NHS Digital Data Release Register - reformatted

NHS Heywood, Middleton And Rochdale Ccg

Project 1 — NIC-139074-D9N2C

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:

  • 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
  • Mental Health Services Data Set
  • Mental Health Minimum Data Set
  • Mental Health and Learning Disabilities Data Set
  • Maternity Services Data Set
  • Improving Access to Psychological Therapies 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:

Commissioning 1. Supporting Quality Innovation Productivity and Prevention (QIPP) to review demand management, integrated care and pathways. a. Analysis to support full business cases. b. Develop business models. c. Monitor In year projects. 2. Supporting Joint Strategic Needs Assessment (JSNA) for specific disease types. 3. Health economic modelling using: a. Analysis on provider performance against 18 weeks wait targets. b. Learning from and predicting likely patient pathways for certain conditions, in order to influence early interventions and other treatments for patients. c. Analysis of outcome measures for differential treatments, accounting for the full patient pathway. d. Analysis to understand emergency care and linking A&E and Emergency Urgent Care Flows (EUCC). 4. Commissioning cycle support for grouping and re-costing previous activity. 5. Enables monitoring of: a. CCG outcome indicators. b. Financial and Non-financial validation of activity. c. Successful delivery of integrated care within the CCG. d. Checking frequent or multiple attendances to improve early intervention and avoid admissions. e. Case management. f. Care service planning. g. Commissioning and performance management. h. List size verification by GP practices. i. Understanding the care of patients in nursing homes. j. Understanding delayed discharges to reduce hospital length of stay 6. Feedback to NHS service providers on data quality at an aggregate and individual record level – only on data initially provided by the service providers. 7. Improved planning by better understanding patient flows through the healthcare system, thus allowing commissioners to design appropriate pathways to improve patient flow and allowing commissioners to identify priorities and identify plans to address these. 8. Improved quality of services through reduced emergency readmissions, especially avoidable emergency admissions. This is achieved through mapping of frequent users of emergency services and early intervention of appropriate care. 9. Improved access to services by identifying which services may be in demand but have poor access, and from this identify areas where improvement is required. 10. Potentially reduced premature mortality by more targeted intervention in primary care, which supports the commissioner to meets its requirement to reduce premature mortality in line with the CCG Outcome Framework. 11. Better understanding of the health of and the variations in health outcomes within the population to help understand local population characteristics. 12. Better understanding of contract requirements, contract execution, and required services for management of existing contracts, and to assist with identification and planning of future contracts 13. Insights into patient outcomes, and identification of the possible efficacy of outcomes-based contracting opportunities.

Outputs:

Commissioning 1. Commissioner reporting: a. Summary by provider view - plan & actuals year to date (YTD). b. Summary by Patient Outcome Data (POD) view - plan & actuals YTD. c. Summary by provider view - activity & finance variance by POD. d. Planned care by provider view - activity & finance plan & actuals YTD. e. Planned care by POD view - activity plan & actuals YTD. f. Provider reporting. g. Statutory returns. h. Statutory returns - monthly activity return. i. Statutory returns - quarterly activity return. j. Delayed discharges. k. Quality & performance referral to treatment reporting. 2. Readmissions analysis. 3. Production of aggregate reports for CCG Business Intelligence. 4. Production of project / programme level dashboards. 5. Monitoring of acute / community / mental health quality matrix. 6. Clinical coding reviews / audits. 7. Budget reporting down to individual GP Practice level. 8. GP Practice level dashboard reports include high flyers. 9. Comparators of CCG performance with similar CCGs as set out by a specific range of care quality and performance measures detailed activity and cost reports 10. Data Quality and Validation measures allowing data quality checks on the submitted data 11. Contract Management and Modelling 12. Patient Stratification, such as: o Patients at highest risk of admission o Most expensive patients (top 15%) o Frail and elderly o Patients that are currently in hospital o Patients with most referrals to secondary care o Patients with most emergency activity o Patients with most expensive prescriptions o Patients recently moving from one care setting to another i. Discharged from hospital ii. Discharged from community 13. Production of aggregate reports for CCG Business Intelligence including:- a. Understanding current and future population needs and resource utilisation b. Tracking outcomes across pathways, and meeting outcome targets c. Evaluation of interventions across pathways d. Designing and implementing new payment models e. More sophisticated risk stratification and predictive analytics f. Demand management 14. Production of project / programme level dashboards

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 patient level data will be linked other than as specifically detailed within this agreement. Data will only be shared with those parties listed and will only be used for the purposes laid out in the application/agreement. The data to be released from NHS Digital will not be national data, but only that data relating to the specific locality 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. SUS 2. Local Provider Flows (received directly from providers) o Acute o Ambulance o Community o Demand for Service o Diagnostic Service o Emergency Care o Experience, Quality and Outcomes o Mental Health o Other Not Elsewhere Classified o Population Data o Primary Care Services o Public Health Screening 3. Mental Health Minimum Data Set (MHMDS) 4. Mental Health Learning Disability Data Set (MHLDDS) 5. Mental Health Services Data Set (MHSDS) 6. Maternity Services Data Set (MSDS) 7. Improving Access to Psychological Therapy (IAPT) 8. Child and Young People Health Service (CYPHS) 9. Diagnostic Imaging Data Set (DIDS) 10. Adult Social Care Data quality management and pseudonymisation is completed within the DSCRO and is then disseminated as follows: Data Processor 1 – Arden and Greater East Midlands Commissioning Support Unit 1) Pseudonymised SUS, Local Provider data, Mental Health data (MHSDS, MHMDS, MHLDDS), Maternity data (MSDS), Improving Access to Psychological Therapies data (IAPT), Child and Young People’s Health data (CYPHS), Diagnostic Imaging data (DIDS) and Adult Social Care data only is securely transferred from the DSCRO to Arden and Greater East Midlands Commissioning Support Unit. 2) Arden and Greater East Midlands Commissioning Support Unit add derived fields, link data and provide analysis to: o See patient journeys for pathways or service design, re-design and de-commissioning. o Check recorded activity against contracts or invoices and facilitate discussions with providers. o Undertake population health management o Undertake data quality and validation checks o Thoroughly investigate the needs of the population o Understand cohorts of residents who are at risk o Conduct Health Needs Assessments 3) Allowed linkage is between the data sets contained within point 1. 4) Arden and Greater East Midlands Commissioning Support Unit then pass the processed, pseudonymised and linked data to the 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. 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.

Objectives:

Commissioning 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:  Secondary Uses Service (SUS)  Local Provider Flows o Acute o Ambulance o Community o Demand for Service o Diagnostic Service o Emergency Care o Experience, Quality and Outcomes o Mental Health o Other Not Elsewhere Classified o Population Data o Primary Care Services o Public Health Screening  Mental Health Minimum Data Set (MHMDS)  Mental Health Learning Disability Data Set (MHLDDS)  Mental Health Services Data Set (MHSDS)  Maternity Services Data Set (MSDS)  Improving Access to Psychological Therapy (IAPT)  Child and Young People Health Service (CYPHS)  Diagnostic Imaging Data Set (DIDS)  Adult Social Care 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  Ensuring we do what we should  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  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  Monitoring service and integrated care outcomes across a pathway or care setting involving Adult Social Care  Developing, through evaluation, more effective interventions across a pathway or care setting involving Adult Social Care  Designing and implementing new payment models across health and Adult Social Care  Understanding current and future population needs and resource utilisation for local strategic planning purposes. 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 Arden and Greater East Midlands Commissioning Support Unit.


Project 2 — NIC-140023-N1F1S

Opt outs honoured: N, Y

Sensitive: Sensitive

When: 2018/03 — 2018/05.

Repeats: Ongoing

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

Categories: Anonymised - ICO code compliant, Identifiable

Datasets:

  • 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
  • National Cancer Waiting Times Monitoring DataSet
  • Mental Health-Local Provider Flows
  • Mental Health Services Data Set
  • Mental Health Minimum Data Set
  • Mental Health and Learning Disabilities Data Set
  • Maternity Services Data Set
  • Improving Access to Psychological Therapies 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
  • Community Services Data Set
  • Children and Young People Health
  • Ambulance-Local Provider Flows
  • Acute-Local Provider Flows

Benefits:

Invoice Validation 1. Financial validation of activity 2. CCG Budget control 3. Commissioning and performance management 4. Meeting commissioning objectives without compromising patient confidentiality 5. The avoidance of misappropriation of public funds to ensure the ongoing delivery of patient care 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 thus allowing early intervention. 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. Supports the commissioner to meets its requirement to reduce premature mortality in line with the CCG Outcome Framework by allowing for more targeted intervention in primary care. 5. Better understanding of local population characteristics through analysis of their health and healthcare outcomes All of the above lead to improved patient experience through more effective commissioning of services. MSD Healthcare Services 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. MSD Healthcare (acting as data Processors for the CCG) Test Bed programme seeks to help combat the issues of Long Term Conditions costs (LTCs are one of the main drivers of cost and activity in the NHS and now account for about 50 per cent of all GP appointments, 64 per cent of all outpatient appointments and over 70 per cent of inpatient bed days. Identifying a significantly 'at risk' individual just one year earlier than at present can radically reduce their chances of developing future ill health) with the development of a tool combining data analytics with a telehealth service for managing patients at risk of developing LTCs and designed to help medical professionals better predict who may be at risk. HMR CCG is working with MSD to deliver an NHS England national testbed programme. More information can be found in https://www.england.nhs.uk/ourwork/innovation/test-beds/ltc-prog/ Commissioning 1. Supporting Quality Innovation Productivity and Prevention (QIPP) to review demand management, integrated care and pathways. a. Analysis to support full business cases. b. Develop business models. c. Monitor In year projects. 2. Supporting Joint Strategic Needs Assessment (JSNA) for specific disease types. 3. Health economic modelling using: a. Analysis on provider performance against 18 weeks wait targets. b. Learning from and predicting likely patient pathways for certain conditions, in order to influence early interventions and other treatments for patients. c. Analysis of outcome measures for differential treatments, accounting for the full patient pathway. d. Analysis to understand emergency care and linking A&E and Emergency Urgent Care Flows (EUCC). 4. Commissioning cycle support for grouping and re-costing previous activity. 5. Enables monitoring of: a. CCG outcome indicators. b. Financial and Non-financial validation of activity. c. Successful delivery of integrated care within the CCG. d. Checking frequent or multiple attendances to improve early intervention and avoid admissions. e. Case management. f. Care service planning. g. Commissioning and performance management. h. List size verification by GP practices. i. Understanding the care of patients in nursing homes. 6. Feedback to NHS service providers on data quality at an aggregate and individual record level – only on data initially provided by the service providers. 7. Improved planning by better understanding patient flows through the healthcare system, thus allowing commissioners to design appropriate pathways to improve patient flow and allowing commissioners to identify priorities and identify plans to address these. 8. Improved quality of services through reduced emergency readmissions, especially avoidable emergency admissions. This is achieved through mapping of frequent users of emergency services and early intervention of appropriate care. 9. Improved access to services by identifying which services may be in demand but have poor access, and from this identify areas where improvement is required. 10. Potentially reduced premature mortality by more targeted intervention in primary care, which supports the commissioner to meets its requirement to reduce premature mortality in line with the CCG Outcome Framework. 11. Better understanding of the health of and the variations in health outcomes within the population to help understand local population characteristics. 12. Better understanding of contract requirements, contract execution, and required services for management of existing contracts, and to assist with identification and planning of future contracts 13. Insights into patient outcomes, and identification of the possible efficacy of outcomes-based contracting opportunities.

Outputs:

Invoice Validation 1. Addressing poor data quality issues 2. Production of reports for business intelligence 3. Budget reporting 4. Validation of invoices for non-contracted events 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. MSD Healthcare Services 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 / aggregate with small number suppression 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. Pseudonimised data with only with small number suppression will be provided to University of Manchester for the purposes of evaluating the success of the national Test Bed programme Commissioning 1. Commissioner reporting: a. Summary by provider view - plan & actuals year to date (YTD). b. Summary by Patient Outcome Data (POD) view - plan & actuals YTD. c. Summary by provider view - activity & finance variance by POD. d. Planned care by provider view - activity & finance plan & actuals YTD. e. Planned care by POD view - activity plan & actuals YTD. f. Provider reporting. g. Statutory returns. h. Statutory returns - monthly activity return. i. Statutory returns - quarterly activity return. j. Delayed discharges. k. Quality & performance referral to treatment reporting. 2. Readmissions analysis. 3. Production of aggregate reports for CCG Business Intelligence. 4. Production of project / programme level dashboards. 5. Monitoring of acute / community / mental health quality matrix. 6. Clinical coding reviews / audits. 7. Budget reporting down to individual GP Practice level. 8. GP Practice level dashboard reports include high flyers. 9. Comparators of CCG performance with similar CCGs as set out by a specific range of care quality and performance measures detailed activity and cost reports 10. Data Quality and Validation measures allowing data quality checks on the submitted data 11. Contract Management and Modelling 12. Patient Stratification, such as: o Patients at highest risk of admission o Most expensive patients (top 15%) o Frail and elderly o Patients that are currently in hospital o Patients with most referrals to secondary care o Patients with most emergency activity o Patients with most expensive prescriptions o Patients recently moving from one care setting to another o Discharged from hospital o Discharged from community

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. All access to data is managed under Roles-Based Access Controls 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 patient level data will be linked other than as specifically detailed within this agreement. Data will only be shared with those parties listed and will only be used for the purposes laid out in the application/agreement. The data to be released from NHS Digital will not be national data, but only that data relating to the specific locality and that data required by the applicant. The DSCRO (part of NHS Digital) will apply Type 2 objections before any identifiable data leaves the DSCRO. NHS Digital reminds all organisations party to this agreement of the need to comply with the Data Sharing Framework Contract requirements, including those regarding the use (and purposes of that use) by “Personnel” (as defined within the Data Sharing Framework Contract ie: employees, agents and contractors of the Data Recipient who may have access to that data) Segregation Where the Data Processor and/or the Data Controller hold both identifiable and pseudonymised data, the data will be held separately so data cannot be linked. All access to data is auditable by NHS Digital. Data for the purpose of Invoice Validation is kept within the CEfF, and only used by staff properly trained and authorised for the activity. Only CEfF staff are able to access data in the CEfF and only CEfF staff operate the invoice validation process within the CEfF. Data flows directly in to the CEfF from the CCG and from the providers – it does not flow through any other processors. Invoice Validation 1. SUS+ Data is obtained from the SUS+ Repository by the Data Services for Commissioners Regional Office (DSCRO). 2. The DSCRO pushes a one-way data flow of SUS+ data into the Controlled Environment for Finance (CEfF) located in the CCG. 3. The CEfF conduct the following processing activities for invoice validation purposes: a. Validating that the Clinical Commissioning Group is responsible for payment for the care of the individual by using SUS+ and/or backing flow data. b. Once the backing information is received, this will be checked against national NHS and local commissioning policies as well as being checked against system access and reports provided by NHS Digital to confirm the payments are: i. In line with Payment by Results tariffs ii. In relation to a patient registered with the CCG GP or resident within the CCG area. iii. The health care provided should be paid by the CCG in line with CCG guidance.  4. The CCG are notified by the CEfF that the invoice has been validated and can be paid. Any discrepancies or non-validated invoices are investigated and resolved Risk Stratification Data Processor 1 – 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. 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. Once Arden and Greater East Midlands Commissioning Support Unit has completed the processing, the CCG can access the online system via a secure connection to access the data aggregate with small number suppression. Data Processor 3 – MSD Healthcare Services 1. Identifiable SUS+ data is obtained from the SUS Repository to the Data Services for Commissioners Regional Office (DSCRO). 2. Identifiable local provider flow data is obtained directly from Providers to the Data Services for Commissioners Regional Office (DSCRO). 3. Data quality management and standardisation of data is completed by the DSCRO. The data is then pseudonymised using the open pseudonymiser tool. 4. The pseudonymised SUS data is then securely transferred to MSD Healthcare Services 5. Identifiable GP data is pseudonymised at source using the open pseudonymiser tool and then sent to MSD Healthcare Services. 6. MSD Healthcare Services link and analyse the data to see patient journeys for pathways or service design, re-design and de-commissioning. 7. MSD Healthcare Services then pass the processed, pseudonymised and linked data to the CCG 8. 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. Commissioning The Data Services for Commissioners Regional Office (DSCRO) obtains the following data sets: 1. SUS+ 2. Local Provider Flows (received directly from providers) a. Acute b. Ambulance c. Community d. Demand for Service e. Diagnostic Service f. Emergency Care g. Experience, Quality and Outcomes h. Mental Health i. Other Not Elsewhere Classified j. Population Data k. Primary Care Services l. Public Health Screening 3. Mental Health Minimum Data Set (MHMDS) 4. Mental Health Learning Disability Data Set (MHLDDS) 5. Mental Health Services Data Set (MHSDS) 6. Maternity Services Data Set (MSDS) 7. Improving Access to Psychological Therapy (IAPT) 8. Child and Young People Health Service (CYPHS) 9. Community Services Data Set (CSDS) 10. Diagnostic Imaging Data Set (DIDS) 11. National Cancer Waiting Times Monitoring Data Set (CWT) Data quality management and pseudonymisation is completed within the DSCRO and is then disseminated as follows: Data Processor 1 – Arden and Greater East Midlands Commissioning Support Unit 1. Pseudonymised SUS+, Local Provider data, Mental Health data (MHSDS, MHMDS, MHLDDS), Maternity data (MSDS), Improving Access to Psychological Therapies data (IAPT), Child and Young People’s Health data (CYPHS), Community Services Data Set (CSDS), Diagnostic Imaging data (DIDS) and the National Cancer Waiting Times Monitoring Data Set (CWT) only is securely transferred from the DSCRO to Arden and Greater East Midlands Commissioning Support Unit. 2. Arden and Greater East Midlands Commissioning Support Unit add derived fields, link data and provide analysis to: a. See patient journeys for pathways or service design, re-design and de-commissioning. b. Check recorded activity against contracts or invoices and facilitate discussions with providers. c. Undertake population health management d. Undertake data quality and validation checks e. Thoroughly investigate the needs of the population f. Understand cohorts of residents who are at risk g. Conduct Health Needs Assessments 3. Allowed linkage is between the data sets contained within point 1. 4. Arden and Greater East Midlands Commissioning Support Unit then pass the processed, pseudonymised and linked data to the 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. 6. Patient level data will not be shared outside of the CCG and will only be shared within the CCG on a need to know basis, as per the purposes stipulated within the Data Sharing Agreement. External aggregated reports only with small number suppression can be shared as set out within NHS Digital guidance applicable to each data set. Data Processor 2 – GMSS (via DP1): 1. Pseudonymised SUS+, Local Provider data and Mental Health data (MHSDS, MHMDS, MHLDDS) 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 link data. 3. Allowed linkage is between the data sets contained within point 1. 4. Arden and Greater East Midlands Commissioning Support Unit then pass the processed, pseudonymised and linked data to the Greater Manchester Shared Services. Greater Manchester Shared Services analyse the data to: a. See patient journeys for pathways or service design, re-design and de-commissioning. b. Check recorded activity against contracts or invoices and facilitate discussions with providers. c. Undertake population health management d. Undertake data quality and validation checks e. Thoroughly investigate the needs of the population f. Understand cohorts of residents who are at risk g. Conduct Health Needs Assessments 5. Greater Manchester Shared 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 Greater Manchester Shared Services 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.

Objectives:

Data Processor 1 – Arden and Greater East Midlands Commissioning Support Unit conduct Risk Stratification as instructed by the CCG. The CSU also processes SUS, Local Provider flows, mental health, IAPT, MSDS, CYPHS and DIDS for the purpose of commissioning. Data Processor 2 - 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. Data Processor 3 - MSD Healthcare Services - The data processors will construct a technological platform to link pseudonymised clinical and administrative data across primary and secondary care. These data from primary care and secondary care, provided by NHS Digital will be linked with a common Pseudonym. Lower Super Output Area deprivation data will be linked at geography level. Heywood Middleton Rochdale CCG is working with MSD to deliver an NHS England national testbed programme. More information can be found in https://www.england.nhs.uk/ourwork/innovation/test-beds/ltc-prog/ To use pseudonymised linked data to provide intelligence to support commissioning of health services, a linked data set is needed to enable a much better understanding of all the care that the Rochdale population is currently receiving and the interactions that individuals have with different parts of the system in order to better understand where interventions using tele-health can take place. The linked data set will support HMR CCG in designing and delivering a more joined up, more efficient and higher quality care service in the future and is a key enabler of the LTC Testbed Programme of improving population outcomes by delivering person-centred care in the most appropriate way. A linked data set will enable the LTC Testbed partner organisations to better identify, support and treat people in an integrated health and care system. Data Processor 4 – Heywood, Middleton and Rochdale CCG are conducting Invoice Validation functions using pseudonymised SUS and Local Provider data. Invoice Validation Invoice validation is part of a process by which providers of care or services get paid for the work they do. Invoices are submitted to the Clinical Commissioning Group (CCG) so they are able to ensure that the activity claimed for each patient is their responsibility. This is done by processing and analysing Secondary User Services (SUS+) data, which is received into a secure Controlled Environment for Finance (CEfF). The SUS+ data is identifiable at the level of NHS number. The NHS number is only used to confirm the accuracy of backing-data sets and will not be used further. The legal basis for this to occur is under Section 251 of NHS Act 2006. Invoice Validation with be conducted by Heywood, Middleton and Rochdale CCG The CCG are advised by CEfF within Heywood, Middleton and Rochdale CCG whether payment for invoices can be made or not. Risk Stratification Risk stratification is a tool for identifying and predicting which patients are at high risk or are likely to be. at high risk and prioritising the management of their care in order to prevent worse outcomes. To conduct risk stratification Secondary User Services (SUS+) data, identifiable at the level of NHS number is linked with Primary Care data (from GPs) and an algorithm is applied to produce risk scores. Risk Stratification provides a forecast of future demand by identifying high risk patients. Commissioners can then prepare plans for patients who may require high levels of care. Risk Stratification also enables General Practitioners (GPs) to better target intervention in Primary Care. The legal basis for this to occur is under Section 251 of NHS Act 2006 (CAG 7-04(a)). Risk Stratification will be conducted by Arden and Greater East Midlands Commissioning Support Unit. Commissioning 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: - Secondary Uses Service (SUS+) - Local Provider Flows o Acute o Ambulance o Community o Demand for Service o Diagnostic Service o Emergency Care o Experience, Quality and Outcomes o Mental Health o Other Not Elsewhere Classified o Population Data o Primary Care Services o Public Health Screening - Mental Health Minimum Data Set (MHMDS) - Mental Health Learning Disability Data Set (MHLDDS) - Mental Health Services Data Set (MHSDS) - Maternity Services Data Set (MSDS) - Improving Access to Psychological Therapy (IAPT) - Child and Young People Health Service (CYPHS) - Community Services Data Set (CSDS) - Diagnostic Imaging Data Set (DIDS) - National Cancer Waiting Times Monitoring Data Set (CWT) 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 • Ensuring we do what we should  Data Quality and Validation – allowing data quality checks on the submitted data  Thoroughly investigating the needs of the population, to ensure the right services are available for individuals when and where they need them  Understanding cohorts of residents who are at risk of becoming users of some of the more expensive services, to better understand and manage those needs  Monitoring population health and care interactions to understand where people may slip through the net, or where the provision of care may be being duplicated  Modelling activity across all data sets to understand how services interact with each other, and to understand how changes in one service may affect flows through another  Service redesign  Health Needs Assessment – identification of underlying disease prevalence within the local population  Patient stratification and predictive modelling - to identify specific patients at risk of requiring hospital admission and other avoidable factors such as risk of falls, computed using algorithms executed against linked de-identified data, and identification of future service delivery models The pseudonymised data is required to ensure that analysis of health care provision can be completed to support the needs of the health profile of the population within the CCG area based on the full analysis of multiple pseudonymised datasets. Processing for commissioning will be conducted by Arden and Greater East Midlands Commissioning Support Unit and Greater Manchester Shared Services (GMSS)


Project 3 — NIC-47155-P3C0F

Opt outs honoured: Y, N

Sensitive: Sensitive

When: 2016/12 — 2017/11.

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

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

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). 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 – Mental Health, Maternity, 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. 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. 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 – Mental Health, Maternity, 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:

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 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. Pseudonymised – SUS and Local Flows Data Processor 2 – GMSS (via DP1): 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 4 – AQuA (via DP1): 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) (SUS Only) (via DP1):: 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. NHS Bury CCG, NHS Heywood, Middleton and Rochdale CCG, NHS North Manchester CCG and NHS Oldham CCG have a collaborative information sharing agreement in place to share pseudonymised SLAM and SLAM Backup data between these CCGs only. SLAM data is included under Local Flows and is available under the Health and Social Care Act 2012. Pseudonymised – Mental Health and IAPT 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 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 1. 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. 2. 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. 3. The CCG analyses the data to see patient journeys for pathway or service design, re-design and de-commissioning 4. Aggregation of required data for CCG management use can be completed by the CSU or 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. Data Processor 2 – GMSS (via DP1): 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. 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. 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. Data Processor 4 - Advancing Quality Alliance (AQuA) (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). 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, 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.


Project 4 — NIC-90049-D5S8M

Opt outs honoured: N, Y

Sensitive: Sensitive

When: 2017/12 — 2018/05.

Repeats: Ongoing

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

Categories: Anonymised - ICO code compliant, Identifiable , Identifiable

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
  • 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:

Expected measurable benefits to health and/or social care including target date: Invoice Validation Financial validation of activity CCG Budget control Commissioning and performance management Meeting commissioning objectives without compromising patient confidentiality The avoidance of misappropriation of public funds to ensure the ongoing delivery of patient care Risk Stratification Risk stratification promotes improved case management in primary care and will lead to the following benefits being realised: 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. 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. 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. 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. 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. Data Processor 5- MSD Healthcare Services Risk stratification promotes improved case management in primary care and will lead to the following benefits being realised: 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. 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. 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. 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. 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. MSD Healthcare (acting as data Processors for the CCG) Test Bed programme seeks to help combat the issues of Long Term Conditions costs (LTCs are one of the main drivers of cost and activity in the NHS and now account for about 50 per cent of all GP appointments, 64 per cent of all outpatient appointments and over 70 per cent of inpatient bed days. Identifying a significantly 'at risk' individual just one year earlier than at present can radically reduce their chances of developing future ill health) with the development of a tool combining data analytics with a telehealth service for managing patients at risk of developing LTCs and designed to help medical professionals better predict who may be at risk. HMR CCG is working with MSD to deliver an NHS England national testbed programme. More information can be found in https://www.england.nhs.uk/ourwork/innovation/test-beds/ltc-prog/ Commissioning (Pseudonymised) – SUS and Local Flows Supporting Quality Innovation Productivity and Prevention (QIPP) to review demand management, integrated care and pathways. Analysis to support full business cases. Develop business models. Monitor In year projects. Supporting Joint Strategic Needs Assessment (JSNA) for specific disease types. Health economic modelling using: Analysis on provider performance against 18 weeks wait targets. Learning from and predicting likely patient pathways for certain conditions, in order to influence early interventions and other treatments for patients. Analysis of outcome measures for differential treatments, accounting for the full patient pathway. Analysis to understand emergency care and linking A&E and Emergency Urgent Care Flows (EUCC). Commissioning cycle support for grouping and re-costing previous activity. Enables monitoring of: CCG outcome indicators. Non-financial validation of activity. Successful delivery of integrated care within the CCG. Checking frequent or multiple attendances to improve early intervention and avoid admissions. Case management. Care service planning. Commissioning and performance management. List size verification by GP practices. Understanding the care of patients in nursing homes. 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, Maternity, IAPT, CYPHS and DIDS Supporting Quality Innovation Productivity and Prevention (QIPP) to review demand management, Integrated care and pathways. Analysis to support full business cases. Develop business models. Monitor In year projects. Supporting Joint Strategic Needs Assessment (JSNA) for specific disease types. Health economic modelling using: Analysis on provider performance against targets. Learning from and predicting likely patient pathways for certain conditions, in order to influence early interventions and other treatments for patients. Analysis of outcome measures for differential treatments, accounting for the full patient pathway. Commissioning cycle support for grouping and re-costing previous activity. Enables monitoring of: CCG outcome indicators. Non-financial validation of activity. Successful delivery of integrated care within the CCG. Checking frequent or multiple attendances to improve early intervention and avoid admissions. Case management. Care service planning. Commissioning and performance management. List size verification by GP practices. Understanding the care of patients in nursing homes. 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, based on pseudonymised data: Invoice Validation – Financial validation of activity CCG Budget control Commissioning and performance management Meeting commissioning objectives without compromising patient confidentiality The avoidance of misappropriation of public funds to ensure the ongoing delivery of patient care Risk Stratification 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. Output from the risk stratification tool will provide aggregate reporting of number and percentage of population found to be at risk. Record level output will be available for commissioners (of the CCG), pseudonymised at patient level. 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. 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: Stratify populations based on: disease profiles; conditions currently being treated; current service use; pharmacy use and risk of future overall cost Plan work for commissioning services and contracts Set up capitated budgets Identify health determinants of risk of admission to hospital, or other adverse care outcomes. Data Processor 5 - MSD Healthcare Services 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. Output from the risk stratification tool will provide aggregate reporting of number and percentage of population found to be at risk. Record level output will be available for commissioners (of the CCG), pseudonymised at patient level / aggregate with small number suppression 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. External aggregated reports only with small number suppression will be provided to University of Manchester for the purposes of evaluating the success of the national Test Bed programme Commissioning (Pseudonymised) – SUS and Local Flows Commissioner reporting: Summary by provider view - plan & actuals year to date (YTD). Summary by Patient Outcome Data (POD) view - plan & actuals YTD. Summary by provider view - activity & finance variance by POD. Planned care by provider view - activity & finance plan & actuals YTD. Planned care by POD view - activity plan & actuals YTD. Provider reporting. Statutory returns. Statutory returns - monthly activity return. Statutory returns - quarterly activity return. Delayed discharges. Quality & performance referral to treatment reporting. Readmissions analysis. Production of aggregate reports for CCG Business Intelligence. Production of project / programme level dashboards. Monitoring of acute / community / mental health quality matrix. Clinical coding reviews / audits. Budget reporting down to individual GP Practice level. GP Practice level dashboard reports include high flyers. Commissioning (Pseudonymised) – Mental Health, Maternity, IAPT, CYPHS and DIDS Commissioner reporting: Summary by provider view - plan & actuals year to date (YTD). Summary by Patient Outcome Data (POD) view - plan & actuals YTD. Summary by provider view - activity & finance variance by POD. Planned care by provider view - activity & finance plan & actuals YTD. Planned care by POD view - activity plan & actuals YTD. Provider reporting. Statutory returns. Statutory returns - monthly activity return. Statutory returns - quarterly activity return. Delayed discharges. Quality & performance referral to treatment reporting. Readmissions analysis. Production of aggregate reports for CCG Business Intelligence. Production of project / programme level dashboards. Monitoring of mental health quality matrix. Clinical coding reviews / audits. Budget reporting down to individual GP Practice level. 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 who are resident and registration within the CCG area. Access is limited to those substantive employees of the CCG and its data processors with authorised user accounts used for identification and authentication. Patient level data will not be shared outside of the CCG and Data Processor 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. Invoice Validation (Data Processor 6 - CCG) The Data Services for Commissioners Regional Office (DSCRO), receives a flow of identifiable SUS data from the SUS Repository. 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 any derived fields. Arden & GEM CSU then passes the pseudonymised data securely to the CCG. The CCG conduct the following processing activities for invoice validation purposes: Checking invoiced activity is registered to the Clinical Commissioning Group (CCG) by using the derived commissioner field in SUS and associated with an invoice from the national SUS data flow to validate corresponding records in the backing data flow Once the backing information is received, this will be checked against national NHS and local commissioning policies as well as being checked against system access and reports provided by NHS Digital to confirm the payments are: In line with Payment by Results tariffs Are in relation to patients registered with the CCG GPs or resident within the CCG area. The health care provided should be paid by the CCG in line with CCG guidance.  The CCG are notified that the invoice has been validated and can be paid. Any discrepancies or non-validated invoices are investigated and resolved Risk Stratification Data Processor 1 – Arden and GEM CSU SUS Data is sent from the SUS Repository to the Data Services for Commissioners Regional Office (DSCRO) to the data processor. SUS data identifiable at the level of NHS number regarding hospital admissions, A&E attendances and outpatient attendances is delivered securely from the DSCRO to the data processor. 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 & GEM CSU, who hold the SUS data within the secure Data Centre on N3. Identifiable GP Data is securely sent from the GP system to Arden & GEM CSU. SUS data is linked to GP data in the risk stratification tool by the data processor. 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. 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 5 – MSD Healthcare Services The Data Services for Commissioners Regional Office (DSCRO) obtains a flow of SUS identifiable data for the CCG from the SUS Repository. The DSCRO also obtains identifiable local provider data for the CCG directly from Providers. 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 & GEM CSU, who hold the SUS data within the secure Data Centre on N3. Arden and GEM CSU pseudonymise the data and then pass the pseudonymised, processed, data to MSD Healthcare Services. MSD Healthcare Services receive GP data that has been pseudonymised at source. MSD Healthcare Services link and analyse the data to see patient journeys for pathways or service design, re-design and de-commissioning. Once MSD Healthcare Services has completed the processing, the data is passed to the CCG in pseudonymised form at patient level and as aggregated reports. The developer role is the only role within MSD Healthcare Services that will be given access to the SUS data. User permissions are authorised and assigned by the IG Lead. MSD will not use the data they receive for software development. A separate application is in development to apply for data to assist in the development of software. Pseudonymised – SUS and Local Flows Data Processor 1 – Arden and GEM CSU The Data Services for Commissioners Regional Office (DSCRO) obtains a flow of SUS identifiable data for the CCG from the SUS Repository. The DSCRO also obtains identifiable local provider data for the CCG directly from Providers. 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. 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. Aggregation of required data for CCG management use will be completed by the CSU or the CCG as instructed by the CCG. 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. Data Processor 2 – GMSS (via DP1): The Data Services for Commissioners Regional Office (DSCRO) receives a flow of SUS identifiable data for the CCG from the SUS Repository. The DSCRO also receives identifiable local provider data for the CCG directly from Providers. 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. Arden & GEM CSU then passes the pseudonymised data securely to the Greater Manchester Shared Services (GMSS). GMSS analyse the data to see patient journeys for pathway or service design, re-design and de-commissioning. GMSS then pass the processed pseudonymised data to the CCG 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. Data Processor 3 – AQuA (via DP1): The Data Services for Commissioners Regional Office (DSCRO) receives a flow of SUS identifiable data for the CCG from the SUS Repository. The DSCRO also receives identifiable local provider data for the CCG directly from Providers. 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. 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. AQuA produces aggregate reports only with small number suppression. Only aggregate reports are sent to the CCG. Data Processor 4 – Academic Health Sciences Network (Utilisation Management Team) (SUS Only) (via DP1): The Data Services for Commissioners Regional Office (DSCRO) receives a flow of SUS identifiable data for the CCG from the SUS Repository. 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 and analysis. Arden & GEM CSU then passes the pseudonymised data securely to the Academic Health Service (Utilisation Management Team) (AHSN UMT) 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. AHSN UMT produces aggregate reports only with small number suppression. Only aggregate reports are sent to the CCG. Pseudonymised – Mental Health and IAPT Data Processor 1 – Arden & GEM CSU The 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 MSDS. The 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 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. 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. The CCG analyses the data to see patient journeys for pathway or service design, re-design and de-commissioning Aggregation of required data for CCG management use can be completed by the CSU or the CCG 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. Data Processor 2 – GMSS (via DP1): The 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) the DSCRO also receive a flow of pseudonymised patient level data for each CCG for Improving Access to Psychological Therapies (IAPT) for commissioning purposes The pseudonymised data is securely transferred from the DSCRO to Arden & GEM CSU for the addition of derived fields, linkage of data sets and analysis. Arden & GEM CSU then pass the processed, pseudonymised and linked data to the Greater Manchester Shared Services (GMSS) GMSS analyse and conduct the BI function and then send the Pseudonymised data to the CCG. 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. Data Processor 3 - Advancing Quality Alliance (AQuA) (via DP1): The 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). 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. Arden & GEM CSU then passes the pseudonymised data securely to Advancing Quality Alliance (AQuA). 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. AQuA produces aggregate reports only with small number suppression. Only aggregate reports are sent to the CCG.

Objectives:

"Objective for processing: Data Processor 1 – Arden and GEM CSU conduct Risk Stratification as instructed by the CCG. The CSU also processes SUS, Local Provider flows, mental health, IAPT, MSDS, CYPHS and DIDS for the purpose of commissioning. Data Processor 2 - 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. Data Processor 3 - Advancing Quality Alliance (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. Data Processor 4 - 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. 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. The amended agreement is to include a new data processor for the purpose of Risk Stratification purposes only Data Processor 5 - MSD Healthcare Services Data Processor 5 - MSD Healthcare Services - The data processors wil construct a technological platform to link pseudonymised clinical and administrative data across primary and secondary care. These data from primary care and secondary care, provided by NHS Digital will be linked with a common Pseudonym. Lower Super Output Area deprivation data will be linked at geography level. Heywood Middleton Rochdale CCG is working with MSD to deliver an NHS England national testbed programme acting as a data processor for the CCG. More information can be found in https://www.england.nhs.uk/ourwork/innovation/test-beds/ltc-prog/ MSD Healthcare Services is a division of Merck Sharp and Dohme Limited. Data will only be processed and stored at the addresses listed within the application. The territory of use is limited to England and therefore data will not be sent to or accessed from geographical areas outside of this territory. Data Processor 6 – Heywood, Middleton and Rochdale CCG are conducting Invoice Validation functions using pseudonymised SUS and Local Provider data. There will be no dissemination involving the new data processor until appropriate data destruction has taken place at the former data processor. Invoice Validation The CCG receives pseudonymised SUS and local provider flows data. These data are required for the purpose of invoice validation and will be used to confirm the accuracy of backing-data sets and will not be shared outside of the CCG. Data cannot be matched on NHS Number as this is not present in the data, but can be used to validate invoices to a level that is acceptable to the CCG. If there is no data in SUS or local provider flows data that can be used to validate the invoice, another data set is used from providers which shows practice / area codes to confirm the patient is from the CCG area in order to pay an invoice. 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, 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.