NHS Digital Data Release Register - reformatted

NHS Salford CCG

Project 1 — DARS-NIC-193381-L9V3D

Opt outs honoured: No - data flow is not identifiable, Yes - patient objections upheld

Sensitive: Sensitive

When: 2018/10 — 2019/01.

Repeats: Frequent Adhoc Flow

Legal basis: Health and Social Care Act 2012 – s261(1) and s261(2)(b)(ii), Health and Social Care Act 2012 – s261(7)

Categories: Anonymised - ICO code compliant, Identifiable

Datasets:

  • Acute-Local Provider Flows
  • Ambulance-Local Provider Flows
  • Children and Young People Health
  • Community-Local Provider Flows
  • Demand for Service-Local Provider Flows
  • Diagnostic Imaging Dataset
  • Diagnostic Services-Local Provider Flows
  • Emergency Care-Local Provider Flows
  • Experience, Quality and Outcomes-Local Provider Flows
  • Improving Access to Psychological Therapies Data Set
  • Maternity Services Data Set
  • Mental Health and Learning Disabilities Data Set
  • Mental Health Minimum Data Set
  • Mental Health Services Data Set
  • Mental Health-Local Provider Flows
  • Other Not Elsewhere Classified (NEC)-Local Provider Flows
  • Population Data-Local Provider Flows
  • Primary Care Services-Local Provider Flows
  • Public Health and Screening Services-Local Provider Flows
  • SUS for Commissioners

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 North West region, detailed within the data minimisation. The following pseudonymised datasets are required to provide intelligence to support commissioning of health services: - Secondary Uses Service (SUS+) - Local Provider Flows o Acute 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 Processing for commissioning will be conducted by Arden and GEM Commissioning Support Unit Salford Royal NHS Foundation Trust in their capacity as Data Processor will, in addition, utilise internal teams as follows: Advancing Quality Alliance (AQuA) provide support for a range of quality improvement programmes across regions of CCGs, (listed within the Data Sharing Agreement), undertaking analyses and producing aggregate reports for the CCGs. AQuA includes the NW Advancing Quality (AQ) Programme, which was set up to help drive quality improvements across the region. The AQ programme focusses on several clinical focus areas which affect many patients in the region. These evidence based clinical focus areas fall into categories such as cardiac conditions, orthopaedics (for example Hip and Knee replacement surgery) and respiratory conditions. The overarching aim of the AQ programme is to identify if specific treatment pathways commissioned by CCGs and delivered by the trusts are meeting recommended guidelines for quality, and through working with the trusts and CCGs, improving the performance of the pathways and ensuring patients get the most appropriate treatment for their condition regardless of which hospital they are treated in. To enable them to undertake this work, AQuA requires pseudonymised SUS data and local provider flows which have been specified for the AQ Programme. - Secondary Uses Service (SUS) - Local Provider Flows o Acute

Expected Benefits:

The following measurable benefits are expected through each team AQuA The AQ Programme is a Quality Improvement and Audit programme that identifies a set of robust, evidence based clinical quality measures for given focus areas. The measures represent a standard clinical practice that providers agree patients in the relevant cohort should receive. AQ are currently working with 8 clinical focus areas and each focus area has between 5 and 10 clinical measures. Each measure would have a beneficial outcome. An evaluation of the early AQ programme evidenced that the pneumonia measures reduced mortality within the Northwest region (N Engl J Med 2012; 367:1821-8). Not all measures have such dramatic outcomes; some measures may improve diagnostic speed or improve patient education. All the measures are directed at ensuring consistency of care, improving implementation of care year on year, and reducing inequality of care from trust to trust. The detailed information collected can be used to identify areas where care may fall short within a pathway or amongst trusts and be used as the basis for quality improvement. For example, it was identified through analysis of the data that one NW trust was consistently missing the delivery of antibiotics within 4 hours. The ‘CFA audit data’ was used as the basis to review cases and map the processes, and identify the gaps. The trust identified that prescriptions were being written in A&E, but the dosage was not being delivered in A&E. The process was then updated to ensure that the dosage would be delivered before the patients left the A&E for the ward. Once care has been improved across the region within a clinical focus area and new processes are established, AQ can replace a CFA and work on establishing improvements in new areas. Ongoing benefits for the CCGs therefore include ensuring equitable standards of care for their patients. In addition, the AQ programme delivers impartial monitoring of standard quality measures that are consistently delivered across annual periods showing year on year improvement and adherence to robust clinical standards. AQ continues to build on current progress with its underpinning values of detailed and evidence-based pathways, strong clinical guidance, peer level networking and support underpinned by excellent data collection with regular robust reporting.

Outputs:

The following outputs are expected through each team: AQuA AQuA will use the data to produce a range of reports that will be made available to both commissioners and providers, with specific attention on the clinical focus areas (CFA). All reports will be at an aggregate level and examples include; 1) Monthly coding quality reports to evaluate the completeness of diagnostic coding in the SUS data. The purpose of this report is to ensure that the source data is fit-for-purpose to create the AQ Clinical Focus Area (CFA) populations accurately. 2) Monthly benchmarking reports reporting on the data collection quality of the AQ data. The purpose of this report is to ensure that provider trusts are collecting suitable information in their local data for the identified AQ populations. 3) Monthly benchmarking reports using the collected local CFA data to evaluate the delivery of the AQ CFA measures. The purpose of this report is to allow the provider trusts and CCGs to see the percentage of the AQ population receiving each AQ measure within each trust and compare the performance to other participating trusts. 4) Bi-annual public reports/summary benchmarking reports will be published on the Advancing Quality Alliance website. •

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 CCGs as follows: NHS Bolton CCG NHS Bury CCG NHS Heywood, Middleton and Rochdale CCG NHS Manchester CCG NHS Oldham CCG NHS Salford CCG NHS Stockport CCG NHS Tameside & Glossop CCG NHS Trafford CCG NHS Wigan CCG NHS Eastern Cheshire CCG NHS Halton CCG NHS Knowsley CCG NHS Liverpool CCG NHS South Cheshire CCG NHS South Sefton CCG NHS Southport & Formby CCG NHS St Helens CCG NHS Vale Royal CCG NHS Warrington CCG NHS West Cheshire CCG NHS Wirral CCG NHS Blackburn & Darwin CCG NHS Blackpool CCG NHS Chorley & South Ribble CCG NHS East Lancashire CCG NHS Fylde & Wyre CCG NHS Greater Preston CCG NHS Morecambe Bay CCG NHS West Lancashire CCG NHS Cumbria CCG Patient level data will not be shared outside of the Data Controller. All access to data is managed under Roles-Based Access Controls 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 required by 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 i.e: 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. The applicant will not link the data further and the only data linkages are those permitted under this application / Data Sharing Agreement. Data will not be used for reidentification purposes. All access to data is audited 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 Data quality management and pseudonymisation is completed within the DSCRO and is then disseminated as follows: 1. Pseudonymised SUS+ and Local Provider data only is securely transferred from the DSCRO to Arden and Greater East Midlands Commissioning Support Unit. 2. Arden and Greater East Midlands Commissioning Support Unit add further derived fields and perform additional checks for data quality issues such as local duplication of records, or adjustments for known data recording issues, and prepare the data for further use. 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 Salford Royal NHS Foundation Trust. 5. Salford Royal NHS Foundation Trust 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 6. Data is accessed by two teams within Salford Royal NHS Foundation Trust: - Advancing Quality Alliance (AQuA) 7. Access is via team specific role-based access only and is specific to each team as: - Advancing Quality Alliance (AQuA) team members have access to SUS+ and local provider flow data only. 8. Aggregation of the data will be completed by Arden and Greater East Midlands Commissioning Support Unit.or (Advancing Quality Alliance (AQuA) within Salford Royal NHS Foundation Trust. 9. Patient level data will not be shared outside of (Advancing Quality Alliance (AQuA) within Salford Royal NHS Foundation Trust and will only be shared within the individual teams on a need to know basis, as per the purposes stipulated within the Data Sharing Agreement. External aggregated reports only with small number suppression will be shared with the 31 CCGs listed in line with NHS Digital guidance applicable to each data set.


Project 2 — DARS-NIC-193456-W3M0H

Opt outs honoured: No - data flow is not identifiable (Does not include the flow of confidential data)

Sensitive: Sensitive

When: 2019/01 — 2019/01.

Repeats: Frequent Adhoc Flow

Legal basis: Health and Social Care Act 2012 – s261(1) and s261(2)(b)(ii)

Categories: Anonymised - ICO code compliant

Datasets:

  • Acute-Local Provider Flows
  • SUS for Commissioners

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 North West region, detailed within the data minimisation. The following pseudonymised datasets are required to provide intelligence to support commissioning of health services: - Secondary Uses Service (SUS+) - Local Provider Flows o Acute 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 Processing for commissioning will be conducted by Arden and GEM Commissioning Support Unit Salford Royal NHS Foundation Trust in their capacity as Data Processor will, in addition, utilise internal teams as follows: Advancing Quality Alliance (AQuA) provide support for a range of quality improvement programmes across regions of CCGs, (listed within the Data Sharing Agreement), undertaking analyses and producing aggregate reports for the CCGs. AQuA includes the NW Advancing Quality (AQ) Programme, which was set up to help drive quality improvements across the region. The AQ programme focusses on several clinical focus areas which affect many patients in the region. These evidence based clinical focus areas fall into categories such as cardiac conditions, orthopaedics (for example Hip and Knee replacement surgery) and respiratory conditions. The overarching aim of the AQ programme is to identify if specific treatment pathways commissioned by CCGs and delivered by the trusts are meeting recommended guidelines for quality, and through working with the trusts and CCGs, improving the performance of the pathways and ensuring patients get the most appropriate treatment for their condition regardless of which hospital they are treated in. To enable them to undertake this work, AQuA requires pseudonymised SUS data and local provider flows which have been specified for the AQ Programme. - Secondary Uses Service (SUS) - Local Provider Flows o Acute

Expected Benefits:

The following measurable benefits are expected through each team: AQuA The AQ Programme is a Quality Improvement and Audit programme that identifies a set of robust, evidence based clinical quality measures for given focus areas. The measures represent a standard clinical practice that providers agree patients in the relevant cohort should receive. AQ are currently working with 8 clinical focus areas and each focus area has between 5 and 10 clinical measures. Each measure would have a beneficial outcome. An evaluation of the early AQ programme evidenced that the pneumonia measures reduced mortality within the Northwest region (N Engl J Med 2012; 367:1821-8). Not all measures have such dramatic outcomes; some measures may improve diagnostic speed or improve patient education. All the measures are directed at ensuring consistency of care, improving implementation of care year on year, and reducing inequality of care from trust to trust. The detailed information collected can be used to identify areas where care may fall short within a pathway or amongst trusts and be used as the basis for quality improvement. For example, it was identified through analysis of the data that one NW trust was consistently missing the delivery of antibiotics within 4 hours. The ‘CFA audit data’ was used as the basis to review cases and map the processes, and identify the gaps. The trust identified that prescriptions were being written in A&E, but the dosage was not being delivered in A&E. The process was then updated to ensure that the dosage would be delivered before the patients left the A&E for the ward. Once care has been improved across the region within a clinical focus area and new processes are established, AQ can replace a CFA and work on establishing improvements in new areas. Ongoing benefits for the CCGs therefore include ensuring equitable standards of care for their patients. In addition, the AQ programme delivers impartial monitoring of standard quality measures that are consistently delivered across annual periods showing year on year improvement and adherence to robust clinical standards. AQ continues to build on current progress with its underpinning values of detailed and evidence-based pathways, strong clinical guidance, peer level networking and support underpinned by excellent data collection with regular robust reporting.

Outputs:

The following outputs are expected through each team: AQuA AQuA will use the data to produce a range of reports that will be made available to both commissioners and providers, with specific attention on the clinical focus areas (CFA). All reports will be at an aggregate level and examples include; 1) Monthly coding quality reports to evaluate the completeness of diagnostic coding in the SUS data. The purpose of this report is to ensure that the source data is fit-for-purpose to create the AQ Clinical Focus Area (CFA) populations accurately. 2) Monthly benchmarking reports reporting on the data collection quality of the AQ data. The purpose of this report is to ensure that provider trusts are collecting suitable information in their local data for the identified AQ populations. 3) Monthly benchmarking reports using the collected local CFA data to evaluate the delivery of the AQ CFA measures. The purpose of this report is to allow the provider trusts and CCGs to see the percentage of the AQ population receiving each AQ measure within each trust and compare the performance to other participating trusts. 4) Bi-annual public reports/summary benchmarking reports will be published on the Advancing Quality Alliance website.

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. The Data Controller and any Data Processor will only have access to records of patients of residence and registration within the CCGs as follows: NHS Bolton CCG NHS Bury CCG NHS Heywood, Middleton and Rochdale CCG NHS Manchester CCG NHS Oldham CCG NHS Salford CCG NHS Stockport CCG NHS Tameside & Glossop CCG NHS Trafford CCG NHS Wigan CCG NHS Eastern Cheshire CCG NHS Halton CCG NHS Knowsley CCG NHS Liverpool CCG NHS South Cheshire CCG NHS South Sefton CCG NHS Southport & Formby CCG NHS St Helens CCG NHS Vale Royal CCG NHS Warrington CCG NHS West Cheshire CCG NHS Wirral CCG NHS Blackburn & Darwin CCG NHS Blackpool CCG NHS Chorley & South Ribble CCG NHS East Lancashire CCG NHS Fylde & Wyre CCG NHS Greater Preston CCG NHS Morecambe Bay CCG NHS West Lancashire CCG NHS North Cumbria CCG   Data can only be stored at the addresses listed under storage addresses.   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   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.   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 Minimisation in relation to the data sets listed within section 3 are listed below. This also includes the purpose on which they would be applied - For the purpose of Commissioning: • Patients who are normally registered and/or resident within the commissioner (including historical activity where the patient was previously registered or resident in another commissioner). and/or • Patients treated by a provider where the commissioner is the host/co-ordinating commissioner and/or has the primary responsibility for the provider services in the local health economy – this only relates to both national and local flows. and/or • Activity identified by the provider and recorded as such within national systems (such as SUS+) as for the attention of the commissioner - this only relates to both national and local flows. The above relates to data requested only (Table 3B). : • CCGs of residence and/or registration. For clarity, any access by Ilkeston Community Hospital to data held under this agreement would be considered a breach of the agreement. This includes granting of access to the database[s] containing the data 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 Data quality management and pseudonymisation is completed within the DSCRO and is then disseminated as follows: 1. Pseudonymised SUS+ and Local Provider data only is securely transferred from the DSCRO to Arden and Greater East Midlands Commissioning Support Unit. 2. Arden and Greater East Midlands Commissioning Support Unit add further derived fields and perform additional checks for data quality issues such as local duplication of records, or adjustments for known data recording issues, and prepare the data for further use. 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 Salford Royal NHS Foundation Trust. 5. Salford Royal NHS Foundation Trust 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 6. Data is accessed by two teams within Salford Royal NHS Foundation Trust: - Advancing Quality Alliance (AQuA) 7. Access is via team specific role-based access only and is specific to each team as: - Advancing Quality Alliance (AQuA) team members have access to SUS+ and local provider flow data only. 8. Aggregation of the data will be completed by Arden and Greater East Midlands Commissioning Support Unit.or (Advancing Quality Alliance (AQuA) within Salford Royal NHS Foundation Trust. 9. Patient level data will not be shared outside of (Advancing Quality Alliance (AQuA) within Salford Royal NHS Foundation Trust and will only be shared within the individual teams on a need to know basis, as per the purposes stipulated within the Data Sharing Agreement. External aggregated reports only with small number suppression will be shared with the 31 CCGs listed in line with NHS Digital guidance applicable to each data set.


Project 3 — DARS-NIC-76770-F0J5W

Opt outs honoured: N, Y, No - data flow is not identifiable, Yes - patient objections upheld (Section 251)

Sensitive: Sensitive

When: 2018/06 — 2019/01.

Repeats: Frequent adhoc flow, Frequent Adhoc Flow

Legal basis: Health and Social Care Act 2012 – s261(1) and s261(2)(b)(ii), Section 251 approval is in place for the flow of identifiable data, National Health Service Act 2006 - s251 - 'Control of patient information'.

Categories: Anonymised - ICO code compliant, Identifiable

Datasets:

  • Acute-Local Provider Flows
  • Ambulance-Local Provider Flows
  • Children and Young People Health
  • Community-Local Provider Flows
  • Demand for Service-Local Provider Flows
  • Diagnostic Imaging Dataset
  • Diagnostic Services-Local Provider Flows
  • Emergency Care-Local Provider Flows
  • Experience, Quality and Outcomes-Local Provider Flows
  • Improving Access to Psychological Therapies Data Set
  • Maternity Services Data Set
  • Mental Health and Learning Disabilities Data Set
  • Mental Health Minimum Data Set
  • Mental Health Services Data Set
  • Mental Health-Local Provider Flows
  • Other Not Elsewhere Classified (NEC)-Local Provider Flows
  • Population Data-Local Provider Flows
  • Primary Care Services-Local Provider Flows
  • Public Health and Screening Services-Local Provider Flows
  • SUS for Commissioners

Yielded Benefits:

Improved planning to understand patient flows Helped to identify high risk patients who regularly attend and admit to hospital. Supports the integrated commissioning programme of work

Objectives:

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 focus for future demands by enabling commissioners to prepare plans for 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 GEM CSU 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) The pseudonymised data is required to for the following purposes: § Population health management: • Understanding the interdependency of care services • Targeting care more effectively • Using value as the redesign principle § Data Quality and Validation – allowing data quality checks on the submitted data § Thoroughly investigating the needs of the population, to ensure the right services are available for individuals when and where they need them § Understanding cohorts of residents who are at risk of becoming users of some of the more expensive services, to better understand and manage those needs § Monitoring population health and care interactions to understand where people may slip through the net, or where the provision of care may be being duplicated § Modelling activity across all data sets to understand how services interact with each other, and to understand how changes in one service may affect flows through another § Service redesign § Health Needs Assessment – identification of underlying disease prevalence within the local population § Patient stratification and predictive modelling - to identify specific patients at risk of requiring hospital admission and other avoidable factors such as risk of falls, computed using algorithms executed against linked de-identified data, and identification of future service delivery models The pseudonymised data is required to ensure that analysis of health care provision can be completed to support the needs of the health profile of the population within the CCG area based on the full analysis of multiple pseudonymised datasets. Processing for commissioning will be conducted by Arden and GEM CSU, Greater Manchester Shared Services (GMSS), Advancing Quality Alliance (AQuA) and The Academic Health Sciences Network (Utilisation Management Team).

Expected 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 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. 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:

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

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. 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 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. 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) The DSCRO (part of NHS Digital) will apply Type 2 objections before any identifiable data leaves the DSCRO. 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. 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. Risk Stratification (Arden and GEM CSU) 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 GEM CSU, 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 GEM CSU. 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 GEM CSU has completed the processing, the CCG can access the online system via a secure connection to access the data pseudonymised at patient leveland as aggregated reports. 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. Diagnostic Imaging Data Set (DIDS) Data quality management and pseudonymisation is completed within the DSCRO and is then disseminated as follows: Data Processor 1 – Arden and GEM CSU 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) and Diagnostic Imaging data (DIDS) only is securely transferred from the DSCRO to Arden and GEM CSU. 2. Arden and GEM 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 GEM CSU 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 GEM CSU or the CCG as instructed by the CCG. 6. Patient level data will not be shared outside of the CCG and will only be shared within the CCG on a need to know basis, as per the purposes stipulated within the Data Sharing Agreement. External aggregated reports only with small number suppression can be shared as set out within NHS Digital guidance applicable to each data set. Data Processor 2 - Greater Manchester Shared Services (GMSS) 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) and Diagnostic Imaging data (DIDS) only is securely transferred from the DSCRO to Arden GEM CSU. 2. Arden and GEM add derived fields, link data and provide analysis. 3. Allowed linkage is between the data sets contained within point 1. 4. Arden & GEM CSU then pass the processed, pseudonymised and linked data to the Greater Manchester Shared Services (GMSS) 5. GMSS analyse and conduct the BI function and then send the Pseudonymised data to the CCG. 4. GMSS then pass the processed, pseudonymised and linked 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 can be shared as set out within NHS Digital guidance applicable to each data set. Data Processor 3 - Advancing Quality Alliance (AQuA) 1. Pseudonymised SUS+, Local Provider data, Mental Health data (MHSDS, MHMDS, MHLDDS) only is securely transferred from the DSCRO to Arden and GEM CSU. 2. Arden and GEM CSU add derived fields, link data and provide analysis. 3. Allowed linkage is between the data sets contained within point 1. 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. 4. AquA then pass the processed, pseudonymised and linked 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 can be shared as set out within NHS Digital guidance applicable to each data set. Data Processor 4 – Academic Health Sciences Network (Utilisation Management Team) (SUS Only) 1. Pseudonymised SUS+ only is securely transferred from the DSCRO to Arden and GEM CSU. 2. Arden and GEM CSU add derived fields, link data and provide analysis. 3. Allowed linkage is between the data sets contained within point 1. 4. Arden & GEM CSU then passes the pseudonymised data securely to the Academic Health Service (Utilisation Management Team) (AHSN UMT) 5. The AHSN UMT receive pseudonymised SUS data for Greater Manchester patients. They analyse the data to look at processes rather than patients, for example, A&E performance, process times, bed days as well as ‘deep dives’ to support clinical reviews for CCGs. 6. AHSN UMT then pass the processed, pseudonymised and linked data to the CCG. 7. Patient level data will not be shared outside of the CCG and will only be shared within the CCG on a need to know basis, as per the purposes stipulated within the Data Sharing Agreement. External aggregated reports only with small number suppression can be shared as set out within NHS Digital guidance applicable to each data set.


Project 4 — NIC-47177-D5J3V

Opt outs honoured: Y, N

Sensitive: Sensitive

When: 2016/12 — 2017/08.

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

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.

Expected 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.


Project 5 — NIC-76770-F0J5W

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

Datasets:

  • SUS data (Accident & Emergency, Admitted Patient Care & Outpatient)
  • SUS for Commissioners
  • Public Health and Screening Services-Local Provider Flows
  • Primary Care Services-Local Provider Flows
  • Population Data-Local Provider Flows
  • Other Not Elsewhere Classified (NEC)-Local Provider Flows
  • Mental Health-Local Provider Flows
  • 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

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. Data Processor 5 – Salford CCG are conducting Invoice Validation functions using pseudonymised SUS and Local Provider data. 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. 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.

Expected Benefits:

Expected measurable benefits to health and/or social care including target date: Invoice Validation – Salford CCG only 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 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:

Specific outputs expected, including target date: 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. 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. 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:

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 specified within the Data Minimisation Efforts within Annex A of the Data Sharing Agreement. Access is limited to those substantive employees with authorised user accounts used for identification and authentication. Patient level data will not be shared outside of the CCG unless it is for the purpose of Direct Care, where it may be shared only with those health professionals who have a legitimate relationship with the patient and a legitimate reason to access the data. CCGs should work with general practices within their CCG to help them fulfil data controller responsibilities regarding flow of identifiable data into risk stratification tools. No record level data will be linked other than as specifically detailed within this application/agreement. Data will only be shared with those parties listed and will only be used for the purposes laid out in the application/agreement. The data to be released from NHS Digital will not be national data, but only that data relating to the specific locality of interest of the applicant. The DSCRO (part of NHS Digital) will apply Type 2 objections before any identifiable data leaves the DSCRO. Invoice Validation (Data Processor 5 - Salford CCG) 1. The Data Services for Commissioners Regional Office (DSCRO), receives a flow of identifiable SUS data from the SUS Repository. 2. Data quality management and pseudonymisation of data is completed by the DSCRO and the pseudonymised data is then passed securely to Arden & GEM CSU for the addition of any derived fields. 3. Arden & GEM CSU then passes the pseudonymised data securely to the CCG. 4. The CCG conduct the following processing activities for invoice validation purposes: a. 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 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. Are in relation to patients registered with the CCG GPs or resident within the CCG area. iii. The health care provided should be paid by the CCG in line with CCG guidance.  5. 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) 1. SUS Data is sent from the SUS Repository to the 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 the DSCRO to the data processor. 3. 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. 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 1 – Arden and GEM CSU 1. 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. 2. Data quality management and pseudonymisation of data is completed by the DSCRO and the pseudonymised data is then passed securely to Arden and GEM CSU for the addition of derived fields, linkage of data sets and analysis. Allowed linkage is between SUS data sets and local flows. 3. Arden and GEM CSU then pass the processed, pseudonymised and linked data to the CCG. The CCG analyse the data to see patient journeys for pathways or service design, re-design and de-commissioning. 4. Aggregation of required data for CCG management use will be completed by the CSU or the CCG as instructed by the CCG. 5. Patient level data will not be shared outside of the CCG and will only be shared within the CCG on a need to know basis, as per the purposes stipulated within the Data Sharing Agreement. External aggregated reports only with small number suppression can be shared. Data Processor 2 – GMSS (via DP1): 1. 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. 2. Data quality management and pseudonymisation of data is completed by the DSCRO and the pseudonymised data is then passed securely to Arden & GEM CSU for the addition of derived fields, linkage of data sets and analysis. 3. Arden & GEM CSU then 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 can be shared. Data Processor 3 – AQuA (via DP1): 1. 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. 2. Data quality management and pseudonymisation of data is completed by the DSCRO and the pseudonymised data is then passed securely to Arden & GEM CSU for the addition of derived fields, linkage of data sets and analysis. 3. Arden & GEM CSU then 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. Only aggregate reports are sent to the CCG. Data Processor 4 – Academic Health Sciences Network (Utilisation Management Team) (SUS Only) (via DP1): 1. The 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 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. 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. Only aggregate reports are sent to the CCG. Pseudonymised – Mental Health and IAPT Data Processor 1 – Arden & GEM CSU 1. 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 2. Data quality management and pseudonymisation of data is completed by the DSCRO and the pseudonymised data is then passed securely to Arden & GEM CSU for the addition of derived fields, linkage of data sets and analysis. 3. Arden & GEM CSU then pass the processed, pseudonymised and linked data to the CCG who analyse the data to see patient journeys for pathways or service design, re-design and de-commissioning. 4. The CCG analyses the data to see patient journeys for pathway or service design, re-design and de-commissioning 5. Aggregation of required data for CCG management use can be completed by the CSU or the CCG 6. Patient level data will not be shared outside of the CCG and will only be shared within the CCG on a need to know basis, as per the purposes stipulated within the Data Sharing Agreement. External aggregated reports only with small number suppression can be shared. Data Processor 2 – GMSS (via DP1): 1. 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 2. 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. 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 3 - Advancing Quality Alliance (AQuA) (via DP1): 1. 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). 2. Data quality management and pseudonymisation of data is completed by the DSCRO and the pseudonymised data is then passed securely to Arden & GEM CSU for the addition of derived fields, linkage of data sets and analysis. 3. Arden & GEM CSU then 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. Only aggregate reports are sent to the CCG.