NHS Digital Data Release Register - reformatted

NHS Bolton Ccg

Project 1 — NIC-191209-G3Z6Z

Opt outs honoured: N

Sensitive: Sensitive

When: 2018/03 — 2018/05.

Repeats: Ongoing

Legal basis: Health and Social Care Act 2012

Categories: Anonymised - ICO code compliant

Datasets:

  • Acute-Local Provider Flows

Benefits:

Benefits to patient outcomes 1. Will identify and compare areas of weakness and strengths within the present system that will be acted upon by CCGs and providers to improve services and outcomes. a. Patients diagnosed at late stage have shorter survival outcomes. The identification of groups of patients at increased risk of delayed diagnosis will be used to identify why late diagnosis occurring and then enable the most effective targeted measures to be implemented in order to improve earlier diagnosis for these patients and thus improved patient outcomes 2. Will be used to compare and contrast service performance, clinical outcomes and patient experience to highlight areas where changes are required. a. Identification of services and CCGs that are under-performing in terms of 62 day waiting times, percentage patients diagnosed at late stage cancer and percentage of patients who survive one year, compared to the regional and England average, will enable us to identify why some patients are waiting longer for treatment, why some patients are being diagnosed late and why some patients have poorer outcomes so that the most effective targeted improvement measures can be implemented in order to reduce variation across the region, and ensure waiting times, earlier diagnosis and outcomes are equitable for all patients. 3. Will provide evidence of good practice across the system. a. Identification of best practice will be used to show the level that should be achievable by all services and CCGs and used to develop measures to improve services and ultimately patient experience and outcomes across the region. Benefits to the Wider GM Cancer System 1. Increased access to detailed timely intelligence on local service performance, outcomes and patient experience without new or extra data collection. 2. Increased understanding of service and outcomes variation by providing easy access to organisation level metrics and data, in an understandable format 3. Improvement in data quality by demonstrating possible problems within data collection at individual centres and allow them to be rectified 4. Provides an evidence base of outcomes and patient experience facilitating investigation of differences which is open and difficult to challenge 5. Allows good practice to be shared and emulated 6. Allows benchmarking against other organisations These benefits are expected to start to be measurable early 2018/2019.

Outputs:

The Cancer Intelligence Service will use the datasets for different levels of analysis and reporting. All the below are non-disclosive: 1) Dashboard of performance and out outcomes metrics mapped to all data. Dashboard reports will be aggregated with small number suppression. 2) Dashboard of performance and out outcomes metrics mapped to individual CCGs. Dashboard reports will be aggregated with small number suppression. 3) Audience specific reports (based on aggregate data with small number suppression) with key messages and narrative to aid interpretation of the metrics dashboard. 4) Bespoke investigatory analysis and audit to highlight areas of concern and best practice, help improve patient’s care, reduce unwanted variation and aid decision making.

Processing:

Data must only be used as stipulated within this Data Sharing Agreement. Data Processors must only act upon specific instructions from the Data Controller. Data can only be stored at the addresses listed under storage addresses. The Data Controller and any Data Processor will only have access to records of patients of residence and registration within the CCG. 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. No record level data will be linked to the Acute Local Provider flows. All outputs to the CCGs listed will only contain aggregate data with small number suppression. 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. NHS Digital reminds all organisations party to this agreement of the need to comply with the Data Sharing Framework Contract requirements, including those regarding the use (and purposes of that use) by “Personnel” (as defined within the Data Sharing Framework Contract ie: employees, agents and contractors of the Data Recipient who may have access to that data) Segregation Where the Data Processor and/or the Data Controller hold both identifiable and pseudonymised data, the data will be held separately so data cannot be linked. All access to data is audited Commissioning The Data Services for Commissioners Regional Office (DSCRO) obtains the following data sets: 1) Local Provider Flows where the GM&EC Cancer Vanguard is referenced, received directly from providers) a. Acute 1. Cancer Outcomes and Services Dataset (COSD) 2. Cancer waiting Times data (CWT) 3. Systemic Anti-Cancer Therapy Dataset (SACT) 4. National Radiotherapy Dataset (RTDS) Data quality management and pseudonymisation is completed within the DSCRO and is then disseminated as follows: 1) Pseudonymised Local Provider 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 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&EC 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 (i-iv above) datasets to create a single dataset 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.

Objectives:

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 and Eastern Cheshire (GM&EC) population meet national standards and that all patients have equal access to care. To ensure this happens, the Cancer Vanguard’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 timeframe 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&EC 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 Cancer Vanguard, local flows have been established by the relevant CCGs. The local datasets required are listed below; • Cancer Outcomes and Services Dataset (COSD) • Cancer Waiting Times data (CWT) • Systemic Anti-Cancer Therapy Dataset (SACT) • National Radiotherapy Dataset (RTDS) These, referred together, are the Acute Local Provider flows. This timely access to these local datasets will be used to facilitate local clinical outcomes and performance evaluation in as close to real time as is possible. The datasets 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. The Cancer Vanguard provides services across the CCGs in the GM&EC region and relevant cancer service providers; Greater Manchester and Eastern Cheshire CCGs: - NHS Bolton CCG - NHS Bury CCG - NHS Eastern 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 The following pseudonymised dataset is required to provide intelligence to support commissioning of health services: - Local Provider Flows o Acute


Project 2 — NIC-47180-P3Z1Q

Opt outs honoured: Y, N

Sensitive: Sensitive

When: 2016/12 — 2018/05.

Repeats: Ongoing

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

Categories: Identifiable, Anonymised - ICO code compliant, 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, Public Health & Screening services
  • 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 - Public Health & Screening services
  • Local Provider Data - Mental Health
  • Local Provider Data - Other not elsewhere classified
  • Local Provider Data - Population Data
  • SUS Accident & Emergency data
  • SUS Admitted Patient Care data
  • SUS Outpatient data
  • Maternity Services Dataset
  • SUS data (Accident & Emergency, Admitted Patient Care & Outpatient)
  • SUS for Commissioners
  • Public Health and Screening Services-Local Provider Flows
  • Primary Care Services-Local Provider Flows
  • Population Data-Local Provider Flows
  • Other Not Elsewhere Classified (NEC)-Local Provider Flows
  • Mental Health-Local Provider Flows
  • Maternity Services Data Set
  • Experience, Quality and Outcomes-Local Provider Flows
  • Emergency Care-Local Provider Flows
  • Diagnostic Services-Local Provider Flows
  • Diagnostic Imaging Dataset
  • Demand for Service-Local Provider Flows
  • Community-Local Provider Flows
  • Children and Young People Health
  • Ambulance-Local Provider Flows
  • Acute-Local Provider Flows

Benefits:

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 (Pseudonymised) – SUS and Local Flows 1. Supporting Quality Innovation Productivity and Prevention (QIPP) to review demand management, integrated care and pathways. a. Analysis to support full business cases. b. Develop business models. c. Monitor In year projects. 2. Supporting Joint Strategic Needs Assessment (JSNA) for specific disease types. 3. Health economic modelling using: a. Analysis on provider performance against 18 weeks wait targets. b. Learning from and predicting likely patient pathways for certain conditions, in order to influence early interventions and other treatments for patients. c. Analysis of outcome measures for differential treatments, accounting for the full patient pathway. d. Analysis to understand emergency care and linking A&E and Emergency Urgent Care Flows (EUCC). 4. Commissioning cycle support for grouping and re-costing previous activity. 5. Enables monitoring of: a. CCG outcome indicators. b. Non-financial validation of activity. c. Successful delivery of integrated care within the CCG. d. Checking frequent or multiple attendances to improve early intervention and avoid admissions. e. Case management. f. Care service planning. g. Commissioning and performance management. h. List size verification by GP practices. i. Understanding the care of patients in nursing homes. 6. Feedback to NHS service providers on data quality at an aggregate and individual record level – only on data initially provided by the service providers. . Commissioning (Pseudonymised) – Mental Health, 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). 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 reports 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. Commissioning (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. Commissioning (Pseudonymised) – Mental Health, 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 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:

North West DSCRO (part of NHS Digital) will apply Type 2 objections before any identifiable data leaves the DSCRO. The CCG and any Data Processor will only have access to records of its own CCG. Access is limited to those administrative staff with authorised user accounts used for identification and authentication. Risk Stratification 1. SUS Data is sent from the SUS Repository to North West Data Services for Commissioners Regional Office (DSCRO) to the data processor. 2. SUS data identifiable at the level of NHS number regarding hospital admissions, A&E attendances and outpatient attendances is delivered securely from North West DSCRO to the data processor. 3. Data quality management and standardisation of data is completed by North West DSCRO and the data identifiable at the level of NHS number is transferred securely to Arden & GEM CSU, who hold the SUS data within the secure Data Centre on N3. 4. Identifiable GP Data is securely sent from the GP system to Arden & GEM CSU. 5. SUS data is linked to GP data in the risk stratification tool by the data processor. 6. Arden & GEM CSU who hosts the risk stratification system that holds SUS data is limited to those administrative staff with authorised user accounts used for identification and authentication. 7. Once Arden & GEM CSU has completed the processing, the data is passed to the CCG in pseudonymised form at patient level and as aggregated reports. Commissioning (Pseudonymised) – SUS and Local Flows Data Processor 1 – Arden and GEM CSU 1. North West Data Services for Commissioners Regional Office (DSCRO) obtains a flow of SUS identifiable data for the CCG from the SUS Repository. North West DSCRO also obtains 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 and GEM CSU for the addition of derived fields, linkage of data sets and analysis. Allowed linkage is between SUS data sets and local flows. 3. Arden and GEM CSU then pass the processed, pseudonymised and linked data to the CCG. The CCG analyse the data to see patient journeys for pathways or service design, re-design and de-commissioning. 4. Aggregation of required data for CCG management use will be completed by the CSU 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 in line with the HES analysis guide can be shared where contractual arrangements are in place. Data Processor 2 – GMSS 1. North West Data Services for Commissioners Regional Office (DSCRO) receives a flow of SUS identifiable data for the CCG from the SUS Repository. North West DSCRO also receives identifiable local provider data for the CCG directly from Providers. 2. Data quality management and pseudonymisation of data is completed by North West DSCRO and the pseudonymised data is then passed securely to Arden & GEM CSU for the addition of derived fields, linkage of data sets and analysis. 3. Arden & GEM CSU then passes the pseudonymised data securely to the Greater Manchester Shared Services (GMSS). 4. GMSS analyse the data to see patient journeys for pathway or service design, re-design and de-commissioning. 5. GMSS then pass the processed pseudonymised data to the CCG 6. Patient level data will not be shared outside of the CCG and will only be shared within the CCG on a need to know basis, as per the purposes stipulated within the Data Sharing Agreement. External aggregated reports only with small number suppression in line with the HES analysis guide. Data Processor 3 - 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 4 – Academic Health Sciences Network (Utilisation Management Team) (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 and analysis. 3. Arden & GEM CSU then passes the pseudonymised data securely to the Academic Health Service (Utilisation Management Team) (AHSN UMT) 4. The AHSN UMT receive pseudonymised SUS data for Greater Manchester patients. They analyse the data to look at processes rather than patients, for example, A&E performance, process times, bed days as well as ‘deep dives’ to support clinical reviews for CCGs. 5. AHSN UMT produces aggregate reports only with small number suppression in line with the HES analysis guide. Only aggregate reports are sent to the CCG. Commissioning (Pseudonymised) – Mental Health, MSDS, IAPT, CYPHS and DIDS Data Processor 1 - Arden and GEM CSU 1. North West Data Services for Commissioners Regional Office (DSCRO) obtains a flow of data identifiable at the level of NHS number for Mental Health (MHSDS, MHMDS, MHLDDS), Maternity (MSDS), Improving Access to Psychological Therapies (IAPT), Child and Young People’s Health (CYPHS) and Diagnostic Imaging (DIDS) for commissioning purposes. 2. Data quality management and pseudonymisation of data is completed by North West DSCRO and the pseudonymised data is then passed securely to Arden and GEM CSU for the addition of derived fields and analysis. 3. Arden and GEM CSU then pass the processed, pseudonymised data to the CCG. 4. The CCG analyses the data to see patient journeys for pathway or service design, re-design and de-commissioning. 5. Aggregation of required data for CCG management use will be completed by the CSU or the CCG as instructed by the CCG 6. Patient level data will not be shared outside of the CCG and will only be shared within the CCG on a need to know basis, as per the purposes stipulated within the Data Sharing Agreement. External aggregated reports only with small number suppression can be shared where contractual arrangements are in place. Data Processor 2 – GMSS (via DP1): 1. North West Data Services for Commissioners Regional Office (DSCRO) receives a flow of data identifiable at the level of NHS number for Mental Health (MHSDS, MHMDS, MHLDDS) North West DSCRO also receive a flow of pseudonymised patient level data for each CCG for Improving Access to Psychological Therapies (IAPT) for commissioning purposes 2. The pseudonymised data is securely transferred from North West DSCRO to Arden & GEM CSU for the addition of derived fields, linkage of data sets and analysis. 3. Arden & GEM CSU then pass the processed, pseudonymised and linked data to the Greater Manchester Shared Services (GMSS) 4. GMSS analyse and conduct the BI function and then send the Pseudonymised data to the CCG. 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 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:

Advancing Quality Alliance (AQuA) and the Academic Health Science Network are hosted by Salford Royal NHS Foundation Trust who are the legal entity for both. AQuA provide support for a range of quality improvement programmes including the NW Advancing Quality Programme. They will identify cohorts of patients within specific disease groups for further analysis to help drive quality improvements across the region. The Academic Health Sciences Network (Utilisation Management Team) receive Pseudonymised SUS data for Greater Manchester patients. They analyse the data to look at processes rather than patients, for example, A&E performance, process times, bed days as well as ‘deep dives’ to support clinical reviews for CCGs. Greater Manchester Shared Services (GMSS) have taken BI services in house and are now hosted by Oldham CCG. AGEM CSU flow data to a small team within GMSS. Access to the data is restricted to this team who access and manage the data. These BI services were previously provided by North West CSU. GMSS deliver a range of services including; • effective use of resources; • data quality; • information governance; • market management; • provider contract & performance management; To enable GMSS to support these services a team within the GMSS have controlled access to SUS data at a pseudonymised level. Access to the data is controlled by AGEM CSU using users’ roles to ensure only appropriate users gain access to pseudonymised data. Data can then be used for reporting to support the range of services being offered to CCGs, and CCGs receive aggregate level reports from GMSS. GMSS staff are separate from Oldham CCG staff and accordingly have separate functions and roles. Risk Stratification To use SUS data identifiable at the level of NHS number according to S.251 CAG 7-04(a)/2013 (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. Commissioning (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. Commissioning (Pseudonymised) – Mental Health, Maternity, IAPT, CYPHS and DIDS To use pseudonymised data for the following datasets to provide intelligence to support commissioning of health services : - Mental Health Minimum Data Set (MHMDS) - Mental Health Learning Disability Data Set (MHLDDS) - Mental Health Services Data Set (MHSDS) - Maternity Services Data Set (MSDS) - Improving Access to Psychological Therapy (IAPT) - Child and Young People Health Service (CYPHS) - Diagnostic Imaging Data Set (DIDS) The pseudonymised data is required to ensure that analysis of health care provision can be completed to support the needs of the health profile of the population within the CCG area based on the full analysis of multiple pseudonymised datasets. No record level data will be linked other than as specifically detailed within this application/agreement. Data will only be shared with those parties listed and will only be used for the purposes laid out in the application/agreement. The data to be released from the HSCIC will not be national data, but only that data relating to the specific locality of interest of the applicant.