DSfC Cheshire CCG - STP - Comm

Text removed is struck through; text added is underlined. Unchanged paragraphs are summarised rather than repeated.

Data controllers: + CHESHIRE EAST COUNCIL; + CHESHIRE WEST AND CHESTER COUNCIL; + HALTON BOROUGH COUNCIL; + KNOWSLEY METROPOLITAN BOROUGH COUNCIL; + LIVERPOOL CITY COUNCIL; + SEFTON METROPOLITAN BOROUGH COUNCIL; + ST HELENS COUNCIL; + WARRINGTON BOROUGH COUNCIL; + WIRRAL BOROUGH COUNCIL

Datasets: + Adult Social Care; + Medicines dispensed in Primary Care (NHSBSA data)

Objective for processing

One of the key changes under the new Health and Social Care bill is the creation of 42 Integrated Care Systems (ICS) constituted of new legal entities which replace CCGs. As this agreement is coming into existence shortly prior to the expected date of this change, it is understood that it is likely there will need to be a new, closely related agreement put in place well before the end date stated here. [2 paragraphs unchanged] Sustainability and transformation partnerships build on collaborative work that began under the [6 words unchanged] – 2020/21, to support implementation of the Five Year Forward View. They are were supported by six national health and care bodies: NHS England; NHS Improvement; [11 words unchanged] England (PHE) and the National Institute for Health and Care Excellence (NICE). [8 paragraphs unchanged] To ensure the patient is at the heart of care, the STP [19 words unchanged] the right place for patients who may move and change services across CCGs. CCGs and local authorities. The CCGs and local authorities will work proactively and collaboratively with the other CCGs and local authorities in the STP to redesign services across boundaries to integrate services. Collaborative sharing is required for CCGs and local authorities to understand these requirements. The CCGs and local authorities will use pseudonymised data to provide intelligence to support the commissioning of [19 words unchanged] planned to support the needs of the population within the STP area. The CCGs and local authorities commission services from a range of providers covering a wide array of [5 words unchanged] flow categories requested supports the commissioned activity of one or more providers. The following CCGs and local authorities are Joint Data Controllers and will receive data for the area of residence and registration for the CCGs listed: [9 paragraphs unchanged] - Cheshire East Council - Cheshire West and Chester Council - Halton Borough Council - Knowsley Metropolitan Borough Council - St Helens Metropolitan Borough Council - Warrington Borough Council - Metropolitan Borough of Wirral - Liverpool City Council - Sefton Metropolitan Borough Council [30 paragraphs unchanged] - Medicines Dispensed in Primary Care (NHSBSA Data) - Adult Social Care Data Processing of the Medicines Dispensed in Primary Care (NHSBSA Data) dataset is only permitted to provide intelligence about the safety and effectiveness of medicines, as specified by the NHS Business Services Authority (NHSBSA) Medicines Data Directions 2019. [4 paragraphs unchanged] • Using value as the redesign principle [10 paragraphs unchanged] 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.  Provide intelligence about the safety and effectiveness of medicines. Processing for commissioning will be conducted by Arden and Greater East Midlands Commissioning Support Unit.  Allow analysis of patient pathways across healthcare and social care. 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 and local authority area based on the full analysis of multiple pseudonymised datasets. Processing for commissioning will be conducted by Arden and Greater East Midlands Commissioning Support Unit, Optum Health Solutions UK Limited and Graphnet Health Ltd. Optum Health Solutions The Optum Population Health Management national programme will identify patient cohorts and inequalities in outcome, spend and opportunity for further investigation, with a view to improving service delivery and patient health outcomes. Graphnet Health Ltd Graphnet will receive the outputs from Optum and provide further analysis and report access. Graphnet provide processing for the Data Controllers on DARS-NIC-396095-H1P1D-v2. Data provided under this application will be in a separate pseudonym.

Processing activities

[6 paragraphs unchanged] Onward Sharing 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. There is no requirement for the analytical teams to re-identify patients, but in the development of cohorts of patients considered to be at risk, the data controllers may need the facility to provide identifiable results back to direct healthcare professionals or local authority direct care staff only for the purpose of direct care. Additionally clinicians, made aware of a number of cases that they believe would need intervention may request re-identification for that direct care purpose. These instances of re-identification will generally be carried out as programmes of work or, rarely, on an individual/small group basis. All re-id requests will be processed and authorised by the DSCRO on a case by case basis. National data opt outs are not applied in these cases as they are for the purposes of direct care which follows the legal basis of implied consent. The following are typical (generic) examples of instances where a CCG might want to use the re-identification process: A&E High Attendance usage The CCG can filter data to show for example the number of A&E attendances in a given period for each patient. The CCG can then flag to the relevant GP of the patient any patients that require intervention. An outcome of this is earlier intervention in the patient(s) care thus potentially reducing future costs and minimising future risk. Polypharmacy re-IDs CCG's can request re-ID of a list of patients to be sent to the relevant GP with a high number of medications (ingredient count) and review the medication for these patients. This can help address the risk of polypharmacy which is recognised as an adverse risk factor for patient safety. A by-product of such reviews may be to reduce costs of medication. The Re-identification process for direct care is as follows: 1. The CCG identifies a patient cohort to be re-identified for the purpose of direct care. 2. The CCG sends a re-id request to the DSCRO. This may be done through the CCG or CSU’s Business Intelligence (BI) Tool, or through a manual form. 3. The DSCRO assesses as to whether the request passes the specified re-identification process checks. Checks include if the requester is authorised to access identifiable data, if the number of patients in the cohort is appropriate, and that the request does not seem inappropriate or outside of expected parameters, including for example around timings and the requestor’s relationship with patients in the data. These checks are carried out either by DSCRO staff using pre-approved information (timing’s, requester’s identity etc) or via an automated system. For automated systems, steps 1 -3 wouldn’t apply in most cases as it would be the direct care professional who identifies the cohort and as long as they are an approved re-id user and have gone through security checks initially, they will be able to re-id without more further checks. 4. If successful/approved, the DSCRO re-identifies the relevant data item(s) for the appropriate patients and returns the identifiable fields to Health or care professional(s) with a legitimate relationship to the patient. The CCG does not see the identifiable record. 5. DSCROs retain an audit trail of all re-id requests 6. National Data opt outs are not applied for the purpose of direct care [16 paragraphs unchanged] Midlands and Lancashire Commissioning Support Unit and Greater Manchester Shared Services (hosted by Salford Royal NHS Oldham CCG) Foundation Trust) supply IT infrastructure for Arden and GEM Commissioning Support Unit and are [33 words unchanged] agreement. This includes granting of access to the database[s] containing the data. Microsoft Limited provide Cloud Services for Graphnet Health Ltd and NHS Arden and GEM Commissioning Support Unit and are therefore listed as [30 words unchanged] the agreement. This includes granting of access to the database[s] containing the data data. Amazon Web Services and Microsoft Limited provide Cloud Services for Optum Health Solutions Limited 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. [34 paragraphs unchanged] 11. National Cancer Waiting Times Monitoring Data Set (CWT) 12. Civil Registries Data (CRD) (Births and Deaths) (Births) 13. Patient Reported Outcome Measures (PROMS) 13. Civil Registries Data (CRD) (Deaths) [1 paragraph unchanged] 15. e-Referral Service (eRS) 15. Patient Reported Outcome Measures (PROMs) 16. Personal Demographics e-Referral Service (PDS) (eRS) 17. Summary Hospital-level Mortality Indicator (SHMI) 17. Personal Demographics Service (PDS) 18. Summary Hospital-level Mortality Indicator (SHMI) 19. Medicines Dispensed in Primary Care (NHSBSA Data) 20. Adult Social Care Data [2 paragraphs unchanged] 1. Pseudonymised SUS+, Local Provider data, Mental Health data (MHSDS, MHMDS, MHLDDS), [40 words unchanged] Measures (PROMS) National Diabetes Audit (NDA), e-Referral Service (eRS), Personal Demographics Service (PDS) and (PDS), Summary Hospital-level Mortality Indicator (SHMI) (SHMI), Medicines Dispensed in Primary Care (BSA Data) and Adult Social Care data only is securely transferred from the DSCRO to Arden and Greater East Midlands Commissioning Support Unit. 2. Arden and Greater East Midlands Commissioning Support Unit add derived fields, link data also receive GP, Social Care, Mental Health, Acute and provide analysis to: Community Data (see points i - iii below) 3. Arden and Greater East Midlands Commissioning Support Unit add derived fields, link data and provide analysis to: [7 paragraphs unchanged] 3. 4. Allowed linkage is between the data sets contained within point 1. 1 and 2. 4. 5. Arden and Greater East Midlands Commissioning Support Unit then pass the processed, pseudonymised and linked data to the CCGs. 5. Aggregation of required data for CCG management use will be completed by Arden and Greater East Midlands Commissioning Support Unit or the CCGs as instructed by the CCGs. 6. Patient level data will not be shared outside of the Data Controllers and will only be shared 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. 6. Patient level data will not be shared outside of the CCGs and will only be shared within the CCGs 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. GP, Social Care, Mental Health, Acute and Community Data i. Providers pseudonymise the data with a DSCRO issued local key (different to the one used by the DSCRO) ii. The pseudonymised data is then transferred to Arden and GEM Commissioning Support Unit iii. The DSCRO provides Arden and GEM Commissioning Support Unit with a mapping table to convert the local pseudo into the DSCRO pseudo Data Processor 2 - Optum Health Solutions UK Limited 1) Pseudonymised SUS+, Local Provider data, Mental Health data, Community Services Data Set (CSDS), GP Data, Community Data, Acute data and Social Care data is securely transferred from Arden and Greater East Midlands Commissioning Support Unit to Optum Health Solutions UK Limited. 2) Optum Health Solutions (UK) Ltd add derived fields, link SUS fields and provide analysis to: • Whole population segmentation to assess population health needs • Prospective risk scoring for individuals to indicate the likelihood of future adverse events • Predictive modelling to determine individuals at risk and an understanding of the drivers of risk • Longitudinal analysis of intersegmental drift - identifying individuals who move between complexity classifications and the drivers of these transitions • The production of individual-level theographs to identify gaps in care • Actuarial modelling to understand unmitigated and mitigated system-level activity and cost historically and in the future 3) Allowed linkage is between the data sets contained within point 1. 4) Optum Health Solutions (UK) Ltd then pass the processed, pseudonymised and linked data to the Data Controllers. 5) Patient level data will not be shared outside of the Data Controllers and will only be shared 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 - Graphnet Health Ltd 1) Pseudonymised SUS+, Local Provider data, Mental Health data, Community Services Data Set (CSDS), GP Data, Community Data, Acute data and Social Care data is securely transferred from Optum Health Solutions UK Limited to Graphnet Health Ltd 2) Graphnet provide further analysis and use existing channels to present the data back to the Data Controllers. 3) Patient level data will not be shared outside of the Data Controllers and will only be shared within 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.

Expected output

[45 paragraphs unchanged] 25. Investigate mortality outcomes for trusts trusts. 26. Identify medication prescribing trends and their effectiveness. 27. Linking prescribing habits to entry points into the health and social care system 28. Identify, quantify and understand cohorts of patient’s high numbers of different medications (polypharmacy) 29. Monitoring, at a population level, particular cohorts of service users and designing analytical models which support more effective interventions in health and adult social care 30. Monitoring service and integrated care outcomes across a pathway or care setting involving adult social care

Expected measurable benefits

[39 paragraphs unchanged] 23. Monitoring of entire population, as a pose opposed to only those that engage with services [3 paragraphs unchanged] 27. Understand admissions linked to overprescribing. 28. Add value to the population health management workstream by adding prescribing data into linked dataset for segmentation and stratification. 29. Developing, through evaluation of person-level data, more effective prevention strategies and interventions across a pathway or care setting involving adult social care 30. Designing and implementing new payment models across health and adult social care 31. Understanding current and future population needs and resource utilisation for local strategic planning and commissioning purposes including for health, social care and public health needs.

Benefits reported

Supporting STP work across the North West region Liverpool CCG 1. EROC (Elective Recovery Outpatient Collection) is a new national data collection. where Integrated care systems' (ICS) are asked to either submit data from eRS and other sources, or instruct NHS E &I to access eRS directly. Liverpool CCG's ICS lack the maturity to carry this out themselves so have asked CCGs to collate this on their behalf. Liverpool CCG could not do this without access to eRS. This assures NHS England & Improvement that local delivery of elective restoration plan align with the strategic direction of the NHS. In this specific circumstance, that the people of Liverpool can access planned care outpatient appointments in a safe, timely and medium-appropriate manner. Without this data, commissioners would be unsighted on levels of referrals including ‘Advice & Guidance’, at an overall and an underlying practice level. The CCG would therefore be slow to identify outlier practices with abnormally low or high referrals, either of which could have a negative impact on patients accessing care in a timely fashion. 2. The new 2-hour crisis response target. Liverpool CCG are tasked by NHS England and Improvement with a). planning trajectories, b). monitoring performance, and c). improving provider completion of such data. Having access to CSDS (Community Services Data Set) allows Liverpool CCG to answer and address the 3 tasks. This assures NHS England & Improvement that local planning and delivery of community crisis services align with the strategic direction of the NHS. In this specific circumstance, that the people of Liverpool can access community crisis response services within 2 hours of need. Access to this data at a service level enables Liverpool CCG to identify poorly performing services, and commence discussion with those providers as to how to improve the service. Without this data, commissioners would be unsighted on the volume of referrals and adherence to the 2-hour target. The CCG would therefore be slow to identify poorly performing services, which could have a negative impact on patients accessing care the care they urgently need. 3. Ethnicity Coding. There has long been direction from NHS E & I and the DHSC to improve Ethnicity Coding within SUS and other NHS Digital datasets, this was re-emphasised early on in the pandemic when it became evident that certain ethnic groups were experiencing greater hospitalisation rates and poorer outcomes. NHS E & I wrote to CCGs asking them to work ensure Liverpool CCG's providers are coding ethnicity widely and accurately. Having access to commissioning data enables the CCG to work with their providers to ensure ethnicity coding is populated and accurate. In Liverpool, as it is for much of the country, people from black and minority ethnic populations experience poorer health outcomes than the general population. CCGs need access to robust ethnicity data in order to: a) Identify the inequality gaps b) Ensure that the planning of services incorporates an Equality Impact Assessment that addresses equality of access for BME populations The CCGs has recently published their annual reports for 2020/21 of which highlights the achievements made during the year, of which some would only have been achieved by using the data from NHS Digital. - Liverpool CCG report is available at https://www.liverpoolccg.nhs.uk/media/5112/annual-report-accounts-2020-21-final.pdf. This report includes several case studies (page 62) for which would have used data from NHS Digital and further detailed below. Cheshire CCG's annual report- Includes benefits from page 12-36. https://www.cheshireccg.nhs.uk/media/2438/nhs-cheshire-ccg-annual-report-and-accounts-2020_21-final.pdf Warrington CCG's Annual Report- Benefits page 8 onwards.https://www.haltonwarringtonccg.nhs.uk/about-us/policies-and-procedures/warrington-policies/annual-reports-1/1621-nhs-warrington-ccg-annual-report-2020-2021/file Knowsley CCG's Annual Report- Benefits page 9-22 https://www.knowsleyccg.nhs.uk/assets/uploaded/documents/29035_01J_CCG_Annual_Report_2020-21_FINAL.pdf South Sefton CCG's Annual Report- Benefits page 15 https://www.southseftonccg.nhs.uk/media/4888/auditors-annual-report-nhs-south-sefton-ccg.pdf Southport and Formby CCG's Annual Report- Benefits page 15 and 16 https://www.southportandformbyccg.nhs.uk/media/4674/auditors-annual-report-nhs-southport-and-formby-ccg.pdf St Helen's CCG's Annual Report- Benefits page 13 to 53 https://www.sthelensccg.nhs.uk/media/4580/annual-report-2020-21-v16-final-website-without-signatures-v3.pdf Wirral CCG's Annual Report- Benefits page 10 to 26 https://www.wirralccg.nhs.uk/media/8849/12f_ccg_annual_report_2020-21_final-no-sig.pdf The following are cases studies extracted from Liverpool CCG (as the lead applicant for this request) with an explanation on how NHS Digital data has benefited; https://www.liverpoolccg.nhs.uk/media/5112/annual-report-accounts-2020-21-final.pdf Page 63 of the above annual report details a case study - Helping to prevent Type 2 diabetes. This looks at Diabetes related activity in Liverpool that takes place within the ‘Liverpool Diabetes Partnership’. NHS Digital data (mainly APC SUS) is a vital tool in planning and monitoring this service. Admitted Patient Care SUS data is key to monitoring 6 of the 12 key diabetes outcomes, which help the CCG (and partners) tell whether existing services are working. - Reduction in serious episodes of hypoglycaemia - Reduction in serious episodes of Ketoacidosis - Reduction in proportion of people with diabetes having a heart attack - Reduction in proportion of people with diabetes having a stroke - Reduction in proportion of people with diabetes having a Transient Ischaemic Attack (TIA) - Reduction in proportion of people with diabetes undergoing amputations SUS is also key in providing data for modelling work that forms part of service redesign. The ability to link the CCG’s primary care data to admitted patient care data is vital in providing a picture of patient flows across the CCG’s complex health economy. Page 64 of the above annual report details a case study - Telehealth service expanded The CCG use the risk stratification score to help identify patients and to describe the cohort using Telehealth. The CCG also use all core SUS Datasets (AAE, OPA, APC) to calculate the average cost of individuals not using Telehealth in order to calculate potential savings of Telehealth. The CCG also use SUS to monitor emergency admissions (or lack thereof) following Telehealth as part of service evaluation. Page 65 of the above annual report details a case study - Helping to support COVID-19 vaccine roll out. Although most initial vaccination data requirements were met by local primary care data and NHS Digital data provided under COPI, NHS Digital data for commissioning purposes has been useful. In particular the CCG are often asked to show the proportion of recent inpatients who have/haven’t been vaccinated. Further information about other achievements and future priorities can be found within the reports across all CCGs listed under this Agreement.

Text removed is struck through; text added is underlined. Unchanged paragraphs are summarised rather than repeated.

Datasets: + Personal Demographic Service; + Summary Hospital-level Mortality Indicator (SHMI); + e-Referral Service for Commissioning

Objective for processing

[52 paragraphs unchanged] - e-Referral Service (eRS) - Personal Demographics Service (PDS) - Summary Hospital-level Mortality Indicator (SHMI) [12 paragraphs unchanged]  Patient stratification and predictive modelling - to highlight cohorts of patients at risk of requiring hospital admission and other avoidable factors such [7 words unchanged] executed against linked de-identified data, and identification of future service delivery models  Demand Management - to improve the care service for patients by predicting the impact on certain care pathways and support the secondary care system in ensuring enough capacity to manage the demand.  Support measuring the health, mortality or care needs of the total local population [2 paragraphs unchanged]

Processing activities

[19 paragraphs unchanged] • Activity identified by the provider and recorded as such within national [13 words unchanged] is only for commissioning and relates to both national and local flows. This includes data that was previously under a different organisation name but has now merged into this CCG - This includes data that was previously under a different organisation name but has now merged into this CCG [4 paragraphs unchanged] Microsoft Limited provide Cloud Services for NHS Arden and GEM Commissioning Support Unit 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 [4 paragraphs unchanged] St Helens & Knowsley Hospital NHS Trust 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.. This storage address is used by the following CCG's St Helens & Knowsley Hospital NHS Trust supply IT infrastructure to the following CCG's [3 paragraphs unchanged] - St Helen's and Knowsley NHS Foundation Trust also provide staff resource to St Helens CCG so are required to access the data [29 paragraphs unchanged] 15. e-Referral Service (eRS) 16. Personal Demographics Service (PDS) 17. Summary Hospital-level Mortality Indicator (SHMI) [2 paragraphs unchanged] 1. Pseudonymised SUS+, Local Provider data, Mental Health data (MHSDS, MHMDS, MHLDDS), [30 words unchanged] (DIDS), Civil Registration Data (CRD)[Births and Deaths], Patient Reported Outcome Measures (PROMS) and National Diabetes Audit (NDA) (NDA), e-Referral Service (eRS), Personal Demographics Service (PDS) and Summary Hospital-level Mortality Indicator (SHMI) data only is securely transferred from the DSCRO to Arden and Greater East Midlands Commissioning Support Unit. [12 paragraphs unchanged]

Expected output

Commissioning [23 paragraphs unchanged] a. o Patients at highest risk of admission b. o High Cost Activity Uses cost activity uses (top 15%) c. o Frail and elderly d. o Patients that are currently in hospital e. o Patients with most referrals to secondary care f. o Patients with most emergency activity g. o Patients with most expensive prescriptions h. o Patients recently moving from one care setting to another [2 paragraphs unchanged] 13. Identifying and managing preventable and existing conditions 13. Validation for payment approval, ability to validate that claims are not being made after an individual has died, like Oxygen services. a. Identifying types of individuals and population cohorts at risk of non-elective re-admission 14. Validation of programs implemented to improve patient pathway e.g. High users unable to validate if the process to help patients find the best support are working or did the patient die. b. Risk stratification to identify populations suitable for case management 15. Clinical - understand reasons why patients are dying, what additional support services can be put in to support. c. Risk profiling and predictive modelling 16. Understanding where patient are dying e.g. are patients dying at hospitals due to hospices closing due to Local authorities withdrawing support, or is there a problem at a particular trust. d. Risk stratification for planning services for population cohorts 17. Removal of patients from Risk Stratification reports. e. Identification of disease incidence and diagnosis stratification 18. Re births provide a one stop shop of information, Births are recorded in multiple sources covering hospital and home births, a chance to overlook activity. 14. Reducing health inequalities 19. Manage demand, by understanding the quantity of assessments required CCGs are able to improve the care service for patients by predicting the impact on certain care pathways and ensure the secondary care system has enough capacity to manage the demand. 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 20. Monitor the timing of key actions relating to referral letters. CCG’s are unable to see the contents of the referral letters. b. Socio-demographic analysis 21. Identify low priority procedures which could be directed to community-based alternatives and as such commission these services and deflect referrals for low priority procedures resulting in a reduction in hospital referrals. 15. Managing demand 22. Allow Commissioners to better protect or improve the public health of the total local patient population a. Waiting times analysis 23. Allow Commissioners to plan, evaluate and monitor health and social care policies, services, or interventions for the total local patient population b. Service demand and supply modelling 24. Allow Commissioners to compare their providers (trusts) mortality outcomes to the national baseline. c. Understanding cross-border and overseas visitor 25. Investigate mortality outcomes for trusts d. Winter planning e. Emergency preparedness, business continuity, recovery and contingency planning 16. Care co-ordination and planning a. Planning packages of care b. Service planning c. Planning care co-ordination 17. 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, Accountable Care Organisations and Sustainable Transformation Partnerships i. Identifying duplications in care j. Identifying gaps in care, missed diagnoses and triple fail events k. Analysing individual and aggregated timelines 18. 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 19. 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 20. 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 21. Comparing expected levels a. Standardised comparisons for prevalence, activity, cost, quality, experience, outcomes for given populations 22. 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 23. Monitoring activity and cost compliance against contract and agreed plans a. Contract monitoring b. Contract reconciliation and challenge c. Invoice validation 24. 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 25. Improving provider data quality a. Coding audit b. Data quality validation and review c. Checking validity of patient identity and commissioner assignment 26. Validation for payment approval, ability to validate that claims are not being made after an individual has died, like Oxygen services. 27. Validation of programs implemented to improve patient pathway e.g. High users unable to validate if the process to help patients find the best support are working or did the patient die. 28. Clinical - understand reasons why patients are dying, what additional support services can be put in to support. 29. Understanding where patients are dying e.g. are patients dying at hospitals due to hospices closing due to Local authorities withdrawing support, or is there a problem at a particular trust. 30. Removal of patients from Risk Stratification reports. 31. Re births provide a one stop shop of information, Births are recorded in multiple sources covering hospital and home births, a chance to overlook activity.

Expected measurable benefits

[15 paragraphs unchanged] c. Successful delivery of integrated care within the STP. CCG. [10 paragraphs unchanged] 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 CCGs CCG Outcome Framework. [3 paragraphs unchanged] 14. Insights into patient outcomes, and identification of the possible efficacy of outcomes-based contracting opportunities. 14. Providing greater understanding of the underlying courses and look to commission improved supportive networks, this would be ongoing work which would be continually assessed. 15. Reviewing current service provision 15. Insight to understand the numerous factors that play a role in the outcome for both datasets. The linkage will allow the reporting both prior to, during and after the activity, to provide greater assurance on predictive outcomes and delivery of best practice. a. Cost-benefit analysis and service impact assessments to underpin service transformation across health economy 16. Provision of indicators of health problems, and patterns of risk within the commissioning region. b. Service planning and re-design (development of NMoC and integrated care pathways, new partnerships, working with new providers etc.) 17. Support of benchmarking for evaluating progress in future years. c. Impact analysis for different models or productivity measures, efficiency and experience 18. Allow reporting to drive changes and improve the quality of commissioned services and health outcomes for people. d. Service and pathway review 19. Assists commissioners to make better decisions to support patients and drive changes in health care e. Service utilisation review 20. Allows comparisons of providers performance to assist improvement in services – increase the quality 16. Ensuring compliance with evidence and guidance 21. Allow analysis of health care provision to 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. a. Testing approaches with evidence and compliance with guidance. 22. To evaluate the impact of new services and innovations (e.g. if commissioners implement a new service or type of procedure with a provider, they can evaluate whether it improves outcomes for patients compared to the previous one). 17. Monitoring outcomes 23. Monitoring of entire population, as a pose to only those that engage with services a. Analysis of variation in outcomes across population group 24. Enable Commissioners to be able to see early indications of potential practice resilience issues in that an early warning marker can often be a trend of patients re-registering themselves at a neighbouring practice. 18. Understanding how services impact across the health economy 25. Monitor the quality and safety of the delivery of healthcare services. a. Service evaluation 26. Allow focused commissioning support based on factual data rather than assumed and projected sources 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. Ensuring inclusion of required elements in future contracts 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 23. Providing greater understanding of the underlying courses and look to commission improved supportive networks, this would be ongoing work which would be continually assessed. 24. Insight to understand the numerous factors that play a role in the outcome for both datasets. The linkage will allow the reporting both prior to, during and after the activity, to provide greater assurance on predictive outcomes and delivery of best practice. 25. Provision of indicators of health problems, and patterns of risk within the commissioning region. 26. Support of benchmarking for evaluating progress in future years.

Unchanged: Benefits reported.

Text removed is struck through; text added is underlined. Unchanged paragraphs are summarised rather than repeated.

Datasets: + National Diabetes Audit; + Patient Reported Outcome Measures (PROMs)

Objective for processing

[16 paragraphs unchanged] - NHS Eastern Cheshire CCG [3 paragraphs unchanged] - NHS South Cheshire CCG [3 paragraphs unchanged] - NHS Vale Royal CCG [1 paragraph unchanged] - NHS West Cheshire CCG [26 paragraphs unchanged] - Patient Reported Outcome Measures (PROMS) - National Diabetes Audit (NDA) [15 paragraphs unchanged]

Processing activities

[19 paragraphs unchanged] • Activity identified by the provider and recorded as such within national [13 words unchanged] is only for commissioning and relates to both national and local flows. This includes data that was previously under a different organisation name but has now merged into this CCG In addition to the dissemination of Cancer Waiting Times Data via the DSCRO, the CCG is able to access reports held within the CWT system in NHS Digital directly. Access within the CCG is limited to those with a need to process the data for the purposes described in this agreement. A CCG user will be able to access the provider extracts from the portal for any provider where at least 1 patient for whom they are the registered CCG for that individuals GP practice appears in that setting Although a CCG user may have access to pseudonymised patient information not related to that CCG, users should only process and analyse data for which they have a legitimate relationship (as described within Data Minimisation). [5 paragraphs unchanged] Ilkeston Community Hospital, Eagle Bridge Health Centre St Helens & Knowsley Hospital NHS Trust supply IT infrastructure and Macclesfield District General Hospital, are therefore listed as a data processor. They supply support to the system, but do not access data held under this agreement as they only supply the building. 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. data.. This storage address is used by the following CCG's Local Identifiers: - Knowsley CCG If a Data Controller organisation (or the Data Processor working on their behalf): - Halton CCG a. only receives a DSCRO disseminated identifiable (NHS Number) flow, then it can receive clear local identifiers. - Warrington CCG b. receives and pseudonymised flow, then clear local identifiers can be included and used only for the purpose outlined within the Data Sharing Agreement Ilkeston Community Hospital (Part of Derbyshire Community Health Services NHS Foundation Trust), Wrightington, Wigan and Leigh NHS Foundation Trust, Eagle Bridge Health Centre and Macclesfield District General Hospital, do not access data held under this agreement as they only supply the building. 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. c. receives both DSCRO disseminated identifiable and pseudonymised flows, the identifiable flow must have the local identifiers pseudonymised or removed. [26 paragraphs unchanged] 13. Patient Reported Outcome Measures (PROMS) 14. National Diabetes Audit (NDA) [2 paragraphs unchanged] 1. Pseudonymised SUS+, Local Provider data, Mental Health data (MHSDS, MHMDS, MHLDDS), [18 words unchanged] Services Data Set (CSDS), National Cancer Waiting Times (CWT), Diagnostic Imaging data (DIDS) and (DIDS), Civil Registration Data (CRD) (CRD)[Births and Deaths], Patient Reported Outcome Measures (PROMS) and National Diabetes Audit (NDA) only is securely transferred from the DSCRO to Arden and Greater East Midlands Commissioning Support Unit. [12 paragraphs unchanged]

Expected output

[19 paragraphs unchanged] 8. GP Practice level dashboard reports include high flyers. reports. [5 paragraphs unchanged] b. Most expensive patients High Cost Activity Uses (top 15%) [83 paragraphs unchanged]

Unchanged: Expected measurable benefits, Benefits reported.