NHS Digital Data Release Register - reformatted

NHS Doncaster CCG

Project 1 — DARS-NIC-165797-G5C7M

Opt outs honoured: No - data flow is not identifiable (Section 251, Does not include the flow of confidential data)

Sensitive: Sensitive

When: 2018/06 — 2019/04.

Repeats: Frequent adhoc flow, 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
  • Ambulance-Local Provider Flows
  • Community-Local Provider Flows
  • Demand for Service-Local Provider Flows
  • Diagnostic Services-Local Provider Flows
  • Emergency Care-Local Provider Flows
  • Experience, Quality and Outcomes-Local Provider Flows
  • 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:

Business Intelligence Commissioning Planning Performance Management Contract Monitoring Population Health Management

Objectives:

This is an amendment application for the following purposes: 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 The pseudonymised data is required to for the following purposes: § Population health management: • Understanding the interdependency of care services • Targeting care more effectively • Using value as the redesign principle • Ensuring we do what we should § Data Quality and Validation – allowing data quality checks on the submitted data § Thoroughly investigating the needs of the population, to ensure the right services are available for individuals when and where they need them § Understanding cohorts of residents who are at risk of becoming users of some of the more expensive services, to better understand and manage those needs § Monitoring population health and care interactions to understand where people may slip through the net, or where the provision of care may be being duplicated § Modelling activity across all data sets to understand how services interact with each other, and to understand how changes in one service may affect flows through another § Service redesign § Health Needs Assessment – identification of underlying disease prevalence within the local population § Patient stratification and predictive modelling - to identify specific patients at risk of requiring hospital admission and other avoidable factors such as risk of falls, computed using algorithms executed against linked de-identified data, and identification of future service delivery models The pseudonymised data is required to ensure that analysis of health care provision can be completed to support the needs of the health profile of the population within the CCG area based on the full analysis of multiple pseudonymised datasets. Processing for commissioning will be conducted by North of England Commissioning Support Unit and Attain Health Management Services Ltd.

Expected Benefits:

Commissioning 1. Supporting Quality Innovation Productivity and Prevention (QIPP) to review demand management, integrated care and pathways. a. Analysis to support full business cases. b. Develop business models. c. Monitor In year projects. 2. Supporting Joint Strategic Needs Assessment (JSNA) for specific disease types. 3. Health economic modelling using: a. Analysis on provider performance against 18 weeks wait targets. b. Learning from and predicting likely patient pathways for certain conditions, in order to influence early interventions and other treatments for patients. c. Analysis of outcome measures for differential treatments, accounting for the full patient pathway. d. Analysis to understand emergency care and linking A&E and Emergency Urgent Care Flows (EUCC). 4. Commissioning cycle support for grouping and re-costing previous activity. 5. Enables monitoring of: a. CCG outcome indicators. b. Financial and Non-financial validation of activity. c. Successful delivery of integrated care within the CCG. d. Checking frequent or multiple attendances to improve early intervention and avoid admissions. e. Case management. f. Care service planning. g. Commissioning and performance management. h. List size verification by GP practices. i. Understanding the care of patients in nursing homes. 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. • Analytic outputs will provide an aggregated picture of service activity and spend in relation to the CCG commissioned services that fall under the remit of the Doncaster component of Sustainability and Transformation Planning, largely encompassing acute stroke and children’s secondary care services. • Benefits will derive 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 across the planning geography.

Outputs:

Commissioning 1. Commissioner reporting: a. Summary by provider view - plan & actuals year to date (YTD). b. Summary by Patient Outcome Data (POD) view - plan & actuals YTD. c. Summary by provider view - activity & finance variance by POD. d. Planned care by provider view - activity & finance plan & actuals YTD. e. Planned care by POD view - activity plan & actuals YTD. f. Provider reporting. g. Statutory returns. h. Statutory returns - monthly activity return. i. Statutory returns - quarterly activity return. j. Delayed discharges. k. Quality & performance referral to treatment reporting. 2. Readmissions analysis. 3. Production of aggregate reports for CCG Business Intelligence. 4. Production of project / programme level dashboards. 5. Monitoring of acute / community / mental health quality matrix. 6. Clinical coding reviews / audits. 7. Budget reporting down to individual GP Practice level. 8. GP Practice level dashboard reports include high flyers. 9. Comparators of CCG performance with similar CCGs as set out by a specific range of care quality and performance measures detailed activity and cost reports 10. Data Quality and Validation measures allowing data quality checks on the submitted data 11. Contract Management and Modelling 12. Patient Stratification, such as: o Patients at highest risk of admission o Most expensive patients (top 15%) o Frail and elderly o Patients that are currently in hospital o Patients with most referrals to secondary care o Patients with most emergency activity o Patients with most expensive prescriptions o Patients recently moving from one care setting to another i. Discharged from hospital ii. Discharged from community • Attain has been appointed to assist the CCG with its planning for Sustainability & Transformation Plans; work that is focused on Doncaster-based tertiary services having larger geographic catchments, principally acute stroke and children’s secondary care services. • 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. 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 of interest of the applicant. The DSCRO (part of NHS Digital) will apply Type 2 objections before any identifiable data leaves the DSCRO. NHS Digital reminds all organisations party to this agreement of the need to comply with the Data Sharing Framework Contract requirements, including those regarding the use (and purposes of that use) by “Personnel” (as defined within the Data Sharing Framework Contract ie: employees, agents and contractors of the Data Recipient who may have access to that data) Segregation Where the Data Processor and/or the Data Controller hold both identifiable and pseudonymised data, the data will be held separately so data cannot be linked. All access to data is audited Commissioning The Data Services for Commissioners Regional Office (DSCRO) obtains the following data sets: 1. SUS+ 2. Local Provider Flows (received directly from providers) 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 Data quality management and pseudonymisation is completed within the DSCRO and is then disseminated as follows: Data Processor 1 – North of England Commissioning Support Unit 1. Pseudonymised SUS+, Local Provider data, only is securely transferred from the DSCRO to North of England Commissioning Support Unit. 2. North of England Commissioning Support Unit add derived fields, link data and provide analysis to: a. See patient journeys for pathways or service design, re-design and de-commissioning (CCG). b. Check recorded activity against contracts or invoices and facilitate discussions with providers (CCG). 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. North of England Commissioning Support Unit then pass the processed, pseudonymised and linked data to Attain Health Management Services Ltd. 5. 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 / Accountable Care System. The CCG will only receive data relating to that of the CCG. No other partners within the Commissioners' Working Together programme will receive data. 6. Analytic outputs will provide an aggregated picture of service activity and spend across the health & care system in the context of Doncaster within the STP planning footprint and the Doncaster 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


Project 2 — DARS-NIC-204562-J5W0T

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

Sensitive: Non Sensitive

When: 2019/02 — 2019/04.

Repeats: System Access

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

Categories: Anonymised - ICO code compliant

Datasets:

  • National Cancer Waiting Times Monitoring DataSet (CWT)

Yielded Benefits:

Cancer Alliances have previously had access to Cancer Waiting Times reports and pseudonymised data through the system on Open Exeter, under an agreement with NHS England. This has enabled analysis to inform service improvement both to achieve the national Cancer Waiting Times standards and also wider cancer pathway improvement work, which will have contributed to oncoming improvements to cancer survival and patient experience.

Objectives:

This agreement is for the South Yorkshire, Bassetlaw and North Derbyshire Cancer Alliance to access Cancer Waiting Times data. However, the Cancer Alliance is not a legal entity - its staff (and those accessing the Cancer Waiting Times data) are substantively employed by NHS Doncaster Clinical Commissioing Group (CCG). NHS Doncaster CCG is therefore the lead organisation, and the data controller who processes data. In this agreement, therefore, all references to accessing the data refer to the legal entity - NHS Doncaster CCG. Improvements for Cancer patients The independent Cancer Taskforce set out an ambitious vision for improving services, care and outcomes for everyone with Cancer: fewer people getting Cancer, more people surviving Cancer, more people having a good experience of their treatment and care, whoever they are and wherever they live, and more people being supported to live as well as possible after treatment has finished. Cancer Alliances Cancer Alliances, which have been set up across England, are key to driving the change needed across the country to achieve the Taskforce’s vision. Bringing together local clinical and managerial leaders from providers and commissioners who represent the whole Cancer pathway, Cancer Alliances provide the opportunity for a different way of working to improve and transform Cancer services. Cancer Alliance partners will take a whole population, whole pathway approach to improving outcomes across their geographical ‘footprints’, building on their relevant Sustainability and Transformation Plans (STPs). They will bring together influential local decision-makers and be responsible for directing funding to transform services and care across whole pathways, reducing variation in the availability of good care and treatment for all people with Cancer, and delivering continuous improvement and reduction in inequality of experience. They will particularly focus on leading transformations at scale to improve survival, early diagnosis, patient experience and long-term quality of life. Successful delivery will be shown in improvements in ratings in the Clinical Commissioning Group (CCG) Improvement and Assessment Framework (IAF), including, importantly, in the 62 day wait from referral to first treatment standard. https://www.england.nhs.uk/publication/ccg-iaf-methodology-manual/ Cancer Wait Times (CWT) system The Cancer Wait Times (CWT) system collects and validates the National Cancer Waiting Times Monitoring Data Set (NCWTMDS), allowing performance to be measured against operational Cancer standards. Data is validated and records merged to the same pathway to cover the period from referral to first definitive treatment for Cancer and any additional subsequent treatments. The CWT system then determines whether the operational standard(s) that apply were met or not for the patient and the accountable provider(s). The CWT system holds NCWTMDS in a series of pre-aggregated static reports. These reports are available monthly and quarterly data (aligned with the National Statistics for Cancer Waiting Times published by NHS England). Users can query the CWT system to generate reports to feedback on the progress towards meeting these targets. South Yorkshire, Bassetlaw and North Derbyshire Cancer Alliance Doncaster CCG will directly access the Cancer Waiting Times System for the South Yorkshire, Bassetlaw and North Derbyshire Cancer Alliance region, which covers a population of 1.85 million people. South Yorkshire, Bassetlaw and North Derbyshire Cancer Alliance works with health organisations across South Yorkshire, Bassetlaw and North Derbyshire including 5 acute providers and 7 clinical commissioning groups. Acute Providers BARNSLEY HOSPITAL NHS FOUNDATION TRUST CHESTERFIELD ROYAL HOSPITAL NHS FOUNDATION TRUST DONCASTER AND BASSETLAW TEACHING HOSPITALS NHS FOUNDATION TRUST THE ROTHERHAM NHS FOUNDATION TRUST SHEFFIELD TEACHING HOSPITALS NHS FOUNDATION TRUST CCGs NHS BARNSLEY CCG NHS BASSETLAW CCG NHS DONCASTER CCG NHS HARDWICK CCG NHS NORTH DERBYSHIRE CCG NHS ROTHERHAM CCG NHS SHEFFIELD CCG Data access The CWT system provides one organisation (the lead organisation) representing each Cancer Alliance, with access to the following; a) Aggregate reports (which may include unsuppressed small numbers) b) Pseudonymised record level data - users can directly download this data from the CWT system c) I-View Plus tool Doncaster CCG will only access patient records which fall within the Cancer Alliances' footprint of responsibility based on the patients' CCG of responsibility. This Cancer Alliance is limited to South Yorkshire, Bassetlaw and North Derbyshire Cancer Patients. A) Aggregate reports including small numbers Aggregate data is available in the form of reports at Provider (Trust) and Clinical Commissioning Group (CCG) level. Small numbers may be included in the aggregate data reports and are essential for analyses carried out by lead organisations. Investigating breaches Doncaster CCG routinely monitor performance and standards using the CWT system, particularly in relation to breaches of the 62 day wait target. Due to the large number of potential Trust/CCG combinations, breach counts could result in small numbers as in some cases there are less than 6 breaches in a whole year. Given that financial penalties are linked to target breaches counts must accurately reflect the true percentage without suppression. Mitigating risk of re-identification Risk of disclosure is minimised as the dataset does not include patient demographics (increasing risk of re-identification) that may allow users to identify an individual e.g. there are no age, ethnic categories or geographic breakdowns based on patient postcode. Additionally, the aggregation categories are such that the data is not at a lesser granular level e.g. the source NCWTMDS data collects information at ICD diagnosis code level, but the CWT system aggregates at tumour group level – e.g. Head & Neck, Upper GI, lower GI, Breast etc. B) Pseudonymised record level extracts Doncaster CCG will access record level pseudonymised data which includes the system generated pseudo CWT patient ID. Any record level data extracted from the system will not be processed outside of the authorised users of the system. C) i-View Plus . iView Plus uses cube functionality to allow lead organisations to produce graphs, charts and tabulations from the data through the construction of queries. The data in iView plus is split by operational standard being measured and can then be analysed against a range of dimensions collected in the data and measures such as count, percentage and median. The outputs of iView Plus are aggregate, and no record level data can be obtained, however some queries may result in small numbers and these currently have limited disclosure control applied, see A) for further explanation. iView Plus holds published data, the lowest organisational granularity is trust level, data can also be aggregated to CCG level and other health hierarchies. Doncaster CCG will use the data to both monitor and improve performance against the Cancer Waiting Time standards and to inform wider Cancer pathway improvements. Doncaster CCG's use of the data will fall into two separate categories, each requiring different levels of suppression, and onward sharing both within the Cancer Alliance and with wider NHS stakeholders; Purpose One - Aggregate local reports Generation of routine Cancer Waiting Times reports at Provider (Trust) or CCG level. Lead organisations will access a summary of the totals for the Providers (Trust) and CCG's that are treating cancer patients where they have a commissioning responsibility for that patient (based on the CCG they are aligned to). This analysis would then be shared with the providers and commissioners and used to inform service improvement by providing benchmarked comparable data. The format of this report would be in a tabulated or graphical form (i.e. not record level) but may contain small numbers. An example of where small numbers would not be suppressed would be in relation to cases of breaches against a standard where small numbers would be essential to ensure the report is meaningful. Examples of this type of analysis include: a. Comparative Cancer Waiting Times performance at tumour group and individual tumour site (i.e. ICD10 code) level for Trusts and CCGs across the geography b. Analysis of Cancer Waiting Times performance by treatment modality c. Grouping length of waits for standards d. Analysis of free text and derived breach reason fields to identify trends in reasons for delays e. To provide assurance through comparative analysis (e.g. orphan record identification, active monitoring proportions and validation of waiting list adjustments entered) f. Analysis of flows of patients including analysis by provider trust site g. Reviewing waits between surgery and radiotherapy for Head and Neck Cancer patients with a maximum recommended wait of 6 weeks h. Reviewing routes to diagnosis of patients i. Quantifying treatment volumes by provider organisation including analysis treatment rates Purpose Two - Sharing of record level data (including free text breach reasons) with providers and commissioners responsible for direct patient care for that patient. This will be for local audit purposes. The two broad purposes for this would be; 1) To support audit work 2) Investigate individual outliers to the national standards Pathway analysis will be undertaken, identifying trends in reasons for breaches. The analysis will inform system wide pathway improvements and compliance to the national standards. Examples of potential changes to achieve this could be to support trusts in additional resources and processes and also to facilitate discuss between trusts for example in reaching agreement for diagnostics between trusts. Examples of the types of reasons for this include; a. Patients waiting excessively long period of time to seen of received treatment b. Free text breach reasons identifying areas of concern which require more detail or clarification from provider c. Identification of 28 day standard exceptions - National guidance states patients who are diagnosed with cancer should be informed face to face, this would highlights numbers of patients who are not told in person by provider d. Audits to review orphan records which require local providers to review local patients records Record level data (pseudonymised) will be shared via NHS.net email accounts and access will be controlled by password protecting all files.

Expected Benefits:

1) Benefits type: Supporting delivery of CWT standards The Cancer Waiting Times standards are key operational standards for the NHS, which aim to reduce the waits for diagnosis and treatment for Cancer patients, which will support improvements to survival rates and improve patient experience. This includes the new 28 day faster diagnosis standard being introduced as a standard from April 2020. A key enabler to achieve these standards, and thus improve survival and patient experience is the role of Cancer Alliances locally to work with providers and commissioners to improve patient pathways. Access to the Cancer Waiting Times data as detailed in the above will enable Cancer Alliances to have informed discussions and allocate resources optimally to improve performance against these standards. It will also enable Cancer Alliances to work with local providers and commissioners to identify outliers against the standards, and mitigate the risk of similar delays for other patients. Improvement would be expected on an on-going basis with standards already in place for nine standards:- • 2 week wait urgent GP referral – 93% • 2 week wait breast symptomatic – 93% • 31 day 1st treatment - 96% • 31 day subsequent surgery – 94% • 31 day subsequent drugs – 98% • 31 day subsequent radiotherapy – 94% • 62 day (GP) referral to 1st treatment – 85% • 62 day (screening ) referral to 1st treatment – 90% • 62 day upgrade to 1st treatment – locally agreed standard In addition this access and use of data will be key in delivering the new 28 day faster diagnosis standard being introduced from 2020 2) Benefits type: Improvements beyond constitutional standards This access and resulting analysis will enable Cancer Alliances to undertake local analysis beyond the Cancer Waiting times operational standards to support improvements to Cancer patients pathways beyond those already achieved by improving performance against standard set. This could include reviewing times between treatments, or treatment rates. The overall aim of this type of additional analysis would be to support improvements to Cancer patients survival and experience. The Cancer Taskforce recommendation set out a number of ambitions to be met nationally and locally by 2020 including improving 1 year survival for Cancer to 75%, and improving the proportions of patients staged 1 or 2 to 62%. For both of these improvements to the diagnostic and treatment pathways are key, and require Cancer Alliances to be able to analyse the Cancer Waiting Times dataset to identify sub-optimum pathways and resulting improvements.

Outputs:

Outputs fall into the following categories: 1) Analysis to support delivery of Cancer Waiting Times standard and identify variation, including clinical discussions to improve patient pathways a. Comparative Cancer Waiting Times performance at tumour group and individual tumour site (i.e. ICD10 code) level for Trusts and CCGs. b. Analysis of Cancer Waiting Times performance by treatment modality to inform discussions c. Grouping length of waits for standards to inform discussions on going beyond constitutional standards d. Analysis of free text and derived breach reason fields to identify trends in reasons for delays. e. To provide assurance through comparative analysis (e.g. orphan record identification, active monitoring proportions and validation of waiting list adjustments entered) f. Analysis of flows of patients including analysis by provider trust site g. Outlier identification including exceptionally long waits to inform individual queries to providers 2) Cancer Waits analysis (not directly linked to constitutional standards) for the aim of identifying variation which may impact Cancer patient’s outcomes or patient experience. Examples for use of the data may include reviewing waits between surgery and radiotherapy for Head and Neck cancer patients with a maximum recommended wait of 6 weeks and using the data source to validate surgical numbers by provider trust. The overarching aim of all future analysis/outputs is to inform priorities and potential investment to improve Cancer pathways including reducing Cancer incidence and mortality, improving Cancer survival, improving patient experience, improving service efficiency and meeting national constitution standards relating to Cancer patients.

Processing:

Access to the Cancer Wait Times (CWT) System will enable Cancer Alliances to undertake a wide range of locally-determined and locally-specific analyses to support the Cancer Taskforce vision for improving services, care and outcomes for everyone with Cancer. Only Doncaster Clinical Commissioning Group (CCG) will directly access the Cancer Waiting Times system. Extracts can be downloaded and will be stored on the Doncaster CCG servers. Role Based Access Control prevents access to data downloads to employees outside of the analytical team responsible for producing outputs. The CWT system is hosted by NHS Digital, access to and usage of the system is fully auditable. Users must comply with the use of the data as specified in this agreement. The CWT system complies with the requirements of NHS Digital Code of Practice on Confidential Information, the Caldicott Principles and other relevant statutory requirements and guidance to protect confidentiality. Access to the CWT system will be granted to individual users only when a valid Data Usage Certificate (DUC) form is submitted to NHS Digital via the lead organisations Senior Information Risk Officer (SIRO), and where there is a valid Data Sharing Agreement between the lead organisation and NHS Digital. Approved users will log into the system via an N3 connection and will use a Single Sign-On (users are prompted to create a unique username and password). Doncaster Clinical Commissioning Group users will access: a) Aggregate reports (which may include unsuppressed small numbers) b) Pseudonymised record level data - users can directly download this data from the CWT system c) I-View Plus tool (aggregated - access to produce graphs, charts/tabulations from the data through the construction of queries). This will give users access to run bespoke analysis on pre-defined measures and dimensions. It delivers the same data that is available through the reports and record level downloads (i.e. it will not contain patient identifiable data). Any record level data extracted from the system will not be processed outside of the Doncaster Clinical Commissioning Group unless otherwise specified in this agreement. Users are not permitted to upload data into the system. Data will only be available for the Providers (Trust) and CCG's that are treating cancer patients where they have a commissioning responsibility for that patient (based on the CCG that this Cancer Alliance is aligned to). The data will only be shared with other members of the Cancer Alliance in the format described in purpose 1 and purpose 2 of this agreement. The primary method for sharing outputs PDF, MS Excel via NHS NET, as well as developing to Power BI for assigned users through secure access. Aggregate data/ graphical outputs may be shared via e-mail; for example as part of Alliance meeting papers. Where record level data is shared with individual trusts these are shared only with trust(s) who were involved in the direct care of the patient, only via NHS.net email accounts. As part of partnership working to improve Cancer Waiting Times performance, outputs may be shared with national/ regional bodies including NHS England, CADEAS, STP and Macmillan. Data will only be shared as described in purpose one and purpose two of this agreement and where recipient organisations hold a valid Data Sharing Agreement with NHS Digital to access Cancer Waiting Times data. Training on the CWT system is not required as it is a data delivery system and it does not provide functionality to conduct bespoke detailed analysis. User guides are available for further assistance. Access to the CWT system data is restricted to Cancer Alliance employees who are substantively employed by the Data Controller in fulfilment of their public health function. The Cancer Alliances will use the data to produce a range of quantitative measures (counts, crude and standardised rates and ratios) that will form the basis for a range of statistical analyses of the fields contained in the supplied data. Typical uses will include: 1) Analysis to support delivery of Cancer Waiting Times standard and identify variation, including clinical discussions to improve patient pathways a. Comparative Cancer Waiting Times performance at tumour group and individual tumour site (i.e. ICD10 code) level for Trusts and CCGs. b. Analysis of Cancer Waiting Times performance by treatment modality to inform discussions c. Grouping length of waits for standards to inform discussions on going beyond constitutional standards d. Analysis of free text and derived breach reason fields to identify trends in reasons for delays. e. To provide assurance through comparative analysis (e.g. orphan record identification, active monitoring proportions and validation of waiting list adjustments entered) f. Analysis of flows of patients including analysis by provider trust site g. Outlier identification including exceptionally long waits to inform individual queries to providers 2) Cancer Waits analysis (not directly linked to constitutional standards) for the aim of identifying variation which may impact Cancer patient’s outcomes or patient experience. Examples for use of the data may include reviewing waits between surgery and radiotherapy for Head and Neck cancer patients with a maximum recommended wait of 6 weeks and using the data source to validate surgical numbers by provider trust.


Project 3 — DARS-NIC-86861-K1L1N

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

Sensitive: Sensitive

When: 2018/06 — 2019/04.

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
  • Community Services Data Set
  • National Cancer Waiting Times Monitoring DataSet (CWT)
  • Civil Registration - Births
  • Civil Registration - Deaths

Yielded Benefits:

Validation of invoices to ensure only appropriate activity is paid for. Population Health Management in conjunction with Doncaster MBC Integrated commissioning in conjunction with Doncaster MBC Business intelligence including performance reporting

Objectives:

Invoice Validation As an approved Controlled Environment for Finance (CEfF), the CCG receives SUS data identifiable at the level of NHS number according to S.251 CAG 7-07(a) and (b)/2013. The data is required for the purpose of invoice validation. The NHS number is only used to confirm the accuracy of backing-data sets and will not be shared outside of the CEfF. 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 - 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 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 - Commissioning 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. Pseudonymised – SUS - Commissioning PI Health and Care Ltd receive pseudonymised SUS data from the DSCRO and pseudonymised Social Care Data from Doncaster Council. PI Health and Care Ltd link and process the data in order for the CCG to provide a range of commissioning intelligence that is linked to local partnership priorities and to support the approach to measuring activity, outcome and financial impact of local Better Care Fund schemes and Sustainability and Transformation Plan. Data is a vital asset, both for the provision of services and for the efficient management of services and resources. It is therefore essential that the data the CCG hold is intelligently analysed and leveraged to inform strategic commissioning decisions, assist with the evaluation of project outcomes and to enable a culture of shared decision making across the care system. PI Health and Care Ltd will produce online reports using HealthTrak data analysis tool to provide Doncaster CCG with a range of high level commissioning intelligence based on integrated pathways of care in Doncaster. Access to these reports is based on user access controls, as follows: *PI Care and Health is a product of the company PI Limited. PI Limited also known as PI Benchmark. The company registration number is 1728605. 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 Digita; will not be national data, but only that data relating to the specific locality of interest of the applicant.

Expected Benefits:

Invoice Validation 1. Financial validation of activity 2. CCG Budget control 3. Commissioning and performance management 4. Meeting commissioning objectives without compromising patient confidentiality 5. The avoidance of misappropriation of public funds to ensure the ongoing delivery of patient care Risk Stratification Risk stratification promotes improved case management in primary care and will lead to the following benefits being realised: 1. Improved planning by better understanding patient flows through the healthcare system, thus allowing commissioners to design appropriate pathways to improve patient flow and allowing commissioners to identify priorities and identify plans to address these. 2. Improved quality of services through reduced emergency readmissions, especially avoidable emergency admissions. This is achieved through mapping of frequent users of emergency services 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. Commissioning (Pseudonymised) – SUS Data Processor 3 - PI Health and Care Ltd 1. Improved performance against national Better Care Fund metrics 2. Reduction in A&E attendances (especially for elderly persons) 3. Reduction in emergency hospital admissions (especially for elderly persons) 4. Accurate evaluation of local Better Care Fund schemes 5. Accurate evaluation of system transformation of Intermediate Care 6. Improved experience of service users 7. Improved health outcomes 8. Improved social care outcomes 9. Improved productivity thorough streamlining and integration of services. 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:

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 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. Commissioning (Pseudonymised) – SUS Data Processor 3 - PI Health and Care Ltd Reports, analyses and dashboards to support the integration of health and social care including: 1. Falls 2. Better Care Fund national metrics 3. Better Care Fund local schemes 4. Intermediate Care 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:

DSCRO will apply Type 2 objections before any identifiable data leaves the DSCRO. The CCG and any Data Processor will only have access to records of its own CCG. Access is limited to substantive employees with authorised user accounts used for identification and authentication. Invoice Validation 1. SUS Data is obtained from the SUS Repository to Yorkshire DSCRO 2. Yorkshire DSCRO pushes a one-way data flow of SUS data into the Controlled Environment for Finance (CEfF) located in the CCG. 3. The CEfF conduct the following processing activities for invoice validation purposes: a. Checking the individual 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 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 the HSCIC to confirm the payments are: i. In line with Payment by Results tariffs ii. Are in relation to a patient registered with the CCG GP or resident within the CCG area. iii. The health care provided should be paid by the CCG in line with CCG guidance.  4. The CCG are notified that the invoice has been validated and can be paid. Any discrepancies or non-validated invoices are investigated and resolved 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 Data Centre on N3. 3. 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. 4. North of England CSU who host the risk stratification system that holds SUS data is limited to those administrative staff with authorised user accounts used for identification and authentication. 5. 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. 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. Commissioning (Pseudonymised) – SUS Data Processor - PI Health and Care Ltd 1. Yorkshire Data Services for Commissioners Regional Office (DSCRO) receive a flow of identifiable SUS data for Doncaster from SUS. 2. Data quality management of data is completed by the DSCRO. The SUS data is then pseudonymised using University of Nottingham open pseudonymiser tool - a standalone windows desktop application which creates a digest of one or more columns of a CSV file, using a shared key (SALT file) controlled by Yorkshire Data Services for Commissioners Regional Office 3. The completed pseudonymised file is then passed to PI Limited (PI Care and Health) via secure FTP. 4. Data quality management of social care data is completed by Doncaster Council. The social care data is then pseudonymised using University of Nottingham open pseudonymiser tool. Pseudonymised Social Care Data will be sent to PI Limited (PI Care and Health) direct from Doncaster Council via secure FTP. 5. The pseudonymisation key cannot be used to re-identify data as the tool does not allow for this to happen, it only allows for one way pseudonymisation. The pseudonymisation tool will not be received by PI Limited (PI Care and Health) from either the DSCRO or Doncaster CCG. Pseudonymisation that allows data to be matched is dependent on having the correct key. 6. PI Limited (PI Care and Health) then link the data using the common pseudo link, which is undertaken within a controlled environment by a named member of staff, who then produce online reports using HealthTrak data analysis tool to provide Doncaster CCG a with a range of high level commissioning intelligence based on integrated pathways of care in Doncaster. Access to these reports is based on user access controls, as follows: - Access to the commissioning intelligence at pseudonymised level is accessible by only 2 named members of staff in the CCG (based on a super user access licence for HealthTrak) - Access to aggregate commissioning intelligence (anonymised) is available to no more than 3 additional users across the CCG (standard user licence) - External aggregated reports only with small number suppression can be shared. Access to the HealthTrak system, both on a super user and standard user approach is governed via respective organisation employee code of practice, data protection policies and information governance protocols. Additionally, super users conform to a specific information access agreement which mitigates the risk of how the pseudonymised data can be handled and used. 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.


Project 4 — NIC-37497-DOD1Y

Opt outs honoured: Y, N

Sensitive: Sensitive

When: 2016/12 — 2017/02.

Repeats: Ongoing

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

Categories: Identifiable, Anonymised - ICO code compliant

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

Objectives:

Invoice Validation As an approved Controlled Environment for Finance (CEfF), the CCG receives SUS data identifiable at the level of NHS number according to S.251 CAG 7-07(a) and (b)/2013. The data is required for the purpose of invoice validation. The NHS number is only used to confirm the accuracy of backing-data sets and will not be shared outside of the CEfF. 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 Digita; will not be national data, but only that data relating to the specific locality of interest of the applicant.

Expected Benefits:

Invoice Validation 1. Financial validation of activity 2. CCG Budget control 3. Commissioning and performance management 4. Meeting commissioning objectives without compromising patient confidentiality 5. The avoidance of misappropriation of public funds to ensure the ongoing delivery of patient care Risk Stratification Risk stratification promotes improved case management in primary care and will lead to the following benefits being realised: 1. Improved planning by better understanding patient flows through the healthcare system, thus allowing commissioners to design appropriate pathways to improve patient flow and allowing commissioners to identify priorities and identify plans to address these. 2. Improved quality of services through reduced emergency readmissions, especially avoidable emergency admissions. This is achieved through mapping of frequent users of emergency services 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:

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 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. Invoice Validation 1. SUS Data is obtained from the SUS Repository to Yorkshire DSCRO 2. Yorkshire DSCRO pushes a one-way data flow of SUS data into the Controlled Environment for Finance (CEfF) located in the CCG. 3. The CEfF conduct the following processing activities for invoice validation purposes: a. Checking the individual 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 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 the HSCIC to confirm the payments are: i. In line with Payment by Results tariffs ii. Are in relation to a patient registered with the CCG GP or resident within the CCG area. iii. The health care provided should be paid by the CCG in line with CCG guidance.  4. The CCG are notified that the invoice has been validated and can be paid. Any discrepancies or non-validated invoices are investigated and resolved 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 South East CSU, who hold the SUS data within the secure Data Centre on N3. 3. 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. 4. South East CSU who host the risk stratification system that holds SUS data is limited to those administrative staff with authorised user accounts used for identification and authentication. 5. Once South East 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. 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.


Project 5 — NIC-86861-K1L1N

Opt outs honoured: N, Y

Sensitive: Sensitive

When: 2017/03 — 2018/02.

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:

  • Children and Young People's Health Services Data Set
  • Improving Access to Psychological Therapies 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
  • Mental Health and Learning Disabilities Data Set
  • Mental Health Minimum Data Set
  • Mental Health Services Data Set
  • SUS Accident & Emergency data
  • SUS Admitted Patient Care data
  • SUS Outpatient data
  • Maternity Services Dataset
  • SUS data (Accident & Emergency, Admitted Patient Care & Outpatient)

Objectives:

Invoice Validation As an approved Controlled Environment for Finance (CEfF), the CCG receives SUS data identifiable at the level of NHS number according to S.251 CAG 7-07(a) and (b)/2013. The data is required for the purpose of invoice validation. The NHS number is only used to confirm the accuracy of backing-data sets and will not be shared outside of the CEfF. 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 - 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 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 - Commissioning 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. Pseudonymised – SUS - Commissioning PI Health and Care Ltd receive pseudonymised SUS data from the DSCRO and pseudonymised Social Care Data from Doncaster Council. PI Health and Care Ltd link and process the data in order for the CCG to provide a range of commissioning intelligence that is linked to local partnership priorities and to support the approach to measuring activity, outcome and financial impact of local Better Care Fund schemes and Sustainability and Transformation Plan. Data is a vital asset, both for the provision of services and for the efficient management of services and resources. It is therefore essential that the data the CCG hold is intelligently analysed and leveraged to inform strategic commissioning decisions, assist with the evaluation of project outcomes and to enable a culture of shared decision making across the care system. PI Health and Care Ltd will produce online reports using HealthTrak data analysis tool to provide Doncaster CCG with a range of high level commissioning intelligence based on integrated pathways of care in Doncaster. Access to these reports is based on user access controls, as follows: *PI Care and Health is a product of the company PI Limited. PI Limited also known as PI Benchmark. The company registration number is 1728605. 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 Digita; will not be national data, but only that data relating to the specific locality of interest of the applicant.

Expected Benefits:

Invoice Validation 1. Financial validation of activity 2. CCG Budget control 3. Commissioning and performance management 4. Meeting commissioning objectives without compromising patient confidentiality 5. The avoidance of misappropriation of public funds to ensure the ongoing delivery of patient care Risk Stratification Risk stratification promotes improved case management in primary care and will lead to the following benefits being realised: 1. Improved planning by better understanding patient flows through the healthcare system, thus allowing commissioners to design appropriate pathways to improve patient flow and allowing commissioners to identify priorities and identify plans to address these. 2. Improved quality of services through reduced emergency readmissions, especially avoidable emergency admissions. This is achieved through mapping of frequent users of emergency services 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. Commissioning (Pseudonymised) – SUS Data Processor 3 - PI Health and Care Ltd 1. Improved performance against national Better Care Fund metrics 2. Reduction in A&E attendances (especially for elderly persons) 3. Reduction in emergency hospital admissions (especially for elderly persons) 4. Accurate evaluation of local Better Care Fund schemes 5. Accurate evaluation of system transformation of Intermediate Care 6. Improved experience of service users 7. Improved health outcomes 8. Improved social care outcomes 9. Improved productivity thorough streamlining and integration of services. 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:

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 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. Commissioning (Pseudonymised) – SUS Data Processor 3 - PI Health and Care Ltd Reports, analyses and dashboards to support the integration of health and social care including: 1. Falls 2. Better Care Fund national metrics 3. Better Care Fund local schemes 4. Intermediate Care 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:

DSCRO will apply Type 2 objections before any identifiable data leaves the DSCRO. The CCG and any Data Processor will only have access to records of its own CCG. Access is limited to substantive employees with authorised user accounts used for identification and authentication. Invoice Validation 1. SUS Data is obtained from the SUS Repository to Yorkshire DSCRO 2. Yorkshire DSCRO pushes a one-way data flow of SUS data into the Controlled Environment for Finance (CEfF) located in the CCG. 3. The CEfF conduct the following processing activities for invoice validation purposes: a. Checking the individual 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 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 the HSCIC to confirm the payments are: i. In line with Payment by Results tariffs ii. Are in relation to a patient registered with the CCG GP or resident within the CCG area. iii. The health care provided should be paid by the CCG in line with CCG guidance.  4. The CCG are notified that the invoice has been validated and can be paid. Any discrepancies or non-validated invoices are investigated and resolved 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 South East CSU, who hold the SUS data within the secure Data Centre on N3. 3. 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. 4. South East CSU who host the risk stratification system that holds SUS data is limited to those administrative staff with authorised user accounts used for identification and authentication. 5. Once South East 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. 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. Commissioning (Pseudonymised) – SUS Data Processor 3 - PI Health and Care Ltd 1. Yorkshire Data Services for Commissioners Regional Office (DSCRO) receive a flow of identifiable SUS data for Doncaster from SUS. 2. Data quality management of data is completed by the DSCRO. The SUS data is then pseudonymised using University of Nottingham open pseudonymiser tool - a standalone windows desktop application which creates a digest of one or more columns of a CSV file, using a shared key (SALT file) controlled by Yorkshire Data Services for Commissioners Regional Office 3. The completed pseudonymised file is then passed to PI Limited (PI Care and Health) via secure FTP. 4. Data quality management of social care data is completed by Doncaster Council. The social care data is then pseudonymised using University of Nottingham open pseudonymiser tool. Pseudonymised Social Care Data will be sent to PI Limited (PI Care and Health) direct from Doncaster Council via secure FTP. 5. The pseudonymisation key cannot be used to re-identify data as the tool does not allow for this to happen, it only allows for one way pseudonymisation. The pseudonymisation tool will not be received by PI Limited (PI Care and Health) from either the DSCRO or Doncaster CCG. Pseudonymisation that allows data to be matched is dependent on having the correct key. 6. PI Limited (PI Care and Health) then link the data using the common pseudo link, which is undertaken within a controlled environment by a named member of staff, who then produce online reports using HealthTrak data analysis tool to provide Doncaster CCG a with a range of high level commissioning intelligence based on integrated pathways of care in Doncaster. Access to these reports is based on user access controls, as follows: - Access to the commissioning intelligence at pseudonymised level is accessible by only 2 named members of staff in the CCG (based on a super user access licence for HealthTrak) - Access to aggregate commissioning intelligence (anonymised) is available to no more than 3 additional users across the CCG (standard user licence) - External aggregated reports only with small number suppression can be shared. Access to the HealthTrak system, both on a super user and standard user approach is governed via respective organisation employee code of practice, data protection policies and information governance protocols. Additionally, super users conform to a specific information access agreement which mitigates the risk of how the pseudonymised data can be handled and used. 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.