes granting of access to the database[s] containing the data. 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) 12. Civil Registries Data – Births and Deaths (CRD) Data quality management and pseudonymisation is completed within the DSCRO and is then disseminated as follows: Midlands and Lancashire 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), National Cancer Waiting Times Monitoring Data Set (CWT) and Civil Registries Data – Births and Deaths (CRD) only is securely transferred from the DSCRO to Midlands and Lancashire Commissioning Support Unit. 2. Midlands and Lancashire Commissioning Support Unit will add derived fields, link data and provide analysis to: a. See patient journeys for pathways or service design, re-design and de-commissioning b. Check recorded activity against contracts or invoices and facilitate discussions with providers. c. Undertake population health management d. Undertake data quality and validation checks e. Thoroughly investigate the needs of the population f. Understand cohorts of residents who are at risk g. Conduct Health Needs Assessments 3. Allowed linkage is between the data sets contained within point 1. 4. Midlands and Lancashire Commissioning Support Unit will then pass the processed, pseudonymised and linked data to the CCG. 5. Aggregation of required data for CCG management use will be completed by Midlands & Lancashire Commissioning Support Unit 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 3 — DARS-NIC-05206-L1V6D

Opt outs honoured: N, Y, No - data flow is not identifiable (Does not include the flow of confidential data)

Sensitive: Non Sensitive, and Sensitive

When: 2016/12 — 2019/08.

Repeats: One-Off

Legal basis: Health and Social Care Act 2012, Section 42(4) of the Statistics and Registration Service Act (2007) as amended by section 287 of the Health and Social Care Act (2012), Health and Social Care Act 2012 – s261(1) and s261(2)(b)(ii)

Categories: Anonymised - ICO code compliant, Identifiable

Datasets:

  • Diagnostic Imaging Dataset
  • Hospital Episode Statistics Outpatients
  • Hospital Episode Statistics Accident and Emergency
  • Office for National Statistics Mortality Data
  • Patient Reported Outcome Measures (Linkable to HES)
  • Hospital Episode Statistics Admitted Patient Care
  • Mental Health and Learning Disabilities Data Set
  • Hospital Episode Statistics Critical Care
  • Bridge file: Hospital Episode Statistics to Diagnostic Imaging Dataset
  • Bridge file: Hospital Episode Statistics to Mental Health Minimum Data Set
  • Bespoke Monthly Extract : SUS PbR A&E
  • Bespoke Monthly Extract : SUS PbR APC Episodes
  • Bespoke Monthly Extract : SUS PbR OP
  • Bespoke Monthly Extract : SUS PbR APC Spells
  • Bespoke Extract : SUS PbR A&E
  • Bespoke Extract : SUS PbR APC Episodes
  • Bespoke Extract : SUS PbR APC Spells
  • Bespoke Extract : SUS PbR OP
  • Bridge file: Hospital Episode Statistics to Mortality Data from the Office of National Statistics
  • Patient Reported Outcome Measures
  • HES:Civil Registration (Deaths) bridge
  • Civil Registration - Deaths
  • Mental Health Services Data Set
  • Mental Health Minimum Data Set

Objectives:

To support contractual and strategic benchmarking across Midlands and Lancashire, for programmes such as planning commissioning and productivity, service quality and performance improvement, and activity and outcomes monitoring for local populations. The CSU needs: • The provision of analytically based intelligence for a range of CCGs for benchmarking of similar health economies or populations in England, not just in the CSU’s area. • To provide in depth analysis of all aspects of a specific service areas and allow comparisons with other CCG areas or health economies known to have better outcomes or new/different pathways. • To support large scale transformation projects that may impact several commissioners (CCGs) • Descriptive analyses of healthcare needs, demands or supply including comparisons between providers, commissioners and geographical areas, analysis over time and of the characteristics of patients and the services they receive. • Retrospective analyses exploring the reasons for observed changes in healthcare provision and health outcomes • Prospective modelling of the impact of planned or proposed changes in healthcare services on healthcare activity, travel times and resource use • Quantitative evaluations and monitoring estimating the impact of service redesign of improvement initiatives on healthcare and outcomes • To develop tools and information packs to support patients, clinicians, commissioners and providers to make informed decisions about healthcare service provision, organisation and strategy The specific services and products that will utilise the data are the following :- A. QIPP opportunity packs which provide a summary of performance, cost and activity levels for individual CCGs/trusts compared to other local CCGs/trusts. The packs include aggregate analysis in relation to QIPP priorities covering Inpatient, Outpatient and A&E but are subject to change in line with the QIPP programme. These packs were originally produced for those CCGs within the CSU's core geography (Birmingham and the Black County). However the CSU have now been requested to provide packs for a wider range of CCGs and trusts including all Staffordshire, Lancashire, Herefordshire, Worcestershire, Shropshire and Telford and Wrekin. The CSU have also had requests from as far afield as Cornwall. The value of these packs (as demonstrated by the willingness to pay) in supporting CCGs/trusts to assist with their statutory duty to commission/provide high quality and best value services for their populations is clearly proven and as such the CSU will be offering the packs to all CCGs trusts in England. In addition to the wider provision of packs the CSU's existing customers have also requested that the packs be enhanced to offer comparisons against national nearest neighbour comparators or bespoke comparators (for example Birmingham combined CCGs compared with other large cities). Customers for the packs also can request ‘deep dive’ analyses to explore identified opportunities in greater detail B. Development of decision support tools for clinicians to help them make better decisions when deciding whether a patient is suitable for Hip or knee replacement procedures. The development of the tools requires sophisticated statistical analysis to establish the relationship between a range of patient characteristics and procedure outcomes (as measured by PROMs data). The statistical relationships will be used within the tools whereby it will allow a clinician to input patient characteristics and provide an estimate of the likely benefit of the procedure for the patient. This additional information can help both the patient and their clinician make the best informed decision about whether to proceed with the operation. In order to ensure that that relationship is as robust as possible and to maximise the predictive power of the tool (which is vital given that the tool will be used to support important decisions about patient care) a full national dataset is required. In order to further validate the relationship and establish its robustness over time (which will be important for clinician and patient confidence in the tool) the CSU will be carrying out the analysis on all data years. The development of these tools will establish a prototype for the development of other similar products for other procedures where data is available through the PROMS dataset such as Varicose vein surgery etc. However for the purposes of this request the CSU are requesting only PROMS data relating to hip and knee procedures. A number of Local CCGs with programmes aimed at improving orthopaedic services (across all of Staffordshire for example) have confirmed that they plan to put this tool into practice on an initial pilot basis as soon as it is available. The CSU have also been approached by a number of other CCGs who have indicated that they would also be interested in applying the tool once its efficacy has been established. C. Projects on behalf of CCGs and Strategic Clinical Networks (part of NHS England) to model expected future Mental Health activity levels and capacity requirements within a CCG after taking into account the impact of projected demographic changes and also the potential impact of mental health prevention strategies, admission avoidance strategies and length/intensity of treatment reduction strategies. An integral part of this work is to elicit modelling parameters from clinicians and commissioning stakeholders relating to expected impacts on activity levels as a result of planned changes or interventions. In order to do this the CSU produce a range of supporting analyses to help them to understand current activity levels, trends in activity and also how they compare with others. Provision of this supporting data is key to helping stakeholders to make considered and robust estimates based on a clear understanding of past progress and performance against other relevant comparators. In order to provide this comparative benchmarking the CSU require full national datasets covering multiple years. As the CSU are requesting the full set of historical data, they felt it important to clarify their rationale for doing so. In terms of the number of years of data requested, the CSU's professional experience has shown that providing longer term trends (in excess of 5 years) is often important, given the level of variation that exists, in order to evidence general trends. Being able to show local trends in the context of national trends is also essential for sophisticated interpretation. Shorter time series can often be misleading in this respect and as such could result in incorrect assumptions about future levels of demand. D. Projects on behalf of CCGs to understand how the nature and scale of healthcare utilisation changes as a result of changes in demographics. One specific aim of this work (for which ONS mortality data is required) is to investigate how patient need, demand and service utilisation changes towards the end of a person’s life. In addition it will also allow the CSU to develop a new approach to estimating the likely impact of an ageing population on future healthcare demand. The new approach will take into account not only the future size and age structure of a population but also changes in the proportion of the population who are estimated to be in their final months of life. It is also worth noting that NHS England have expressed interest in the CSU's development of this method of forecasting future demand as part of their national Fit for the Future programme (FFF). The project requires national level datasets in order that the analysis is as statistically robust as possible. It will also allow the CSU to establish the extent to which utilisation prior to death varies across the country. Benchmarking analysis (including historical trend analysis) will be carried out in order to provide estimates of the potential scale of opportunity for reducing acute healthcare activity (or developing alternatives to acute provision) for those patients at the end of life. Benchmarking and trend analysis will also enable the identification of those Trusts or CCGs who may be more advanced in end of life care provision. As part of this project the CSU will also be considering how patterns of utilisation at the end of life have changed over time (advances in medical technology and new treatments will certainly have had an impact on levels of service utilisation particularly for older people). Long term trends in excess of five years will be important in order to identify and have confidence in historical trends and applying these trends to future estimates. E. Other specific projects are: 1 Describing changes in acute utilisation over the long term provides insights that are lost when focusing on the most recent past. Striking reductions, for example, in casemix-adjusted length of stay following an emergency acute hospital admission or the frequency of admissions to psychiatric inpatient units only really become apparent when viewed over a long time frame. These longer term perspectives demonstrate the enormous positive changes that have been achieved in the past and can motivate and guide health economies seeking improvements in areas that seem equally intractable. To delete older data would eliminate the potential for these insights. The CSU have deployed this kind of longitudinal analysis (going back to pre 2000) recently in support of several Sustainability and Transformation Plans (STPs - compromising of CCGs, trusts, Foundation Trusts, Local Authorities and other key local partners) as they seek to address the requirements placed upon them nationally. 2 When explaining historical acute hospital utilisation rates, or forecasting future rates, the longer the time series, the more robust (on average) the explanation or forecast. Whilst for time series models, it might be argued the diminishing returns result from adding very old data points, this is not necessarily the case for causal models. 3 The CSU are frequently asked to model the potential implications of new models of care. These ‘new’ models are more commonly reinventions or adaptations of earlier models. The ‘NHS Five Year Forward View’ describes a number of new care models which move away from a purchaser-provider split in favour of lead-provider arrangements. To many these proposed models mirror or approximate arrangements that existed in the NHS prior to the development of primary care trusts. If analysed and interpreted appropriately, data relating to these earlier periods can provide useful insights into the unintended consequences of ‘new’ care models and the CSU are being asked to do this to support STPs and national Vanguards in meeting the national requirements placed upon them. Data will only be used for the purposes outlined above, and any requirement to change the purpose will be subject to a separate request to NHS Digital.

Yielded Benefits:

MLCSU's work is dedicated to helping commissioners, providers, charities, and government to solve complex problems by providing evidence-informed analysis and advice. This is carried out as better evidence leads to improved decision making and implementation. A. QIPP opportunity packs—these reports have facilitated commissioners to target interventions for reducing acute hospital activity. B. PROMS—this work has helped commissioners to determine whether rates of orthopaedic surgery are the result of differences in clinical thresholds for surgery. C. Mental Health activity modelling—the modelling in this area has provided powerful evidence of the need to integrate mental and physical health care, this is borne out by NHS England commissioning the CSU to produce reports for all 44 STPs. D: Impact of demography—this work has been used by commissioners in strategic plans that set the foundations for planning and contracting future levels of healthcare activity and expenditure. MLCSU suggests that that the value of its work is perhaps best judged based on feedback from its customers. "The Strategy Unit are inspiring in their commitment, dedication to evidence and use of innovative analysis as a way to improve health and care." Professor Sir Bruce Keogh—National Medical Director, NHS England "I just wanted to drop you a note to say how impressed I have been with the work you and the team have recently undertaken on the 'Making the Case for Integrating Mental and Physical Health Care' report. The product you provided was extremely well researched and presented. The delivery to the MH Alliance board generated significant discussion across the system. On a personal level, the presentation and report gave me a more informed narrative and evidence to help me further drive the West Midlands Mental Health Commission priorities whilst ensuring we adopt a whole population level health promotion approach. I would urge other areas to commission this report as a baseline assessment to develop a better understanding of the potential integration opportunities across STP and local footprints". Superintendent Sean Russell—Mental Health Lead, West Midlands Combined Authority "The Black Country STP has been an early adopter of this important study by the Strategy Unit. We saw its potential to inspire a transformation of our response to the physical health needs of mental health service users so we commissioned an earlier version to inform the development of our plan. Some of the differentials in both health outcomes and health service utilisation are chastening but we have been able to use these findings (and the summary of the evidence base provided) to begin building a broad coalition of local partners to identify and implement practical changes. I commend it enthusiastically to colleagues as a catalyst for much needed change". Andy Williams—Accountable Officer, Sandwell and West Birmingham CCG.

Expected Benefits:

As illustrated within the examples above, the work provides customers (CCGs, Trusts, Local Authorities for the purposes of public health and social care, CQC, Public Health England, Department of Health), Clinical senates, Strategic clinical networks, NHS England, Monitor, Trust Development Agency (TDA)), and health charities) with understanding and insight that enables them to make the best decisions about the healthcare services they commission or provide. Improved decision making will have a direct effect on the quality of care and outcomes for patients. The CSU's work does not go outside the health and social care arena and outcomes will only be used by health and social care organisations. In relation to each of the services stated above, the specific benefits are :- a. The QIPP opportunity packs provide estimates of the potential scale of opportunity if best performance levels of comparator CCGs/trusts are achieved. These packs will help CCGs/trusts to prioritise their QIPP programmes to maximise potential benefits. The Quality, Innovation, Productivity and Prevention (QIPP) programme is a large-scale programme developed by the Department of Health to drive forward quality improvements in NHS care, at the same time as making up to £20 billion of efficiency savings by 2014/15. b. The PROMS project will help both patients and clinician make the best decisions about whether to undergo major surgery. It will help to minimise both the financial cost of carrying out operations that are unlikely to have a beneficial impact for the patient and avoid painful and debilitating surgery for some patients who are unlikely to gain significant benefit. c. The modelling of future mental health demand will provide commissioners with detailed projections about the potential future demand for services. Understanding this is key to the effective functioning of commissioner organisations if they are to make sound decisions about changes to future service requirements to meet expected demand or to commission new targeted preventative services to mitigate against projected demand growth. d. The impact of demographics project will provide CCGs with valuable insight into how healthcare need changes not only as we get older but also as we approach the end of life. Given that healthcare utilization increases significantly at this time a clearer understanding of the patterns of utilization at this stage of life will enable commissioners to provide the most appropriate and cost effective services for these patients. The CSU are not the end user of the outputs they produce however they regularly receive positive feedback from their customer base and currently receive repeat custom from around 75% of customers. Regarding the CSU's customer base for the above projects: As mentioned above, with the introduction of STPs, the CSU's customer base has rapidly become a collective local ‘health and care economy’, comprising of a number of different organisation types within the NHS. For this reason, all the parties involved in STPs are referred to as customers . This is because they are now all party but also because, depending on how the STP operates, in some cases different programmes of work within the STP are split between constituents and so the CSU might actually be directly commissioned by any of them on behalf of the collective. Local authorities are often participants and therefore customers of facilitated modelling exercises that the CSU run to assist a health economies to estimate the demand and supply of health and social care services. Walsall MBC employees for example are currently participating in an exercise of this type in the Black Country. See purpose C. Public Health England have recently added the CSU to a framework agreement to supply data analytics and impact assessments. Work via the framework agreement will be issued over the next few months. The CSU anticipate that specifications for these projects will be published by PHE in the public domain. NHS England have recently commissioned the CSU to model the potential shifts in activity and the implications for accessibility that might arise form a potential reconfiguration of PPCI, vascular surgery and hyper acute stroke services across the West Midlands. This is in support of STPs across the region and therefore those who are party to STPs. Along with NHS England, NHS Improvement (previously Monitor and the Trust Development Agency) have responsibility for assessing and signing off Sustainability and Transformation Plans( STPs). The CSU have supported the Black Country, Staffordshire and Hereford and Worcestershire STP footprints to develop their STPs. NHS England and NHS Improvement are the ultimate customers for this work. In 2014/15, Cancer Research UK (CRUK) commissioned the CSU to model the future demand for endoscopy services in the UK. The CSU are in discussions with CRUK to test the feasibility of estimating the impact of cancer awareness campaigns on the identification of non-cancer diseases (e.g. do lung cancer awareness campaigns increase the diagnosis of COPD) and the subsequent impact on the demand for health services. That national work is now also being used to support individual STPs in addressing the future challenges they face in terms of endoscopy capacity.

Outputs:

QIPP opportunity packs (Objective A) – these packs contain a variety of comparative charts and tables plus bespoke ‘deep dive’ reports identifying the potential scale of opportunities available relating to areas of activity that may be amenable to common admission avoidance strategies. These packs are bespoke for an individual CCG/trust will be only be provided for CCGs/trusts who commission a pack. The CSU are continuing to produce these packs for a significant number of CCGs across the country. In 2015 the CSU provided around 30 CCGs with bespoke QIPP opportunity packs and have continued to further develop the product to offer comparisons with national nearest neighbours and bespoke comparator sets. In addition, the CSU has supported a number of CCGs to further explore issues highlighted by the packs by providing more in depth analysis using the data. The CSU anticipate a similar level of demand for this product in the coming months in order to support healthcare systems to develop their Sustainability and Transformation Plan (STP) plans. The packs are expected to be produced before the end of December 2016. PROMS decision support tool (Objective B) – the output of this project will be a web based tool that allows clinicians to input patient characteristics and receive an estimate of likely benefit of undergoing the procedure. The tool contains no patient data. There is currently no confirmed target date for the tool's completion, however it is still being developed and the CSU are keen to continue to refine and test the methodology with a view to piloting it with an interested partner CCG or other NHS organisation. Mental Health activity modelling (Objective C) -the output of this project will be a final written report containing charts, tables and written commentary. Aggregated summary data tables may also be provided and these will not be at patient level and small numbers will be suppressed. The report will be provided only to the project commissioner and will not be published or circulated. Mental Health intervention specific modelling -the output of this project will be a final written report containing charts, tables and written commentary. Aggregated summary data tables may also be provided and these will not be at patient level and small numbers will be suppressed. The report will be provided only to the project commissioner and will not be published or circulated. The CSU has carried out mental health modelling projects using the Mental Health dataset in Warwickshire and Shropshire which provided estimates of the future levels of demand for mental health services in the medium term. The CSU are due to commence work on new modelling projects for Walsall CCG and Sandwell and West Birmingham CCG imminently, with completion expected by end of February 2017. Impact of demography (Objective D) – the primary outputs of this work will be written reports containing appropriate charts, tables and written commentary. The report will be provided to the commissioning organisation and may be shared with other interested organisations with the permission of the commissioner. The secondary output will be in the form of an improved methodology to estimate the impact of population changes on healthcare utilisation. This methodology will be applied in future modelling projects but it would not require the re-use of the mortality data. The CSU have made progress in understanding how proximity to death may be applied to provide better estimates of future healthcare demand but a final methodology is not yet complete and this objective is still on-going. The CSU has carried out two projects looking at patterns of acute healthcare utilisation for patients in the 12 months prior to death. The first was a regional project commissioned by the West Midlands Strategic Clinical network which benchmarked patterns of acute healthcare utilisation for patients in the last 12 months of life. The second was a more in depth local analysis again using the ONS and HES data sets to further investigate seemingly high levels of utilisation in Dudley CCG and to provide the CCG with a more robust understanding of local provision of care to patients in their final year of life. Data presented within all outputs is aggregated with small numbers suppressed in line with the HES Analysis Guide.

Processing:

The data will be stored on a secure server and accessed through a SQL server database by a small group of named analytical staff working within the Strategy Unit of the CSU. Those staff are based at the premises detailed in this application (Kingston House). The data in its raw form will not be loaded into any tool or provided as part of any product or output. All outputs will contain only data which is aggregated, with small numbers suppressed in line with the HES Analysis Guide. SUS PBR As detailed in the “Objectives section” (Objective A) accessing the national SUS PBR data will enable the CSU to offer the QIPP packs to all CCGs/trusts in England as well as allowing the CSU to improve the packs through the use of better comparative groups (i.e. nearest neighbours). In producing these packs the data required is extracted using SQL server and analysed using MS Excel to produce the charts and tables included within the packs. PROMS As detailed in the “Objectives” section (Objective B) the PROMS data will be used to develop a decision support tool, PROMS data will be extracted from SQL server and analysed using appropriate statistical analysis software (STATA or R) in order to establish the relationship between a range of patient characteristics (e.g. age, gender, co-morbidities) and the procedure outcomes based on PROM scores. The tool that will be developed will not contain any patient data. The tool that will be provided to the customer(s) will only contain a mathematical algorithm based on the established statistical relationships between patient characteristics and outcomes. Mental Health Minimum Dataset (MHMDS) The MHMDS will be used to model expected future activity levels and capacity requirements within CCGs after taking into account the impact of projected demographic changes and also the potential impact of mental health prevention strategies, admission avoidance strategies and length/intensity of treatment reduction strategies. Patient level data is required to enable the CSU to adjust and remove activity in line with expected changes. Using patient level data also allows the CSU minimise the impact of overestimating impacts as a result of double counting which is not possible with aggregate data. As outlined in the “objective” section the data will be used in two ways firstly it will be used to provide supporting benchmarking and historical trend analyses to support modelling parameter setting. For this aspect of the project data extracts will be produced using SQL server and downloaded into MS Excel to produce the charts and tables required. Secondly it will be used to create a model to estimate future activity levels after accounting for changes in demographics and the impact of changes to service provision. The model will be constructed using SQL server to process the data applying any modelling factors and parameters. Aggregate output files from SQL server will be downloaded and analysed in MS Excel in order to produce the required charts and tables for inclusion in reports. The dataset will also be used to develop prospective intervention specific models to estimate changes in mental health team activity levels and the scale of potential savings as a result of the introduction of specific strategies to reduce the need for mental health services. These strategies may include, for example, schemes to increase early diagnosis of mental health conditions. This will help the CCG to better understand the costs and benefits of proposed changes allowing them to make better decisions about the effective use of commissioning resources. As with the higher level modelling in order to develop specific intervention impact models requires the production of benchmarking and trend analyses to help the customer to make judgments on the likely scale of impact of specific interventions. These judgments are incorporated into the model so it is important that they are based as far as possible on the best available data available. These prospective models will be constructed within SQL server and aggregate outputs downloaded into MS Excel to produce required outputs. The reports and any accompanying data tables will contain only data which is aggregated, with small numbers suppressed in line with the HES Analysis Guide. ONS mortality data As detailed in the “Objectives” section (Objective D) the ONS mortality data combined with the national HES data will be used to understand how the nature and scale of healthcare utilisation changes as a result of changes in demographics. It will also allow the CSU to develop a new approach to estimating the impact of an ageing population on future healthcare demand. As with the other datasets the ONS data will be stored within a SQL server database and the data required for this analysis will be extracted and analysed within MS Excel or other appropriate statistical software packages such as STATA or R in order to establish the mathematical relationship between proximity to death and healthcare utilisation which can be used in future (and potentially some of the current modelling work outlined in this document). During these data transfers into appropriate analysis software packages the data will not leave the secure environment. Any other projects that may make use of this work (for example the NHS England Fit For the Future programme) would only utilise the methodology derived from this project and would not use the actual ONS data. The reports and any accompanying data tables will contain only data which is aggregated, with small numbers suppressed in line with the HES Analysis Guide. Across all of the above processing, processing will be only carried out by CSU staff with the appropriate governance and access. The data will not be used to link at record level to other datasets (other than where already provided in linked or bridging form by NHS Digital). The data may however be linked to organisational level data such as already exists within the public domain. For clarity, the DSCRO may not process the data for the CSU other than initially downloading the data and storing it on the servers accessible by the CSU, and hence is not listed as a data processor, furthermore, NHS England have confirmed they are happy for this DSA to reflect the request for access to data to sit under the contract which results in NHSE having responsibility for the receipt, use, storage and any dissemination of the data by the CSU.