NHS Digital Data Release Register - reformatted

NHS Sheffield Ccg

Project 1 — NIC-52984-Q1R2T

Opt outs honoured: Y, N

Sensitive: Sensitive

When: 2016/12 — 2017/11.

Repeats: Ongoing

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

Categories: Identifiable, Anonymised - ICO code compliant

Datasets:

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

Benefits:

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

Outputs:

Risk Stratification 1. As part of the risk stratification processing activity detailed above, GPs have access to the risk stratification tool which highlights patients for whom the GP is responsible. The only identifier derived from SUS available to GPs is the NHS numbers of their own patients. Any further identification of the patients is derived from the GP data sourced from their own systems. 2. Output from the risk stratification tool will provide aggregate reporting of number and percentage of population in risk bandings. 3. Record level output will be available for commissioners 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. 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:

Yorkshire DSCRO will apply Type 2 objections (from 14th October 2016 onwards) before any identifiable data leaves the DSCRO. Risk Stratification 1. Identifiable SUS data is obtained from the SUS Repository to Yorkshire Data Services for Commissioners Regional Office (DSCRO). 2. Data quality management and standardisation of data is completed by Yorkshire DSCRO and the data identifiable at the level of NHS number is transferred securely to North of England CSU, who hold the SUS data within the secure NECS network storage. 3. Identifiable GP Data is securely sent from the GP system to North of England 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 derived from SUS available to GPs is the NHS numbers of their own patients. Any further identification of the patients is derived from the GP data sourced from their own systems. 6. North of England 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 North of England CSU has completed the processing, the CCG can access the online system via a secure network connection to access the data pseudonymised at patient level Pseudonymised – SUS and Local Flows 1. Yorkshire Data Services for Commissioners Regional Office (DSCRO) obtains a flow of SUS identifiable data for the CCG from the SUS Repository. Yorkshire 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 North of England CSU for the addition of derived fields, linkage of data sets and analysis. Allowed linkage is between SUS data sets and local flows 3. North of England CSU then pass the processed, pseudonymised and linked data to the CCG. The CCG analyse the data to see patient journeys for pathways or service design, re-design and de-commissioning. 4. Aggregation of required data for CCG management use will be completed by the CSU or the CCG as instructed by the CCG. 5. Patient level data will not be shared outside of the CCG and will only be shared within the CCG on a need to know basis, as per the purposes stipulated within the Data Sharing Agreement. External aggregated reports only with small number suppression in line with the HES analysis guide can be shared where contractual arrangements are in place. Pseudonymised – Mental Health, MSDS, IAPT, CYPHS and DIDS 1. Yorkshire Data Services for Commissioners Regional Office (DSCRO) obtains a flow of data identifiable at the level of NHS number for Mental Health (MHSDS, MHMDS, MHLDDS), Maternity (MSDS), Improving Access to Psychological Therapies (IAPT), Child and Young People’s Health (CYPHS) and Diagnostic Imaging (DIDS) for commissioning purposes. 2. Data quality management and pseudonymisation of data is completed by Yorkshire DSCRO and the pseudonymised data is then passed securely to North of England CSU for the addition of derived fields and analysis. 3. North of England CSU then pass the processed, pseudonymised data to the CCG. 4. The CCG analyses the data to see patient journeys for pathway or service design, re-design and de-commissioning 5. Aggregation of required data for CCG management use will be completed by the CSU or the CCG as instructed by the CCG 6. Patient level data will not be shared outside of the CCG and will only be shared within the CCG on a need to know basis, as per the purposes stipulated within the Data Sharing Agreement. External aggregated reports only with small number suppression can be shared where contractual arrangements are in place.

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 CCG commissions 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 NHS Digital will not be national data, but only that data relating to the specific locality of interest of the applicant.


Project 2 — NIC-89613-L9D8C

Opt outs honoured: N, Y

Sensitive: Sensitive

When: 2018/03 — 2018/05.

Repeats: Ongoing

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

Categories: Anonymised - ICO code compliant, Identifiable

Datasets:

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

Benefits:

Invoice Validation 1) Addressing poor data quality issues 2) Production of reports for business intelligence 3) Budget reporting 4) Validation of invoices for non-contracted events Risk Stratification 1) As part of the risk stratification processing activity detailed above, GPs have access to the risk stratification tool which highlights patients for whom the GP is responsible and have been classed as at risk. The only identifier available to GPs is the NHS numbers of their own patients. Any further identification of the patients will be completed by the GP on their own systems. 2) Output from the risk stratification tool will provide aggregate reporting of number and percentage of population found to be at risk. 3) Record level output will be available for commissioners (of the CCG) pseudonymised at patient level. 4) GP Practices will be able to view the risk scores for individual patients with the ability to display the underlying SUS data for the individual patients when it is required for direct care purposes by someone who has a legitimate relationship with the patient. 5) The CCG will be able to target specific patient groups and enable clinicians with the duty of care for the patient to offer appropriate interventions. The CCG will also be able to: o Stratify populations based on: disease profiles; conditions currently being treated; current service use; pharmacy use and risk of future overall cost o Plan work for commissioning services and contracts o Set up capitated budgets o Identify health determinants of risk of admission to hospital, or other adverse care outcomes. Commissioning Commissioner reporting: o Summary by provider view - plan & actuals year to date (YTD). o Summary by Patient Outcome Data (POD) view - plan & actuals YTD. o Summary by provider view - activity & finance variance by POD. o Planned care by provider view - activity & finance plan & actuals YTD. o Planned care by POD view - activity plan & actuals YTD. o Provider reporting. o Statutory returns. o Statutory returns - monthly activity return. o Statutory returns - quarterly activity return. o Delayed discharges. o 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. Specific outputs related to the work of the following Data Processors: Data Processor 2 – Medeanalytics 1) The Medeanalytics processing will generate an output of predictive risk scores at the person level calculated from predictor parameters obtained from SUS, primary care and local flows. 2) The aim is to calculate a range of different predictive scores that would be useful to inform patients’ direct care, where validated algorithms are available. Such predictive scores would include the risk of hospital admission, electronic frailty index and the risk of admission to long term residential care. 3) The risk scores would be augmented with other contextual information relevant to the care process in the format of a care dashboard, such as diagnoses of long term conditions and relevant service activity. Data Processor 3 – Sheffield Hallam University 1) The university undertakes commissioned projects on behalf of the CCG to evaluate pilots and similar schemes to inform commissioning / investment decisions – generally where the CCG does not have in-house expertise – and this will involve data processing and analytics. Generally, the CCG does not solely work with a single university data processor, because of a) Fair Trading considerations, and b) each university offers quite different specialisations and expertise, the specifics of which in relation to this application are set out below. 2) The general outputs from data processing by the university includes aggregated descriptive and interferential statistics to ascertain outcome and impact in such evaluations, as well as health economic descriptors to understand cost-utility or cost-effectiveness. 3) The CCG works with Sheffield Hallam University principally in relation to projects involving the healthcare workforce (as a training institution for nurses and therapy professions) and those involving community healthcare services. 4) Specific outputs will focus on the effectiveness and cost-effectiveness of options involving the healthcare professions and teams (most often the nursing and therapy workforce), multidisciplinary working, and community healthcare services. Data Processor 4 – University of Sheffield 1) The university undertakes commissioned projects on behalf of the CCG to evaluate pilots and similar schemes to inform commissioning / investment decisions – generally where the CCG does not have in-house expertise – and this will involve data processing and analytics. Generally, the CCG does not solely work with a single university data processor, because of a) Fair Trading considerations, and b) each university offers quite different specialisations and expertise, the specifics of which in relation to this application are set out below. 2) The general outputs from data processing by the university includes aggregated descriptive and interferential statistics to ascertain outcome and impact in such evaluations, as well as health economic descriptors to understand cost-utility or cost-effectiveness. 3) The University of Sheffield, School of Health & Related Research (ScHARR) are partners in the Yorkshire & The Humber Collaboration for Leadership in Applied Health Research and Care (CLAHRC). This includes the evaluation on behalf of the CCG of several current and forthcoming national pilots taking place in the city. 4) The CCG works with the University of Sheffield principally in relation to mental health & wellbeing, secondary care services, urgent & emergency care, primary care services and health technologies. 5) Specific outputs will focus on the effectiveness and cost-effectiveness of options involving the services set out in item 2. Data Processor 5 – Attain Health Management Services Ltd 1) Attain has been appointed to assist the CCG with its planning for Sustainability & Transformation Plans; work that is focused on Sheffield-based tertiary services having larger geographic catchments, principally acute stroke and children’s secondary care services. 2) Outputs from data processing will comprise consolidated activity and commissioner expenditure in relation to these services.

Outputs:

Invoice Validation 1) Addressing poor data quality issues 2) Production of reports for business intelligence 3) Budget reporting 4) Validation of invoices for non-contracted events Risk Stratification 1) As part of the risk stratification processing activity detailed above, GPs have access to the risk stratification tool which highlights patients for whom the GP is responsible and have been classed as at risk. The only identifier available to GPs is the NHS numbers of their own patients. Any further identification of the patients will be completed by the GP on their own systems. 2) Output from the risk stratification tool will provide aggregate reporting of number and percentage of population found to be at risk. 3) Record level output will be available for commissioners (of the CCG) pseudonymised at patient level. 4) GP Practices will be able to view the risk scores for individual patients with the ability to display the underlying SUS data for the individual patients when it is required for direct care purposes by someone who has a legitimate relationship with the patient. 5) The CCG will be able to target specific patient groups and enable clinicians with the duty of care for the patient to offer appropriate interventions. The CCG will also be able to: o Stratify populations based on: disease profiles; conditions currently being treated; current service use; pharmacy use and risk of future overall cost o Plan work for commissioning services and contracts o Set up capitated budgets o Identify health determinants of risk of admission to hospital, or other adverse care outcomes. Commissioning Commissioner reporting: o Summary by provider view - plan & actuals year to date (YTD). o Summary by Patient Outcome Data (POD) view - plan & actuals YTD. o Summary by provider view - activity & finance variance by POD. o Planned care by provider view - activity & finance plan & actuals YTD. o Planned care by POD view - activity plan & actuals YTD. o Provider reporting. o Statutory returns. o Statutory returns - monthly activity return. o Statutory returns - quarterly activity return. o Delayed discharges. o 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. Specific outputs related to the work of the following Data Processors: Data Processor 2 – Medeanalytics 1) The Medeanalytics processing will generate an output of predictive risk scores at the person level calculated from predictor parameters obtained from SUS, primary care and local flows. 2) The aim is to calculate a range of different predictive scores that would be useful to inform patients’ direct care, where validated algorithms are available. Such predictive scores would include the risk of hospital admission, electronic frailty index and the risk of admission to long term residential care. 3) The risk scores would be augmented with other contextual information relevant to the care process in the format of a care dashboard, such as diagnoses of long term conditions and relevant service activity. Data Processor 3 – Sheffield Hallam University 1) The university undertakes commissioned projects on behalf of the CCG to evaluate pilots and similar schemes to inform commissioning / investment decisions – generally where the CCG does not have in-house expertise – and this will involve data processing and analytics. Generally, the CCG does not solely work with a single university data processor, because of a) Fair Trading considerations, and b) each university offers quite different specialisations and expertise, the specifics of which in relation to this application are set out below. 2) The general outputs from data processing by the university includes aggregated descriptive and interferential statistics to ascertain outcome and impact in such evaluations, as well as health economic descriptors to understand cost-utility or cost-effectiveness. 3) The CCG works with Sheffield Hallam University principally in relation to projects involving the healthcare workforce (as a training institution for nurses and therapy professions) and those involving community healthcare services. 4) Specific outputs will focus on the effectiveness and cost-effectiveness of options involving the healthcare professions and teams (most often the nursing and therapy workforce), multidisciplinary working, and community healthcare services. Data Processor 4 – University of Sheffield 1) The university undertakes commissioned projects on behalf of the CCG to evaluate pilots and similar schemes to inform commissioning / investment decisions – generally where the CCG does not have in-house expertise – and this will involve data processing and analytics. Generally, the CCG does not solely work with a single university data processor, because of a) Fair Trading considerations, and b) each university offers quite different specialisations and expertise, the specifics of which in relation to this application are set out below. 2) The general outputs from data processing by the university includes aggregated descriptive and interferential statistics to ascertain outcome and impact in such evaluations, as well as health economic descriptors to understand cost-utility or cost-effectiveness. 3) The University of Sheffield, School of Health & Related Research (ScHARR) are partners in the Yorkshire & The Humber Collaboration for Leadership in Applied Health Research and Care (CLAHRC). This includes the evaluation on behalf of the CCG of several current and forthcoming national pilots taking place in the city. 4) The CCG works with the University of Sheffield principally in relation to mental health & wellbeing, secondary care services, urgent & emergency care, primary care services and health technologies. 5) Specific outputs will focus on the effectiveness and cost-effectiveness of options involving the services set out in item 2. Data Processor 5 – Attain Health Management Services Ltd 1) Attain has been appointed to assist the CCG with its planning for Sustainability & Transformation Plans; work that is focused on Sheffield-based tertiary services having larger geographic catchments, principally acute stroke and children’s secondary care services. 2) Outputs from data processing will comprise consolidated activity and commissioner expenditure in relation to these services.

Processing:

Data must only be used as stipulated within this Data Sharing Agreement. Data Processors must only act upon specific instructions from the Data Controller. Data can only be stored at the addresses listed under storage addresses. The Data Controller and any Data Processor will only have access to records of patients of residence and registration within the CCG. 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. There will be no dissemination involving any new data processor until appropriate data destruction has taken place at the former data processor. Invoice Validation 1. Identifiable SUS Data is obtained from the SUS Repository to the Data Services for Commissioners Regional Office (DSCRO). 2. The DSCRO pushes a one-way data flow of SUS data into the Controlled Environment for Finance (CEfF) in the Rotherham CCG. 3. The CSU carry out the following processing activities within the CEfF for invoice validation purposes: a. Checking the individual is registered to a particular Clinical Commissioning Group (CCG) and associated with an invoice from the SUS data flow to validate the corresponding record 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 a patient registered with a CCG GP or resident within the CCG area. iii. The health care provided should be paid by the CCG in line with CCG guidance.  4. Sheffield CCG are notified that the invoice has been validated and can be paid. Any discrepancies or non-validated invoices are investigated and resolved between Rotherham CCG CEfF team and the provider meaning that no identifiable data needs to be sent to Sheffield CCG. Sheffield CCG only receives notification to pay and management reporting detailing the total quantum of invoices received pending, processed etc. Risk Stratification 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 North of England 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 North of England 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. Access to the Risk Stratification system that North of England CSU hosts is limited to those substantive employees with authorised user accounts used for identification and authentication. 7. Once North of England CSU has completed the processing, the CCG can access the online system via a secure N3 connection to access the data pseudonymised at patient level. Commissioning The Data Services for Commissioners Regional Office (DSCRO) obtains the following data sets: 1. SUS 2. Local Provider Flows (received directly from providers) o Acute o Ambulance o Community o Demand for Service o Diagnostic Service o Emergency Care o Experience, Quality and Outcomes o Mental Health o Other Not Elsewhere Classified o Population Data o Primary Care Services o Public Health Screening 3. Mental Health Minimum Data Set (MHMDS) 4. Mental Health Learning Disability Data Set (MHLDDS) 5. Mental Health Services Data Set (MHSDS) 6. Maternity Services Data Set (MSDS) 7. Improving Access to Psychological Therapy (IAPT) 8. Child and Young People Health Service (CYPHS) 9. Diagnostic Imaging Data Set (DIDS) Data quality management and pseudonymisation is completed within the DSCRO and is then disseminated as follows: Data Processor 1 – North England 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 North England CSU. 2) North England CSU add derived fields, link data and provide analysis. 3) Allowed linkage is between the data sets contained within point 1. 4) Re-identification is only permitted for the purposes of direct care. 5) North England 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. 6) Aggregation of required data for CCG management use will be completed by North England CSU or the CCG as instructed by the CCG. 7) Patient level data will not be shared outside of the CCG and will only be shared within the CCG on a need to know basis, as per the purposes stipulated within the Data Sharing Agreement. External aggregated reports only with small number suppression can be shared. Data Processor 2 - MedeAnalytics International Ltd 1) Pseudonymised SUS and Local Provider data, only is securely transferred from the DSCRO to North England CSU. 2) North England CSU add derived fields, link data and provide analysis. 3) North England CSU also receives identifiable GP data from GP Practices. This data is kept secure and separate from any other data and is pseudonymised once it has entered the CSU. Any identifiable data is then destroyed. 4) North England CSU then securely pass the pseudonymised data to MedeAnalytics International Ltd for the addition of derived fields, linkage of data sets and analysis. 5) Allowed linkage is between the data sets contained within point 1 and point 3 (only once pseudonymised). 6) MedeAnalytics International Ltd also receive pseudonymised Social care data from providers. 7) MedeAnalytics International Ltd then link and process the pseudonymised data and 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. 8) Re-identification is only permitted for the purposes of direct care. 9) Aggregation of required data for CCG management use will be completed by MedeAnalytics International Ltd or the CCG as instructed by the CCG. 10) 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 -Sheffield Hallam University 1) Pseudonymised SUS and Local Provider data only is securely transferred from the DSCRO to North England CSU. 2) North England CSU add derived fields, link data and provide analysis. 3) Allowed linkage is between the data sets contained within point 1. 4) North England CSU then pass the processed, pseudonymised and linked data to the CCG. 5) The CCG analyse the data to see patient journeys for pathways or service design, re-design and de-commissioning 6) The CCG then pass the pseudonymised data securely to Sheffield Hallam University to analyse the data to see patient journeys for pathways or service design, re-design and de-commissioning. 7) Aggregation of required data for CCG management use will be completed by Sheffield Hallam University as instructed by the CCG. 8) Patient level data will not be shared outside of the Sheffield Hallam University and will only be shared within the Sheffield Hallam University 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 4 - University of Sheffield 1) Pseudonymised SUS and Local Provider data only is securely transferred from the DSCRO to North England CSU. 2) North England CSU add derived fields, link data and provide analysis. 3) Allowed linkage is between the data sets contained within point 1. 4) North England CSU then pass the processed, pseudonymised and linked data to the CCG. 5) The CCG analyse the data to see patient journeys for pathways or service design, re-design and de-commissioning. 6) The CCG then pass the pseudonymised data securely to University of Sheffield to analyse the data to see patient journeys for pathways or service design, re-design and de-commissioning. 7) Aggregation of required data for CCG management use will be completed by University of Sheffield as instructed by the CCG. 8) Patient level data will not be shared outside of the University of Sheffield and will only be shared within the University of Sheffield 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 5 – Attain Health Management Services Ltd 1) Pseudonymised SUS and Local Provider data only is securely transferred from the DSCRO to North England CSU. 2) North England CSU add derived fields, link data and provide analysis. 3) Allowed linkage is between the data sets contained within point 1. 4) North of England CSU pass the processed, pseudonymised and linked data to Attain. 5) Attain analyse the data to see patient journeys for pathways or service design, re-design and de-commissioning and then send the pseudonymised data to the CCG 6) Aggregation of required data for CCG management use will be completed by Attain or the CCG as instructed by the CCG. 7) Patient level data will not be shared outside of the CCG and will only be shared within the CCG on a need to know basis, as per the purposes stipulated within the Data Sharing Agreement. External aggregated reports only with small number suppression can be shared.

Objectives:

Invoice Validation As an approved Controlled Environment for Finance (CEfF), Rotherham CCG receives SUS data identifiable at the level of NHS number according to S.251 CAG 7-07(a) and (c)/2013, to undertake invoice validation on behalf of the CCG. NHS number is only used to confirm the accuracy of backing-data sets and will not be shared outside of the CEfF. The CCG are advised by Rotherham CCG whether payment for invoices can be made or not. Risk Stratification To use SUS data identifiable at the level of NHS number according to S.251 CAG 7-04(a) (and Primary Care Data) for the purpose of Risk Stratification. Risk Stratification provides a forecast of future demand by identifying high risk patients. This enables commissioners to initiate proactive management plans for patients that are potentially high service users. Risk Stratification enables General Practitioners (GPs) to better target intervention in Primary Care. Risk Stratification will be conducted by North of England Commissioning Support Unit (NECS) Commissioning To use pseudonymised data to provide intelligence to support commissioning of health services. The pseudonymised data is required to ensure that analysis of health care provision can be completed to support the needs of the health profile of the population within the CCG area based on the full analysis of multiple pseudonymised datasets. The CCGs commission services from a range of providers covering a wide array of services. Each of the data flow categories requested supports the commissioned activity of one or more providers. The following pseudonymised datasets are required to provide intelligence to support commissioning of health services: - Secondary Uses Service (SUS) - 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 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: Data Processor 1 – North of England Commissioning Support Unit (NECS) NECS is the commissioning support unit working with the CCG providing Intelligence (BI) for commissioning. Data Processor 2 – Medeanalytics Medeanalytics process pseudonymised data for the purpose of general commissioning. Activities undertaken are:  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  Contract Management and Modelling  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 Data Processor 3 – Sheffield Hallam University – Sheffield Hallam University undertake evaluations of local programmes on behalf of the CCG and to support the CCG undertaking its research commitments. Specifically, the University includes the evaluation of the Sheffield Primary Care Extended Access national pilot. It is expected that this will improve effectiveness of commissioning decision and use of resources. Research is only undertaken upon instruction by the CCG and for the purpose of commissioning. Data Processor 4 – University of Sheffield – University of Sheffield undertake evaluations of local programmes on behalf of the CCG and to support the CCG undertaking its research commitments. Specifically, the University includes the evaluation of the Sheffield Test Beds health technologies national pilot, Yorkshire & Humberside Collaboration for Leadership in Applied Health Research and Care (CLAHRC) linked mental health analysis & evaluations, and analysis of green space and health outcomes. Data will not be shared with other partners within the CLAHRC. This application is purely in relation to data sharing between the CCG and the University, and not with any other organisations in the CLAHRC collaboration. Research is only undertaken upon instruction by the CCG and for the purpose of commissioning. It is expected that this will improve effectiveness of commissioning decision and use of resources. Data Processor 5 – Attain Health Management Services Ltd – Attain carry out analysis on behalf of the Commissioners' Working Together programme to help evaluate the impact of the South Yorkshire and Bassetlaw (SY&B) Sustainability and Transformation Plan STP. The CCG will only receive data relating to that of the CCG. No other partners within the Commissioners' Working Together programme will receive data. Analytic outputs will provide an aggregated picture of service activity and spend across the health & care system in the context of Sheffield within the STP planning footprint and the Sheffield Place Plan. The specific benefit from this will be derived from informing the development of future models of care centred on how best to deploy the service resources available in the care system to optimise access and the coordination of care for the population.