NHS Digital Data Release Register - reformatted

NHS South Sefton Ccg

Project 1 — NIC-130779-P1H2C

Opt outs honoured: N

Sensitive: Sensitive

When: 2017/12 — 2018/02.

Repeats: Ongoing

Legal basis: Health and Social Care Act 2012

Categories: Anonymised - ICO code compliant

Datasets:

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

Benefits:

Expected measurable benefits to health and/or social care including target date: Risk Stratification Risk stratification promotes improved case management in primary care and will lead to the following benefits being realised: 1. Improved planning by better understanding patient flows through the healthcare system, thus allowing commissioners to design appropriate pathways to improve patient flow and allowing commissioners to identify priorities and identify plans to address these. 2. Improved quality of services through reduced emergency readmissions, especially avoidable emergency admissions. This is achieved through mapping of frequent users of emergency services and early intervention of appropriate care. 3. Improved access to services by identifying which services may be in demand but have poor access, and from this identify areas where improvement is required. 4. Potentially reduced premature mortality by more targeted intervention in primary care, which supports the commissioner to meets its requirement to reduce premature mortality in line with the CCG Outcome Framework. 5. Better understanding of the health of and the variations in health outcomes within the population to help understand local population characteristics. All of the above lead to improved patient experience through more effective commissioning of services. Commissioning 1. Supporting Quality Innovation Productivity and Prevention (QIPP) to review demand management, integrated care and pathways. a. Analysis to support full business cases. b. Develop business models. c. Monitor In year projects. 2. Supporting Joint Strategic Needs Assessment (JSNA) for specific disease types. 3. Health economic modelling using: a. Analysis on provider performance against 18 weeks wait targets. b. Learning from and predicting likely patient pathways for certain conditions, in order to influence early interventions and other treatments for patients. c. Analysis of outcome measures for differential treatments, accounting for the full patient pathway. d. Analysis to understand emergency care and linking A&E and Emergency Urgent Care Flows (EUCC). 4. Commissioning cycle support for grouping and re-costing previous activity. 5. Enables monitoring of: a. CCG outcome indicators. b. Non-financial validation of activity. c. Successful delivery of integrated care within the CCG. d. Checking frequent or multiple attendances to improve early intervention and avoid admissions. e. Case management. f. Care service planning. g. Commissioning and performance management. h. List size verification by GP practices. i. Understanding the care of patients in nursing homes. 6. Feedback to NHS service providers on data quality at an aggregate and individual record level – only on data initially provided by the service providers.

Outputs:

Specific outputs expected, including target date: Risk Stratification 1. As part of the risk stratification processing activity detailed above, GPs have access to the risk stratification tool which highlights patients for whom the GP is responsible and have been classed as at risk. The only identifier available to GPs is the NHS numbers of their own patients. Any further identification of the patients will be completed by the GP on their own systems. 2. Output from the risk stratification tool will provide aggregate reporting of number and percentage of population found to be at risk. 3. Record level output will be available for commissioners (of the CCG), pseudonymised at patient level. 4. GP Practices will be able to view the risk scores for individual patients with the ability to display the underlying SUS data for the individual patients when it is required for direct care purposes by someone who has a legitimate relationship with the patient. 5. The CCG will be able to target specific patient groups and enable clinicians with the duty of care for the patient to offer appropriate interventions. The CCG will also be able to: o Stratify populations based on: disease profiles; conditions currently being treated; current service use; pharmacy use and risk of future overall cost o Plan work for commissioning services and contracts o Set up capitated budgets o Identify health determinants of risk of admission to hospital, or other adverse care outcomes. Commissioning 1. Commissioner reporting: a. Summary by provider view - plan & actuals year to date (YTD). b. Summary by Patient Outcome Data (POD) view - plan & actuals YTD. c. Summary by provider view - activity & finance variance by POD. d. Planned care by provider view - activity & finance plan & actuals YTD. e. Planned care by POD view - activity plan & actuals YTD. f. Provider reporting. g. Statutory returns. h. Statutory returns - monthly activity return. i. Statutory returns - quarterly activity return. j. Delayed discharges. k. Quality & performance referral to treatment reporting. 2. Readmissions analysis. 3. Production of aggregate reports for CCG Business Intelligence. 4. Production of project / programme level dashboards. 5. Monitoring of acute / community / mental health quality matrix. 6. Clinical coding reviews / audits. 7. Budget reporting down to individual GP Practice level. 8. GP Practice level dashboard reports include high flyers.

Processing:

Processing activities: Data must only be used as stipulated within this Data Sharing Agreement. Data Processors must only act upon specific instructions from the Data Controller. Data can only be stored at the addresses listed under storage addresses. The Data Controller and any Data Processor will only have access to records of patients of residence and registration within the CCG. Access is limited to those substantive employees with authorised user accounts used for identification and authentication. Patient level data will not be shared outside of the CCG unless it is for the purpose of Direct Care, where it may be shared only with those health professionals who have a legitimate relationship with the patient and a legitimate reason to access the data. CCGs should work with general practices within their CCG to help them fulfil data controller responsibilities regarding flow of identifiable data into risk stratification tools. No record level data will be linked other than as specifically detailed within this application/agreement. Data will only be shared with those parties listed and will only be used for the purposes laid out in the application/agreement. The data to be released from NHS Digital will not be national data, but only that data relating to the specific locality of interest of the applicant. The DSCRO (part of NHS Digital) will apply Type 2 objections before any identifiable data leaves the DSCRO. Risk Stratification - Arden and Greater East Midlands Commissioning Support Unit 1. Identifiable SUS data is obtained from the SUS Repository to the Data Services for Commissioners Regional Office (DSCRO). 2. Data quality management and standardisation of data is completed by the DSCRO and the data identifiable at the level of NHS number is transferred securely to Arden and Greater East Midlands Commissioning Support Unit, who hold the SUS data within the secure Data Centre on N3. 3. Identifiable GP Data is securely sent from the GP system to Arden and Greater East Midlands Commissioning Support Unit. 4. SUS data is linked to GP data in the risk stratification tool by the data processor. 5. Access to the Risk Stratification system that Arden and Greater East Midlands Commissioning Support Unit hosts is limited to those substantive employees with authorised user accounts used for identification and authentication. 6. Once Arden and Greater East Midlands Commissioning Support Unit has completed the processing, the CCG can access the online system via a secure N3 connection to access the data pseudonymised at patient level. Risk Stratification - Midlands & Lancashire Commissioning Support Unit 1. Identifiable SUS data is obtained from the SUS Repository to the Data Services for Commissioners Regional Office (DSCRO). 2. Data quality management and standardisation of data is completed by the DSCRO and the data identifiable at the level of NHS number is transferred securely to Midlands & Lancashire Commissioning Support Unit, who hold the SUS data within the secure Data Centre on N3. 3. Identifiable GP Data is securely sent from the GP system to Midlands & Lancashire Commissioning Support Unit. 4. SUS data is linked to GP data in the risk stratification tool by the data processor. 5. As part of the risk stratification processing activity, GPs have access to the risk stratification tool within the data processor, which highlights patients with whom the GP has a legitimate relationship and have been classed as at risk. The only identifier available to GPs is the NHS numbers of their own patients. Any further identification of the patients will be completed by the GP on their own systems. 6. Access to the Risk Stratification system Midlands & Lancashire Commissioning Support Unit hosts is limited to those substantive employees with authorised user accounts used for identification and authentication. 7. Once Midlands & Lancashire Commissioning Support Unit has completed the processing, the CCG can access the online system via a secure N3 connection to access the data pseudonymised at patient level. Commissioning The Data Services for Commissioners Regional Office (DSCRO) obtains the following data sets: 1. SUS Data quality management and pseudonymisation is completed within the DSCRO and is then disseminated as follows: Data Processor 1 – I5 Limited 1) Pseudonymised SUS, 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 and provide analysis. 3) Arden and Greater East Midlands Commissioning Support Unit.then pass the processed, pseudonymised and linked data to I5 Limited. I5 Limited analyse the data to see patient journeys for pathways or service design, re-design and de-commissioning. 4) Aggregation of required data for CCG management use will be completed by I5 Limited or the CCG as instructed by the CCG. 5) Patient level data will not be shared outside of the CCG and will only be shared within the CCG on a need to know basis, as per the purposes stipulated within the Data Sharing Agreement. External aggregated reports only with small number suppression can be shared.

Objectives:

Objective for processing: Risk Stratification To use SUS data identifiable at the level of NHS number according to S.251 CAG 7-04(a) (and Primary Care Data) for the purpose of Risk Stratification. Risk Stratification provides a forecast of future demand by identifying high risk patients. This enables commissioners to initiate proactive management plans for patients that are potentially high service users. Risk Stratification enables General Practitioners (GPs) to better target intervention in Primary Care. Risk Stratification will be conducted by Arden and Greater East Midlands Commissioning Support Unit and Midlands & Lancashire Commissioning Support Unit Commissioning To use pseudonymised data to provide intelligence to support commissioning of health services. The pseudonymised data is required to ensure that analysis of health care provision can be completed to support the needs of the health profile of the population within the CCG area based on the full analysis of multiple pseudonymised datasets. The CCGs commission services from a range of providers covering a wide array of services. Each of the data flow categories requested supports the commissioned activity of one or more providers. The following pseudonymised datasets are required to provide intelligence to support commissioning of health services: - Secondary Uses Service (SUS) The pseudonymised data is required to ensure that analysis of health care provision can be completed to support the needs of the health profile of the population within the CCG area based on the full analysis of multiple pseudonymised datasets. Processing for commissioning will be conducted by I5 Health Limited


Project 2 — NIC-47184-P3G4K

Opt outs honoured: Y, N

Sensitive: Sensitive

When: 2016/12 — 2018/02.

Repeats: Ongoing

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

Categories: Identifiable, Anonymised - ICO code compliant, Identifiable

Datasets:

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

Benefits:

Risk Stratification Risk stratification promotes improved case management in primary care and will lead to the following benefits being realised: 1. Improved planning by better understanding patient flows through the healthcare system, thus allowing commissioners to design appropriate pathways to improve patient flow and allowing commissioners to identify priorities and identify plans to address these. 2. Improved quality of services through reduced emergency readmissions, especially avoidable emergency admissions. This is achieved through mapping of frequent users of emergency services and early intervention of appropriate care. 3. Improved access to services by identifying which services may be in demand but have poor access, and from this identify areas where improvement is required. 4. Potentially reduced premature mortality by more targeted intervention in primary care, which supports the commissioner to meets its requirement to reduce premature mortality in line with the CCG Outcome Framework. 5. Better understanding of the health of and the variations in health outcomes within the population to help understand local population characteristics. All of the above lead to improved patient experience through more effective commissioning of services. Pseudonymised – SUS and Local Flows 1. Supporting Quality Innovation Productivity and Prevention (QIPP) to review demand management, Integrated care and pathways. a. Analysis to support full business cases. b. Develop business models. c. Monitor In year projects. 2. Supporting Joint Strategic Needs Assessment (JSNA) for specific disease types. 3. Health economic modelling using: a. Analysis on provider performance against 18 weeks wait targets. b. Learning from and predicting likely patient pathways for certain conditions, in order to influence early interventions and other treatments for patients. c. Analysis of outcome measures for differential treatments, accounting for the full patient pathway. d. Analysis to understand emergency care and linking A&E and Emergency Urgent Care Flows (EUCC) flows. 4. Commissioning cycle support for grouping and re-costing previous activity. 5. Enables monitoring of: a. CCG outcome indicators. b. Non-financial validation of activity. c. Successful delivery of integrated care within the CCG. d. Checking frequent or multiple attendances to improve early intervention and avoid admissions. e. Case management. f. Care service planning. g. Commissioning and performance management. h. List size verification by GP practices. i. Understanding the care of patients in nursing homes. 6. Feedback to NHS service providers on data quality at an aggregate and individual record level – only on data initially provided by the service providers. Pseudonymised – MHSDS, MHMDS, MHLDDS, MSDS, IAPT, CYPHS and DIDS 1. Supporting Quality Innovation Productivity and Prevention (QIPP) to review demand management, Integrated care and pathways. a. Analysis to support full business cases. b. Develop business models. c. Monitor In year projects. 2. Supporting Joint Strategic Needs Assessment (JSNA) for specific disease types. 3. Health economic modelling using: a. Analysis on provider performance against 18 weeks wait targets. b. Learning from and predicting likely patient pathways for certain conditions, in order to influence early interventions and other treatments for patients. c. Analysis of outcome measures for differential treatments, accounting for the full patient pathway. d. Analysis to understand emergency care and linking A&E and Emergency Urgent Care Flows (EUCC) flows. 4. Commissioning cycle support for grouping and re-costing previous activity. 5. Enables monitoring of: a. CCG outcome indicators. b. Non-financial validation of activity. c. Successful delivery of integrated care within the CCG. d. Checking frequent or multiple attendances to improve early intervention and avoid admissions. e. Case management. f. Care service planning. g. Commissioning and performance management. h. List size verification by GP practices. i. Understanding the care of patients in nursing homes. 6. Feedback to NHS service providers on data quality at an aggregate and individual record level – only on data initially provided by the service providers.

Outputs:

Risk Stratification 1. As part of the risk stratification processing activity detailed above, GPs have access to the risk stratification tool which highlights patients for whom the GP is responsible and have been classed as at risk. The risk stratification presents pseudonymised data to the GPs. GPs are able to re-identify information only for their own patients for the purpose of direct care. 2. Output from the risk stratification tool will provide aggregate reporting of number and percentage of population found to be at risk. 3. Record level output will be available for commissioners pseudonymised at patient level and aggregated at patient level. Pseudonymised – SUS and Local Flows 1. Commissioner reporting: a. Summary by provider view - plan & actuals year to date (YTD). b. Summary by Patient Outcome Data (POD) view - plan & actuals YTD. c. Summary by provider view - activity & finance variance by POD. d. Planned care by provider view - activity & finance plan & actuals YTD. e. Planned care by POD view - activity plan & actuals YTD. f. Provider reporting. g. Statutory returns. h. Statutory returns - monthly activity return. i. Statutory returns - quarterly activity return. j. Delayed discharges. k. Quality & performance referral to treatment reporting. 2. Readmissions analysis. 3. Production of aggregate reports for CCG Business Intelligence. 4. Production of project / programme level dashboards. 5. Monitoring of acute / community / mental health quality matrix. 6. Clinical coding reviews / audits. 7. Budget reporting down to individual GP Practice level. 8. GP Practice level dashboard reports include high flyers. Pseudonymised – MHSDS, MHMDS, MHLDDS, MSDS, IAPT, CYPHS and DIDS 1. Commissioner reporting: a. Summary by provider view - plan & actuals year to date (YTD). b. Summary by Patient Outcome Data (POD) view - plan & actuals YTD. c. Summary by provider view - activity & finance variance by POD. d. Planned care by provider view - activity & finance plan & actuals YTD. e. Planned care by POD view - activity plan & actuals YTD. f. Provider reporting. g. Statutory returns. h. Statutory returns - monthly activity return. i. Statutory returns - quarterly activity return. j. Delayed discharges. k. Quality & performance referral to treatment reporting. 2. Readmissions analysis. 3. Production of aggregate reports for CCG Business Intelligence. 4. Production of project / programme level dashboards. 5. Monitoring of acute / community / mental health quality matrix. 6. Clinical coding reviews / audits. 7. Budget reporting down to individual GP Practice level. 8. GP Practice level dashboard reports include high flyers.

Processing:

Prior to the release of identifiable data by North West DSCRO, Type 2 objections will be applied and the relevant patient’s data redacted. Risk Stratification Data Processor 1 – Arden & GEM CSU: 1. SUS Data is sent from the SUS Repository to North West Data Services for Commissioners Regional Office (DSCRO) to the data processor. 2. SUS data identifiable at the level of NHS number regarding hospital admissions, A&E attendances and outpatient attendances is delivered securely from North West DSCRO to the data processor. 3. Data quality management and standardisation of data is completed by North West DSCRO and the data identifiable at the level of NHS number is transferred securely to Arden & GEM CSU, who hold the SUS data within the secure Data Centre on N3. 4. Identifiable GP Data is securely sent from the GP system to Arden & GEM CSU. 5. SUS data is linked to GP data in the risk stratification tool by the data processor. 6. Arden & GEM CSU who hosts the risk stratification system that holds SUS data is limited to those administrative staff with authorised user accounts used for identification and authentication. 7. Once Arden & GEM CSU has completed the processing, the data is passed to the CCG in pseudonymised form at patient level and as aggregated reports. Data Processor 2 – Midlands & Lancashire CSU: 1. SUS Data is sent from the SUS Repository to North West Data Services for Commissioners Regional Office (DSCRO) to the data processor. 2. SUS data identifiable at the level of NHS number regarding hospital admissions, A&E attendances and outpatient attendances is delivered securely from North West DSCRO to the data processor. 3. Data quality management and standardisation of data is completed by North West DSCRO and the data identifiable at the level of NHS number is transferred securely to Arden & GEM CSU, who hold the SUS data within the secure Data Centre on N3. 4. Identifiable GP Data is securely sent from the GP system to Arden & GEM CSU. 5. SUS data is linked to GP data in the risk stratification tool by the data processor. 6. As part of the risk stratification processing activity, GPs have access to the risk stratification tool within the data processor, which highlights patients with whom the GP has a legitimate relationship and have been classed as at risk. The risk stratification presents pseudonymised data to the GPs. GPs are able to re-identify information only for their own patients for the purpose of direct care. 7. Arden & GEM CSU who hosts the risk stratification system that holds SUS data is limited to those administrative staff with authorised user accounts used for identification and authentication. 8. Data identifiable at the level of NHS number is transferred securely to Midlands & Lancashire CSU who apply the data to the BI tool to analyse and produce reports. 9. Once Midlands & Lancashire CSU has completed the processing, the CCG can access the online system via a secure N3 connection to access the data pseudonymised at patient level and aggregated reports. 10. As part of the risk stratification processing activity, GPs have access to the risk stratification tool within the data processor, which highlights patients with whom the GP has a legitimate relationship and have been classed as at risk. The only identifier available to GPs is the NHS numbers of their own patients. Any further identification of the patients will be completed by the GP on their own systems. Pseudonymised – SUS and Local Flows Data Processor 1 – Arden & GEM CSU: 1. North West Data Services for Commissioners Regional Office (DSCRO) receives a flow of SUS identifiable data for the CCG from the SUS Repository. North West DSCRO also receives identifiable local provider data for the CCG directly from Providers. 2. Data quality management and pseudonymisation of data is completed by the DSCRO and the pseudonymised data is then passed securely to Arden & GEM CSU for the addition of derived fields, linkage of data sets and analysis. 3. Arden & GEM CSU then pass the processed, pseudonymised and linked data to the CCG who analyse the data to see patient journeys for pathways or service design, re-design and de-commissioning. 4. Patient level data will not be shared outside of the CCG and will only be shared within the CCG on a need to know basis, as per the purposes stipulated within the Data Sharing Agreement. External aggregated reports only with small number suppression in line with the HES analysis guide. Data Processor 2 – Midlands & Lancashire CSU: 1. North West Data Services for Commissioners Regional Office (DSCRO) receives a flow of SUS identifiable data for the CCG from the SUS Repository. North West DSCRO also receives identifiable local provider data for the CCG directly from Providers. 2. The identifiable SUS data and identifiable local provider flow data is securely transferred from North West DSCRO to Central Midlands DSCRO. 3. Data quality management and pseudonymisation of data is completed by Central Midlands DSCRO and the pseudonymised data is then passed securely to Midland & Lancashire CSU for the addition of derived fields, linkage of data sets and analysis. 5. Midland & Lancashire CSU then pass the processed, pseudonymised and linked data to the CCG who analyse the data to see patient journeys for pathways or service design, re-design and de-commissioning. 6. Patient level data will not be shared outside of the CCG and will only be shared within the CCG on a need to know basis, as per the purposes stipulated within the Data Sharing Agreement. External aggregated reports only with small number suppression in line with the HES analysis guide. Data Processor 3 – I5 Health Limited (SUS only) 1. North West Data Services for Commissioners Regional Office (DSCRO) receives a flow of SUS identifiable data for the CCG from the SUS Repository. 2. Data quality management and pseudonymisation of data is completed by North West DSCRO and the pseudonymised data is then passed securely to Arden & GEM CSU for the addition of derived fields, linkage of data sets and analysis. 3. Arden & GEM CSU then passes the pseudonymised data securely to I5 health. 4. I5 Health analyse the data to see patient journeys for pathway or service design, re-design and de-commissioning. 5. I5 Health then pass the processed pseudonymised data to the CCG Data Processor 4 - Advancing Quality Alliance (AQuA) 1. North West Data Services for Commissioners Regional Office (DSCRO) receives a flow of SUS identifiable data for the CCG from the SUS Repository. North West DSCRO also receives identifiable local provider data for the CCG directly from Providers. 2. Data quality management and pseudonymisation of data is completed by North West DSCRO and the pseudonymised data is then passed securely to Arden & GEM CSU for the addition of derived fields, linkage of data sets and analysis. 3. Arden & GEM CSU then passes the pseudonymised data securely to AQuA to provide support for a range of quality improvement programmes including the NW Advancing Quality Programme. AQuA identifies cohorts of patients within specific disease groups for further analysis to help drive quality improvements across the region. 4. AQuA produces aggregate reports only with small number suppression in line with the HES analysis guide. Only aggregate reports are sent to the CCG. Data Processor 5 – Academic Health Sciences Network (Utilisation Management Team) 1. North West Data Services for Commissioners Regional Office (DSCRO) receives a flow of SUS identifiable data for the CCG from the SUS Repository. North West DSCRO also receives identifiable local provider data for the CCG directly from Providers. 2. Data quality management and pseudonymisation of data is completed by North West DSCRO and the pseudonymised data is then passed securely to Arden & GEM CSU for the addition of derived fields, linkage of data sets and analysis. 3. Arden & GEM CSU then passes the pseudonymised data securely to the Academic Health Service (Utilisation Management Team) (AHSN UMT) 4. The AHSN UMT receive pseudonymised SUS data for Greater Manchester patients. They analyse the data to look at processes rather than patients, for example, A&E performance, process times, bed days as well as ‘deep dives’ to support clinical reviews for CCGs. 6. AHSN UMT produces aggregate reports only with small number suppression in line with the HES analysis guide. Only aggregate reports are sent to the CCG. Pseudonymised – Mental Health, MSDS, IAPT, CYPHS and DIDS Data Processor 1 – Arden & GEM CSU: 1. North West Data Services for Commissioners Regional Office (DSCRO) receives a flow of data identifiable at the level of NHS number for Mental Health (MHSDS, MHMDS, MHLDDS) and Maternity (MSDS). North West DSCRO also receive a flow of pseudonymised patient level data for each CCG for Improving Access to Psychological Therapies (IAPT), Child and Young People’s Health (CYPHS) and Diagnostic Imaging (DIDS) for commissioning purposes 2. The pseudonymised data is securely transferred from North West DSCRO to Central Midlands DSCRO. 3. Data quality management and pseudonymisation of data is completed by North West DSCRO and the pseudonymised data is then passed securely to Arden & GEM CSU for the addition of derived fields, linkage of data sets and analysis. 4. Arden & GEM CSU then pass the processed, pseudonymised and linked data to the CCG who analyse the data to see patient journeys for pathways or service design, re-design and de-commissioning. 5. The CCG analyses the data to see patient journeys for pathway or service design, re-design and de-commissioning 6. Aggregation of required data for CCG management use can be completed by the CSU or 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 in line with the HES analysis guide. Data Processor 2 – Midlands & Lancashire CSU: 1. North West Data Services for Commissioners Regional Office (DSCRO) receives a flow of data identifiable at the level of NHS number for Mental Health (MHSDS, MHMDS, MHLDDS) and Maternity (MSDS). North West DSCRO also receive a flow of pseudonymised patient level data for each CCG for Improving Access to Psychological Therapies (IAPT), Child and Young People’s Health (CYPHS) and Diagnostic Imaging (DIDS) for commissioning purposes 2. The identifiable SUS data and identifiable local provider flow data is securely transferred from North West DSCRO to Central Midlands DSCRO. 3. Data quality management and pseudonymisation of data is completed by Central Midlands DSCRO and the pseudonymised data is then passed securely to Midland & Lancashire CSU for the addition of derived fields, linkage of data sets and analysis. 4. Midland & Lancashire CSU then pass the processed, pseudonymised and linked data to the CCG who analyse the data to see patient journeys for pathways or service design, re-design and de-commissioning. 5. Aggregation of required data for CCG management use can be completed by the CSU or the CCG 6. Patient level data will not be shared outside of the CCG and will only be shared within the CCG on a need to know basis, as per the purposes stipulated within the Data Sharing Agreement. External aggregated reports only with small number suppression in line with the HES analysis guide. Data Processor 4 - Advancing Quality Alliance (AQuA) 1. North West Data Services for Commissioners Regional Office (DSCRO) receives a flow of data identifiable at the level of NHS number for Mental Health (MHSDS, MHMDS, MHLDDS). 2. Data quality management and pseudonymisation of data is completed by North West DSCRO and the pseudonymised data is then passed securely to Arden & GEM CSU for the addition of derived fields, linkage of data sets and analysis. 3. Arden & GEM CSU then passes the pseudonymised data securely to Advancing Quality Alliance (AQuA). 4. AQuA receives pseudonymised SUS data for Greater Manchester patients. They analyse the data to look at processes rather than patients, for example, A&E performance, process times, bed days as well as ‘deep dives’ to support clinical reviews for CCGs. 5. AQuA produces aggregate reports only with small number suppression in line with the HES analysis guide. Only aggregate reports are sent to the CCG.

Objectives:

Risk Stratification To use SUS data identifiable at the level of NHS number according to S.251 CAG 7-04(a) (and Primary Care Data) for the purpose of Risk Stratification. Risk Stratification provides a forecast of future demand by identifying high risk patients. This enables commissioners to initiate proactive management plans for patients that are potentially high service users. Risk Stratification enables GPs to better target intervention in Primary Care Pseudonymised – SUS and Local Flows To use pseudonymised data to provide intelligence to support commissioning of health services. The pseudonymised data is required to ensure that analysis of health care provision can be completed to support the needs of the health profile of the population within the CCG area based on the full analysis of multiple pseudonymised datasets. The CCGs commission services from a range of providers covering a wide array of services. Each of the data flow categories requested supports the commissioned activity of one or more providers. Pseudonymised – Mental Health, MSDS, IAPT, CYPHS and DIDS To use pseudonymised data for the following datasets to provide intelligence to support commissioning of health services : - Mental Health Minimum Data Set (MHMDS) - Mental Health Learning Disability Data Set (MHLDDS) - Mental Health Services Data Set (MHSDS) - Maternity Services Data Set (MSDS) - Improving Access to Psychological Therapy (IAPT) - Child and Young People Health Service (CYPHS) - Diagnostic Imaging Data Set (DIDS) The pseudonymised data is required to ensure that analysis of health care provision can be completed to support the needs of the health profile of the population within the CCG area based on the full analysis of multiple pseudonymised datasets. No record level data will be linked other than as specifically detailed within this application/agreement. Data will only be shared with those parties listed and will only be used for the purposes laid out in the application/agreement. The data to be released from the HSCIC will not be national data, but only that data relating to the specific locality of interest of the applicant.