DSfC - NHS Bolton CCG - STP - GM Cancer - Comm

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

Datasets: + Adult Social Care; + Medicines dispensed in Primary Care (NHSBSA data); + Personal Demographic Service; + Summary Hospital-level Mortality Indicator (SHMI)

Objective for processing

[7 paragraphs unchanged] - Planning the demand and capacity across the healthcare system across 10 11 CCGs to ensure we have the right buildings, services and staff to cope with demand whilst reducing the impact on costs [8 paragraphs unchanged] - NHS Cheshire CCG [39 paragraphs unchanged] - Personal Demographics Service (PDS) - Summary Hospital-level Mortality Indicator (SHMI) - 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 [9 paragraphs unchanged]  Support measuring the health, mortality or care needs of the total local population.  Provide intelligence about the safety and effectiveness of medicines.  Allow analysis of patient pathways across healthcare and social care [3 paragraphs unchanged] - Oldham CCG Northern Care Alliance NHS Foundation Trust (hosting Greater Manchester Shared Services) [1 paragraph unchanged] - Manchester CCG and NHS England (Hosting Greater Manchester Health and Social Care Partnership) AGEM CSU – focus of work is ‘data management’, so preparing data, managing datasets and data assets ready for consumption by DC (and other DPs). This includes adding further derived fields, adding value to the data in terms of content and preparation (eg structure and format of data). AGEM CSU typically give access through data access portals or direct transmission methods only. Access by CCGs is more about access to data ready for production of reports and further analysis, than performing the analysis. - University of Manchester NHS Arden and GEM Commissioning Support Unit – focus of work is ‘data management’, so preparing data, managing datasets and data assets ready for consumption by DC (and other DPs). This includes adding further derived fields, adding value to the data in terms of content and preparation (eg structure and format of data). AGEM CSU typically give access through data access portals or direct transmission methods only. Access by CCGs is more about access to data ready for production of reports and further analysis, than performing the analysis. [1 paragraph unchanged] Oldham CCG Northern Care Alliance NHS Foundation Trust (hosting Greater Manchester Shared Services) – are typically commissioned by the CCGs [88 words unchanged] and Social Care Partnership (GMHSCP) for dissemination to CCGs through their BI-tool. Manchester University NHS Foundation Trust (Hosting Health Innovation Manchester Utilisation Management Team) – their focus is around access to and utilisation of services, across all specialties. Pieces of work are focussed around this remit, and are typically deep dives into data, with detailed analysis using the teams specific knowledge and expertise to produce reports and analyses for consumption by the CCGs. Typically reports are aggregate and supplied as standalone reports, but could also be disseminated via the GMHSCP BI Tool. GMHSCP and GMSS do not have the specific and detailed knowledge to produce these reports. Manchester University NHS Foundation Trust (Hosting Health Innovation Manchester Utilisation Management Team [UM Team]) – their focus is around access to and utilisation of services, across all specialties. Pieces of work are focussed around this remit, and are typically deep dives into data, with detailed analysis using the teams specific knowledge and expertise to produce reports and analyses for consumption by the CCGs. For example, the UM Team has recently been commissioned by the CCGS to conduct a mortality review of deaths within the Salford CCG area. The UM Team will carry out an assessment of the key mortality themes included within records of death to investigate and identify modifiable or preventable factors that contributing to the death of patients registered in the area. Typically reports are aggregate and supplied as standalone reports, but could also be disseminated via the GMHSCP BI Tool. GMHSCP and GMSS do not have the specific and detailed knowledge to produce these reports. [1 paragraph unchanged] Devolution has put the Greater Manchester (GM) region in charge of improving the health and wellbeing of everyone who lives there. The ten eleven boroughs/CCGs are working together with GMHSCP to transform public services and tackle [42 words unchanged] more control over it's own budget for health and social care services [5 paragraphs unchanged] Greater Manchester Health and Social Care Partnership (GMHSCP) GMHSCP is hosted jointly by NHS England and Manchester CCG. There is a small team accessing and processing data on behalf of the 10 Greater Manchester CCGs and Cheshire CCG . GMHSCP is hosted jointly by NHS England and Manchester CCG. There is a small team accessing and processing data on behalf of the 10 Greater Manchester CCGs, and these are all employed and hosted by Manchester CCG. [2 paragraphs unchanged] Snowflake Computing U.K. Limited Snowflake host a tool used by Greater Manchester Health and Social Care Partnership for dataset landing and structuring. This uses Google Cloud processing. University of Manchester The University of Manchester (UoM) plays a significant role in health-related research in the UK. In addition, they routinely work with partners across the Greater Manchester system to develop intelligence, insight and modelling to support operational insights to support the commissioning and planning of services, with the ultimate aim of improving patient care and outcomes. There is a requirement for daily analysis of intervention impact, identification of at-risk populations and to link that to both service provision and reducing inequalities. UoM have unique internationally recognised expertise in this area, and they are running complex models which the Greater Manchester (GM) CCGs want to utilise across the GM region. There is a small team accessing and processing data on behalf of the 10 Greater Manchester CCGs, and these are all employed by University of Manchester. Data is processed and stored within AGEM Azure infrastructure, with data not being moved externally, with results being presented within that infrastructure. UoM also create extra value by for example creating models which are not single use but can become routinely used within the existing Greater Manchester infrastructure. [13 paragraphs unchanged] GM Cancer provides services across the CCGs in the Greater Manchester region and relevant cancer service providers; The following pseudonymised datasets are required to provide intelligence to support commissioning of GM Cancer services: - NHS Bolton CCG - NHS Bury CCG - NHS Cheshire CCG - NHS Heywood, Middleton and Rochdale CCG - NHS Oldham CCG - NHS Salford CCG - NHS Manchester CCG - NHS Stockport CCG - NHS Tameside and Glossop CCG - NHS Trafford CCG - NHS Wigan Borough CCG To use pseudonymised data to provide intelligence to support the commissioning of health services. The data (containing both clinical and financial information) is analysed so that health care provision can be planned to support the needs of the population within the CCG area. The CCGs commission services from a range of providers covering a wide array of services. Each of the data flow categories requested supports the commissioned activity of one or more providers. The following pseudonymised datasets are required to provide intelligence to support commissioning of health services: [4 paragraphs unchanged] 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. [1 paragraph unchanged] Processing for commissioning will be conducted by NHS Arden and Greater East [6 words unchanged] NHS Foundation Trust [hosting the GM Cancer intelligence Service] and Manchester CCG & NHS England (Hosting Greater Manchester Health and Social Care Partnership)

Processing activities

[8 paragraphs unchanged] 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. 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. An example of a request for the re-id of patients for direct care may be; 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 (typically small numbers) 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 (either through an automated system or manual checking in line with the request) 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 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 [5 paragraphs unchanged] 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 Bolton CCG, NHS Bury CCG, Cheshire CCG, NHS Heywood, Middleton and Rochdale CCG, NHS Manchester CCG, NHS Oldham CCG, NHS Salford CCG, NHS Stockport CCG, NHS Tameside & Glossop CCG, NHS Trafford CCG and NHS Wigan Borough 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 Bolton CCG, NHS Bury CCG, NHS Cheshire CCG, NHS Heywood, Middleton and Rochdale CCG, NHS Manchester CCG, NHS Oldham CCG, NHS Salford CCG, NHS Stockport CCG, NHS Tameside & Glossop CCG, NHS Trafford CCG and NHS Wigan Borough CCG are 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 the NHS Bolton CCG, NHS Bury CCG, NHS Cheshire CCG, NHS Heywood, Middleton and Rochdale CCG, NHS Manchester CCG, NHS Oldham CCG, NHS Salford CCG, NHS Stockport CCG, NHS Tameside & Glossop CCG, NHS Trafford CCG and NHS Wigan Borough CCG - this 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 [3 paragraphs unchanged] Tameside and Glossop Integrated Care NHS Foundation Trust supply IT infrastructure for Tameside and Glossop CCG and are therefore listed [32 words unchanged] agreement. This includes granting of access to the database[s] containing the data [1 paragraph unchanged] Agilisys supply data storage IT infrastructure for Wigan CCG and are therefore listed as a data processor. [28 words unchanged] agreement. This includes granting of access to the database(s) containing the data. NHS Midlands and Lancashire Commissioning Support Unit supply Salford City Council IT infrastructure for Arden and GEM Commissioning Support Unit Salford CCG and are therefore listed as a data processors. processor. They supply support to the system, but do not access data. Therefore, [12 words unchanged] a breach of the agreement. This includes granting of access to the database[s] database(s) containing the data. Microsoft Limited supply provide Cloud Services for NHS Arden Midlands and Greater East Midlands Lancashire Commissioning Support Unit and Northern Care Alliance NHS Foundation Trust supply IT infrastructure for Arden and GEM Commissioning Support Unit are therefore listed as a data processor. processors. They supply support to the system, but do not access data. Therefore, [16 words unchanged] agreement. This includes granting of access to the database[s] containing the data. Microsoft Limited supply provide Cloud Services for NHS Arden and Greater East Midlands Commissioning Support Unit and Cheshire CCG 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. Google UK Limited provide Cloud Services for Snowflake Computing U.K. 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 [1 paragraph unchanged] COMMISSIONING - STP Snowflake Computing U.K. Limited data supply IT infrastructure for Greater Manchester Health and Social Care Partnership 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. 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 Bolton CCG, NHS Bury CCG, NHS Heywood, Middleton and Rochdale CCG, NHS Manchester CCG, NHS Oldham CCG, NHS Salford CCG, NHS Stockport CCG, NHS Tameside & Glossop CCG, NHS Trafford CCG and NHS Wigan Borough 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 Bolton CCG, NHS Bury CCG, NHS Heywood, Middleton and Rochdale CCG, NHS Manchester CCG, NHS Oldham CCG, NHS Salford CCG, NHS Stockport CCG, NHS Tameside & Glossop CCG, NHS Trafford CCG and NHS Wigan Borough CCG are 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 the NHS Bolton CCG, NHS Bury CCG, NHS Heywood, Middleton and Rochdale CCG, NHS Manchester CCG, NHS Oldham CCG, NHS Salford CCG, NHS Stockport CCG, NHS Tameside & Glossop CCG, NHS Trafford CCG and NHS Wigan Borough CCG - this 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 [28 paragraphs unchanged] 16.. 16. e-Referral Service (eRS) - See Processing activities (5b) 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), [18 words unchanged] Services Data Set (CSDS), Diagnostic Imaging data (DIDS), National Cancer Waiting Times (CWT) and Civil Registries Monitoring Data (CRD), Set (CWT), Civil Registries Data (CRD) (Deaths), (Births and Deaths), National Diabetes Audit (NDA) and (NDA), Patient Reported Outcome Measures (PROMs) and (PROMs), e-Referral Service (eRS) (eRS), Personal Demographics Service (PDS), Summary Hospital-level Mortality Indicator (SHMI), Medicines Dispensed in Primary Care (NHSBSA Data) and Adult Social Care data only is securely transferred from the DSCRO to Arden and Greater East Midlands Commissioning Support Unit. [12 paragraphs unchanged] Data Processor 2 - Oldham CCG Northern Care Alliance NHS Foundation Trust (hosting Greater Manchester Shared Services) 1. Pseudonymised SUS, SUS+, Local Provider data, Mental Health data (MHSDS, MHMDS, MHLDDS), Maternity data (MSDS), [15 words unchanged] Services Data Set (CSDS), Diagnostic Imaging data (DIDS), National Cancer Waiting Times Monitoring Data Set (CWT), Civil Registries Data (CRD), (CRD) (Births and Deaths), National Diabetes Audit (NDA) and (NDA), Patient Reported Outcome Measures (PROMs) (PROMs), e-Referral Service (eRS), Personal Demographics Service (PDS), Summary Hospital-level Mortality Indicator (SHMI) and Medicines Dispensed in Primary Care (NHSBSA 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 provide data management [5 words unchanged] and Greater East Midlands Commissioning Support Unit then send the data to Oldham CCG Northern Care Alliance NHS Foundation Trust (hosting Greater Manchester Shared Services). 3. Oldham CCG Northern Care Alliance NHS Foundation Trust (hosting Greater Manchester Shared Services) link data and provide analysis to: [8 paragraphs unchanged] 5. Northern Care Alliance NHS Foundation Trust (hosting Greater Manchester Shared Services Services) then pass the processed, pseudonymised and linked data to the CCG. 6. Aggregation of required data for CCG management use will be completed by Oldham CCG Northern Care Alliance NHS Foundation Trust (hosting Greater Manchester Shared Services) or the CCG as instructed by the CCG. [2 paragraphs unchanged] 1) Pseudonymised SUS+ SUS+, Civil Registration Deaths and Local Provider data only is securely transferred from the DSCRO to Arden and GEM Commissioning Support Unit [6 paragraphs unchanged] Data Processor 4 - Manchester CCG and NHS England (Hosting Greater Manchester Health and Social Care Partnership) 1. Pseudonymised SUS, SUS+, Local Provider data, Mental Health data (MHSDS, MHMDS, MHLDDS), Maternity data (MSDS), [15 words unchanged] Services Data Set (CSDS), Diagnostic Imaging data (DIDS), National Cancer Waiting Times Monitoring Data Set (CWT), Civil Registries Data (CRD), (CRD) (Births and Deaths), National Diabetes Audit (NDA) and (NDA), Patient Reported Outcome Measures (PROMs) (PROMs), e-Referral Service (eRS), Personal Demographics Service (PDS), Summary Hospital-level Mortality Indicator (SHMI) and Medicines Dispensed in Primary Care (NHSBSA 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 provide data management [7 words unchanged] East Midlands Commissioning Support Unit then send the data to Manchester CCG and NHS England (Hosting Greater Manchester Health and Social Care Partnership) 3. Manchester CCG and NHS England (Hosting Greater Manchester Health and Social Care Partnership) link data and provide analysis and reports to: [9 paragraphs unchanged] 5. Reports provided by Manchester CCG and NHS England (Hosting Greater Manchester Health and Social Care Partnership) to the CCGs will [10 words unchanged] data they already receive and in accordance with the Data Sharing Agreement. 6. Aggregation of required data for CCG management use will be completed by Manchester CCG and NHS England (Hosting Greater Manchester Health and Social Care Partnership) or the CCG as instructed by the CCG. [1 paragraph unchanged] COMMISSIONING - GM CANCER Snowflake Computing U.K. Limited DATA MINIMISATION Snowflake host a tool used by Greater Manchester Health and Social Care Partnership for dataset landing and structuring. This uses Google Cloud processing. 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 For the purpose of Commissioning: Data Processor 5 - University of Manchester • Patients who are normally registered and/or resident within the GM Cancer (which includes the 11 CCGs) (including historical activity where the patient was previously registered or resident in another commissioner). 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), Civil Registries Data (CRD) (Births and Deaths), National Diabetes Audit (NDA), Patient Reported Outcome Measures (PROMs), e-Referral Service (eRS), Personal Demographics Service (PDS), Summary Hospital-level Mortality Indicator (SHMI) and Medicines Dispensed in Primary Care (NHSBSA Data) and Adult Social Care data only is securely transferred from the DSCRO to Arden and Greater East Midlands Commissioning Support Unit. and/or 2) Arden and GEM Commissioning Support Unit add derived fields and provide the University of Manchester access to the pseudonymised data via Arden and GEM Azure Infrastructure. Data is processed and stored within the Azure Infrastructure and will not be moved externally. • Patients treated by a provider where GM Cancer (which includes the 11 CCGs) 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. 3) University of Manchester run analysis of intervention impact, identification of at-risk populations and to link that to both service provision and reducing inequalities. and/or 4) Results and findings are then presented within the AGEM Azure environment. • Activity identified by the provider and recorded as such within national systems (such as SUS+) as for the attention of GM Cancer (which includes the 11 CCGs) - this is only for commissioning and relates to both national and local flows. 5) Arden and Greater East Midlands Commissioning Support Unit then pass the processed, pseudonymised and linked data to the CCG . This includes data that was previously under a different organisation name but has now merged into this CCG 6) Aggregation of required data for CCG management use will be completed by Arden and Greater East Midlands Commissioning Support Unit or the CCG as instructed by the CCG . 7) Patient level data will not be shared outside of the CCG and will only be shared within the CCG on a need to know basis, as per the purposes stipulated within the Data Sharing Agreement. External aggregated reports only with small number suppression can be shared as set out within NHS Digital guidance applicable to each data set. GM CANCER The Christie NHS Foundation Trust [16 paragraphs unchanged] Manchester CCG and NHS England (Hosting Greater Manchester Health and Social Care Partnership) will also provide reporting and analysis on this data for the CCGs

Expected output

[43 paragraphs unchanged] 23. Allow Commissioners to plan, evaluate and monitor health and social care policies, services, or interventions for the total local patient population 24. Allow Commissioners to compare their providers (trusts) mortality outcomes to the national baseline. 25. Investigate mortality outcomes for 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

[35 paragraphs unchanged] 19. Assists commissioners to make better decisions to support patients and drive changes in health care 20. Help drive changes in healthcare 20. Allows comparisons of providers performance to assist improvement in services – increase the quality 21. Allows comparisons of providers performance to assist improvement in services – increase the quality 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. 22. Inform commissioners and improve services 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). 23. 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. 23. Monitoring of entire population, as opposed to only those that engage with services 24. Understanding the interdependency of care services 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. 25. Targeting care more effectively 25. Monitor the quality and safety of the delivery of healthcare services. 26. Using value as the redesign principle 26. Allow focused commissioning support based on factual data rather than assumed and projected sources 27. Thoroughly investigating the needs of the population, to ensure the right services are available for individuals when and where they need them 27. Understand admissions linked to overprescribing. 28. 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 28. Add value to the population health management workstream by adding prescribing data into linked dataset for segmentation and stratification. 29. 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 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. Service redesign 30. Designing and implementing new payment models across health and adult social care 31. Health Needs Assessment – identification of underlying disease prevalence within the local population 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. 32. 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).

Benefits reported

Not stated in the previous version; added here.

Details of yielded benefits can be found in each of the individual data controllers annual reports. Significant details can be found in the lead controller's (NHS Manchester CCG) report found here - https://www.mhcc.nhs.uk/wp-content/uploads/2021/09/NHS-Manchester-CCG-Annual-Report-2020-21.pdf

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

Datasets: + e-Referral Service for Commissioning

Objective for processing

[54 paragraphs unchanged] - e-Referral Services (eRS) [13 paragraphs unchanged]  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. [1 paragraph unchanged] Processing for commissioning will be conducted by by: [44 paragraphs unchanged] To use pseudonymised data to provide intelligence to support the commissioning of [19 words unchanged] planned to support the needs of the population within the CCG area. The CCGs commission services from a range of providers covering a wide array of services. Each of the data flow categories requested supports the commissioned activity of one or more providers. The 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. [5 paragraphs unchanged] The pseudonymised data is required to for the following purposes: [11 paragraphs unchanged]  Patient stratification and predictive modelling - to highlight identify specific 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 [2 paragraphs unchanged] Processing for commissioning will be conducted by NHS Arden and Greater East Midlands Commissioning Support Unit, The Christie NHS Foundation Trust [hosting the GM Cancer intelligence Service] and - Manchester CCG (Hosting Greater Manchester Health and Social Care Partnership)

Processing activities

[21 paragraphs unchanged] Microsoft Limited supply provide Cloud Services for NHS Arden and Greater East Midlands 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. [39 paragraphs unchanged] 16.. e-Referral Service (eRS) - See Processing activities (5b) [2 paragraphs unchanged] 1. Pseudonymised SUS+, Local Provider data, Mental Health data (MHSDS, MHMDS, MHLDDS), [39 words unchanged] (CRD) (Deaths), National Diabetes Audit (NDA) and Patient Reported Outcome Measures (PROMs) and e-Referral Service (eRS) only is securely transferred from the DSCRO to Arden and Greater East Midlands Commissioning Support Unit. [77 paragraphs unchanged]

Expected output

[39 paragraphs unchanged] 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. 20. In improving the quality of referrals under current structures, CCGs are able to monitor the timing of key actions relating to referral letters. CCG’s are unable to see the contents of the referral letters. 21. CCGs may 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. 22. Using pseudonymised e-RS data to provide intelligence will support the understanding of the quantity of assessments required and demand management. CCGs will be able 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.

Expected measurable benefits

[34 paragraphs unchanged] 18. Allow reporting to drive changes and improve the quality of commissioned services and health outcomes for people. 19. Assists commissioners to make better decisions to support patients 20. Help drive changes in healthcare 21. Allows comparisons of providers performance to assist improvement in services – increase the quality 22. Inform commissioners and improve services 23. 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. 24. Understanding the interdependency of care services 25. Targeting care more effectively 26. Using value as the redesign principle 27. Thoroughly investigating the needs of the population, to ensure the right services are available for individuals when and where they need them 28. 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 29. 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 30. Service redesign 31. Health Needs Assessment – identification of underlying disease prevalence within the local population 32. 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).

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

Data controllers: + NHS CHESHIRE AND MERSEYSIDE ICB

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

Objective for processing

Commissioning COMMISSIONING - STP [49 paragraphs unchanged] - Civil Registries Data (CRD) (Births and Deaths) (Births) - Civil Registries Data (CRD) (Deaths) - National Diabetes Audit (NDA) - Patient Reported Outcome Measures (PROMs) [34 paragraphs unchanged] GREATER MANCHESTER (GM) CANCER GM Cancer (formally Greater Manchester and Eastern Cheshire Cancer Vanguard) (which includes the 11 CCGs) is responsible for ensuring that the delivery of cancer services for the Greater Manchester population meet national standards and that all patients have equal access to care. To ensure this happens, GM Cancer's Intelligence Service needs data for ongoing evaluation of care and outcomes at regional and local levels. These data need to be: 1. Available within a suitable time-frame that allows swift response as soon as any evidence of need arises 2. Able to be aggregated into cohorts that are fully representative of the GM cancer pathway population 3. At pseudonymised record level to allow full interrogation of each pathway and sub-pathway to understand where in the system problems lie and what actions need to be taken. To support the work of the GM Cancer, CCGs use local flows that are established with local providers. In this application, the local provider flows are within the "Acute" category, known as Acute Local Provider Flows which include, but are not limited to, the following data sets: • Cancer Outcomes and Services Dataset (COSD) • Cancer Waiting Times data (CWT) • Systemic Anti-Cancer Therapy Dataset (SACT) • National Radiotherapy Dataset (RTDS) A full list of Local Provider Flows is defined within Schedule 6 of the contract between CCGs and the Providers in line with the data held and requested in sections 3a and 3b of the Data Sharing Agreement. This timely access to these local data sets will be used to facilitate local clinical outcomes and performance evaluation in as close to real time as is possible. The data sets will not replicate the full PHE cancer registration service but provide a set of early indicators of how well the system is performing that will allow effective interventions to be made as needed. Being on a local scale and based on a single Vanguard cancer system the cancer intelligence service will generate metrics more quickly than the national service. Outputs from this approach are not designed to replace national statistics but to act as vital interim information for CCGs ahead of the release of official statistics. GM Cancer provides services across the CCGs in the Greater Manchester region and relevant cancer service providers; - NHS Bolton CCG - NHS Bury CCG - NHS Cheshire CCG - NHS Heywood, Middleton and Rochdale CCG - NHS Oldham CCG - NHS Salford CCG - NHS Manchester CCG - NHS Stockport CCG - NHS Tameside and Glossop CCG - NHS Trafford CCG - NHS Wigan Borough CCG To use pseudonymised data to provide intelligence to support the commissioning of health services. The data (containing both clinical and financial information) is analysed so that health care provision can be planned to support the needs of the population within the CCG area. The CCGs commission services from a range of providers covering a wide array of services. Each of the data flow categories requested supports the commissioned activity of one or more providers. The following pseudonymised datasets are required to provide intelligence to support commissioning of health services: - Local Provider Flows (Acute) - Civil Registries Data (Deaths) - SUS+ - National Cancer Waiting Times Monitoring Data Set (CWT) The pseudonymised data is required 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. GM Cancer supports the 11 CCGs (as listed in the Data Controller section). It is responsible on behalf of the CCGs for ensuring that the delivery of cancer services for the population of these CCGs meets national standards and that all patients have equal access to care. They use the data supplied for audit and evaluation of care at regional and local levels. Their analysis will be greatly enhanced by the inclusion of Civil Registrations (Deaths) data and the CCGs wish this dataset to be included in this DSA. Processing for commissioning will be conducted by NHS Arden and Greater East Midlands Commissioning Support Unit, The Christie NHS Foundation Trust [hosting the GM Cancer intelligence Service] and - Manchester CCG (Hosting Greater Manchester Health and Social Care Partnership)

Processing activities

PROCESSING CONDITIONS: [3 paragraphs unchanged] All access to data is managed under Role-Based Access Controls. Users can only access data authorised by their role. role and the tasks that they are required to undertake. Patient level data will not be linked other than as specifically detailed [16 words unchanged] 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. NHS Digital reminds all organisations party to this agreement of the need [17 words unchanged] that use) by “Personnel” (as defined within the Data Sharing Framework Contract ie: i.e.: employees, agents and contractors of the Data Recipient who may have access to that data) Onward Sharing ONWARD SHARING: [2 paragraphs unchanged] Segregation SEGREGATION: [1 paragraph unchanged] 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. [1 paragraph unchanged] Data Minimisation 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. For the purpose of Commissioning: 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 • Patients who are normally registered and/or resident within the CCG (including historical activity where the patient was previously registered or resident in another commissioner). 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). and/or • Patients treated by a provider where the CCG is the host/co-ordinating commissioner and/or has the primary responsibility for the provider services in the local health economy – 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 the CCG - this is only for commissioning and relates to both national and local flows. NHS Midlands and Lancashire Commissioning Support Unit supply IT infrastructure for 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 [3 paragraphs unchanged] NHS Ilkeston Community Hospital Data Centre Midlands and Lancashire Commissioning Support Unit supply IT infrastructure for Arden and GEM Commissioning Support Unit and are therefore listed as data processors. 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 [5 words unchanged] agreement. This includes granting of access to the database[s] containing the data. Commissioning Ilkeston Community Hospital (Part of Derbyshire Community Health Services NHS Foundation Trust) and Wrightington, Wigan and Leigh NHS Foundation Trust 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. COMMISSIONING - STP 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 Bolton CCG, NHS Bury CCG, NHS Heywood, Middleton and Rochdale CCG, NHS Manchester CCG, NHS Oldham CCG, NHS Salford CCG, NHS Stockport CCG, NHS Tameside & Glossop CCG, NHS Trafford CCG and NHS Wigan Borough 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 Bolton CCG, NHS Bury CCG, NHS Heywood, Middleton and Rochdale CCG, NHS Manchester CCG, NHS Oldham CCG, NHS Salford CCG, NHS Stockport CCG, NHS Tameside & Glossop CCG, NHS Trafford CCG and NHS Wigan Borough CCG are 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 the NHS Bolton CCG, NHS Bury CCG, NHS Heywood, Middleton and Rochdale CCG, NHS Manchester CCG, NHS Oldham CCG, NHS Salford CCG, NHS Stockport CCG, NHS Tameside & Glossop CCG, NHS Trafford CCG and NHS Wigan Borough CCG - this 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 [24 paragraphs unchanged] 12. Civil Registries Data (CRD) (Births) 13. Civil Registries Data (CRD) (Deaths) 14. National Diabetes Audit (NDA) 15. Patient Reported Outcome Measures (PROMs) [2 paragraphs unchanged] 1. Pseudonymised SUS+, Local Provider data, Mental Health data (MHSDS, MHMDS, MHLDDS), [23 words unchanged] Imaging data (DIDS), National Cancer Waiting Times (CWT) and Civil Registries Data (CRD), Civil Registries Data (CRD) (Deaths), National Diabetes Audit (NDA) and Patient Reported Outcome Measures (PROMs) 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 provide data management and add derived fields, link data and provide analysis to: to : [8 paragraphs unchanged] 4. Arden and Greater East Midlands Commissioning Support Unit then pass the processed, pseudonymised and linked data to the CCG. CCG . 5. Aggregation of required data for CCG management use will be completed by Arden and Greater East Midlands Commissioning Support Unit or the CCG as instructed by the CCG. CCG . [2 paragraphs unchanged] 1. Pseudonymised SUS, Local Provider data, Mental Health data (MHSDS, MHMDS, MHLDDS), [18 words unchanged] Services Data Set (CSDS), Diagnostic Imaging data (DIDS), National Cancer Waiting Times (CWT) and (CWT), Civil Registries Data (CRD) (CRD), National Diabetes Audit (NDA) and Patient Reported Outcome Measures (PROMs) only is securely transferred from the DSCRO to Arden and Greater East Midlands Commissioning Support Unit. [22 paragraphs unchanged] 1. Pseudonymised SUS, Local Provider data, Mental Health data (MHSDS, MHMDS, MHLDDS), [18 words unchanged] Services Data Set (CSDS), Diagnostic Imaging data (DIDS), National Cancer Waiting Times (CWT) and (CWT), Civil Registries Data (CRD) (CRD), National Diabetes Audit (NDA) and Patient Reported Outcome Measures (PROMs) only is securely transferred from the DSCRO to Arden and Greater East Midlands Commissioning Support Unit. [12 paragraphs unchanged] 6. Aggregation of required data for CCG management use will be completed by Manchester CCG (Hosting Greater Manchester Health and Social Care Partnership)p Partnership) or the CCG as instructed by the CCG. [1 paragraph unchanged] COMMISSIONING - GM CANCER 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 For the purpose of Commissioning: • Patients who are normally registered and/or resident within the GM Cancer (which includes the 11 CCGs) (including historical activity where the patient was previously registered or resident in another commissioner). and/or • Patients treated by a provider where GM Cancer (which includes the 11 CCGs) 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 GM Cancer (which includes the 11 CCGs) - this 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 The Data Services for Commissioners Regional Office (DSCRO) obtains the following data sets: 1) Local Provider Flows where the GM Cancer is referenced, received directly from providers) a. Acute 2) Civil Registries Data (Deaths) 3) SUS+ 4) National Cancer Waiting Times Monitoring Data Set (CWT) Data quality management and pseudonymisation is completed within the DSCRO and is then disseminated as follows: 1) Pseudonymised SUS+, National Cancer Waiting Times, Local Provider data and Civil Registries Data (Deaths) 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 then apply the following processing on the data: a. Additional checks for Data Quality issues such as local duplication of records, or adjustments for known North West data recording issues b. The creation of a number of additional locally derived fields that support further analysis. c. ‘Localise’ the data where appropriate to support Trust and CCG local reporting capabilities. 3) Arden and Greater East Midlands Commissioning Support Unit then pass the processed, pseudonymised and linkable data to the GM Cancer Intelligence Service hosted by the Christie NHS Foundation Trust. The cancer intelligence service consists of a small team who are all employees of the trust. 4) The Cancer Intelligence Service link the pseudonymised patient-level subsets (1-4 above) data sets to create a single data set that comprises one single record for each patient’s cancer pathway from referral to post treatment and after care experiences and outcomes. 5) Aggregation of required data for CCG management use will be completed by the Christie NHS Foundation Trust as instructed by the CCG. 6) Patient level data will not be shared outside of the CCGs and its data processors and will only be shared within the Data Controller on a need to know basis, as per the purposes stipulated within the Data Sharing Agreement. Only aggregated reports with small number suppression can be shared externally. Manchester CCG (Hosting Greater Manchester Health and Social Care Partnership) will also provide reporting and analysis on this data for the CCGs

Expected output

Commissioning [18 paragraphs unchanged] 8. GP Practice level dashboard reports include high flyers. reports. [4 paragraphs unchanged] a. o Patients at highest risk of admission b. Most expensive patients (top 15%) o High 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 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 15. 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 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

[30 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 d. Service and pathway review e. 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. 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