NHS Digital Data Release Register - reformatted

NHS Midlands And Lancashire Commissioning Support Unit projects

• The Strategy Unit (part of NHS Midlands and Lancashire CSU): analytical support to NHS and partner organisations
• DSfC - STP - NHS Staffordshire and Stoke on Trent CCGs - Comm
• Black Country Joint Data Sharing agreement with NHS Dudley CCG, NHS Sandwell and West Birmingham CCG, NHS Walsall CCG and NHS Wolverhampton CCG.
• DSfC - Worcestershire and Herefordshire STP - Comm

791 data files in total were disseminated unsafely (information about files used safely is missing for TRE/"system access" projects).

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The Strategy Unit (part of NHS Midlands and Lancashire CSU): analytical support to NHS and partner organisations — DARS-NIC-05206-L1V6D

Type of data: Pseudonymised

Opt outs honoured: N, Y, No - data flow is not identifiable, Anonymised - ICO Code Compliant, No (Does not include the flow of confidential data, Flow to de-identified environment - no analysis on confidential patient information)

Legal basis: Health and Social Care Act 2012, Section 42(4) of the Statistics and Registration Service Act (2007) as amended by section 287 of the Health and Social Care Act (2012), Health and Social Care Act 2012 – s261(1) and s261(2)(b)(ii), Health and Social Care Act 2012 – s261(1) and s261(2)(b)(ii), Health and Social Care Act 2012 – s261(2)(b)(ii), Health and Social Care Act 2012 - s261 - 'Other dissemination of information', NHS England De-Identified Data Analytics and Publication Directions 2023

Purposes: Yes, NHS Midlands and Lancashire Commissioning Support Unit (MLCSU) are the sole data controller under this agreement. However MLCSU is not a legal entity as MLCSU forms part of NHS England (NHSE). NHSE are therefore listed as the data controller as the legal entity and MLCSU are listed as a data processor who manage the data. NHS Midlands and Lancashire CSU (MLCSU) is part of The NHS Transformation Unit which is a hosted service of Northern Care Alliance NHS Foundation Trust. Employees accessing the data under this Agreement are employed by Northern Care Alliance NHS Foundation Trust but have an honorary contract with MLCSU. Access to the data supplied under this Agreement is restricted to employees of MLCSU and The NHS Transformation Unit only and access by an employee of Northern Care Alliance NHS Foundation Trust would constitute a breach of the DSA. The purpose of this Agreement is to support contractual and strategic bench-marking across Midlands and Lancashire, for programmes such as planning, commissioning, assessing service quality, performance improvement, and activity and outcomes monitoring. For example, this includes: • provision of analytical intelligence to Integrated Care Boards (ICBs) e.g. for benchmarking of similar health economies or populations in England • in-depth analyses of specific services or pathways to better understand the reasons behind differences in outcomes between health economies • supporting large scale transformation projects involving multiple commissioning organisations • quantitative evaluations and monitoring to estimate the impact of service changes or improvement initiatives The CSU’s customer base consists of: ICBs, Trusts, Local Authorities for the purposes of public health and social care, CQC, Sustainability and Transformation Partnerships, Public Health England, Department of Health, Clinical senates, Strategic clinical networks, NHS England, NHS Improvement, and health charities. Only pseudonymised data is processed under this Agreement. MLCSU wish to retain latest available data previously disseminated data up to 2020/21 of the following data-sets: - Hospital Episode Statistics Critical Care (HES CC) - Hospital Episode Statistics Admitted Patient Care (HES APC) - Hospital Episode Statistics Outpatients (HES OP) - Hospital Episode Statistics Accident and Emergency (HES A&E) (1 year only) - Mental Health Service Dataset (MHSDS) - Secondary Uses Service Payment by Results A&E (SUS PbR A&E) - Secondary Uses Service Payment by Results Outpatients (SUS PbR OP) - Secondary Uses Service Payment by Results Spells (SUS PbR Spells) - Secondary Uses Service Payment by Results Episodes (SUS PbR Episodes) - Civil Registration (deaths) - Patient Reported Outcome Measures (PROMS) - Patient Reported Outcome Measures Linkable to HES - Diagnostic Imaging Dataset (DIDS) - Emergency Care Dataset (ECDS) (1 year only) The specific services and products that will utilise the data are: A. QIPP (Quality Innovation Productivity and Prevention) opportunity packs. These reports provide a summary of performance, cost, and activity levels for individual ICBs/trusts compared with other similar ICBs/trusts. Information in the reports is used to inform strategic planning. Inpatient, outpatient, and A&E hospital services are all included in the reports. The reports were originally produced for ICBs within the CSU's core geography, however, MLCSU has since been asked to produce reports for ICBs across England. The value of these packs in supporting healthcare organisations to assist with their statutory duty to commission/provide high quality and best value services for their populations is clearly proven. B. Development of decision support tools for patients and clinicians to help them make better decisions when deciding whether a patient should undergo a joint replacement procedure. The development of the tools requires advanced statistical analysis to establish the relationship between a range of patient characteristics and procedure outcomes (as measured by PROMs data). Once established, the statistical relationships will be used within the tools to allow a clinician to use individual patient characteristics to provide an estimate of the likely benefit of the procedure for the patient. This extra information can help the patient and clinician make the best informed decision about whether to proceed with the operation. A national panel dataset (i.e. cross-sectional time series data) will ensure that modelled relationship is as robust as possible and maximise the predictive power of the tool (vital given that the tool will be used to support decisions about patient care). A number of local ICBs with programmes aimed at improving orthopaedic services have expressed an interest in piloting the tool to help establish its efficacy. C. Projects on behalf of ICBs and Strategic Clinical Networks (part of NHS England) to model expected future mental health activity levels and capacity requirements. Integral to this work are discussions with clinicians and commissioning stakeholders about the expected impacts of planned changes or interventions (e.g. schemes to increase early diagnosis of mental health conditions). The CSU work with these stakeholder groups to ‘quantify’ their judgements about expected impacts and use these as inputs to statistical models. To inform this process the CSU produce a range of supporting analyses to help participants understand current activity, past trends in activity, and differences between commissioning geographies. The provision of this supporting data is essential for helping stakeholders to make considered and informed estimates, based on a clear understanding of past progress and performance. Without access to record level data, the CSU would not be able to accurately adjust activity in-line with participants' judgements. In particular, the statistical models of future mental health activity that are developed as a result of these discussions would suffer from an increased risk of overestimating the effects of planned changes (due to issues of double counting), which is unavoidable without access to record level data. To provide the supporting materials MLCSU requires national datasets spanning multiple years. The CSU's professional experience is that providing longer-term trends is extremely important when trying to understand the relative contributions of multiple factors to changes in different types of mental health activity. Attempting to rely on shorter time series would limit the value derived from these facilitated modelling exercises and materially increase the risk of making incorrect assumptions about likely future developments. D. Projects on behalf of ICBs to understand how the nature and scale of healthcare utilisation changes as a result of changes in demography. A specific aim of this work is to investigate how patient need, and service utilisation changes towards the end of a person’s life (ONS mortality data is required for this work). MLCSU is developing a new approach to estimating the likely impact of an ageing population on future healthcare demand. The new approach will take into account not only the future size and age structure of a population but also changes in the numbers of people projected to be in their final months of life. Without access to linked national data on hospital activity and mortality this work would not be possible. When constructing statistical models to estimate possible future states the availability of historical data, and in particular, long time series of data is hugely important. Without a good understanding of the statistical relationships between variables over time it is extremely difficult to construct models capable of delivering useful insights about what path the future might take. As part of this project MLCSU will seek to understand how patterns of healthcare utilisation at the end of life have changed over time, for example, in response to advances in medical technology and new treatments. E. Projects to understand longer-term trends. Describing changes in acute utilisation over the long term provides insights that are lost when focusing on the most recent past. Striking reductions, for example, in case mix-adjusted length of stay following an emergency hospital admission or the frequency of admissions to psychiatric inpatient units only really become apparent when viewed over a long time frame. These longer-term perspectives demonstrate the enormous positive changes that have been achieved in the past and can motivate and guide health economies seeking improvements in areas that seem equally intractable. To omit or remove this historical data would eliminate the potential for these insights. The CSU has deployed this kind of longitudinal analysis (going back to pre-2000) recently in support of several Sustainability and Transformation Partnerships as they seek to respond to national requirements. When attempting to understand or explain historical hospital utilisation rates, or forecasting future rates, the longer the time series, the more robust (on average) the explanation or forecast. While for time series models, it might be argued that there are diminishing returns from including ever older data points, this is not necessarily the case for causal models. The CSU are frequently asked to model the potential implications of new models of care. These ‘new’ models are more commonly reinventions or adaptations of earlier models. The ‘NHS Five Year Forward View’ describes a number of new care models which move away from a purchaser-provider split in favour of lead-provider arrangements. To many these proposed models mirror or approximate arrangements that existed in the NHS prior to the development of primary care trusts. If analysed and interpreted appropriately, data relating to these earlier periods can provide useful insights into the unintended consequences of ‘new’ care models, and the CSU are being asked to do this to support STPs and national Vanguards in meeting the national requirements placed upon them. Integrated Care Boards (ICBs) pay for the CSU out of their management allowance which is set by central government, a form of internal SLA within the NHS. Therefore funding for the processing of the data is provided by the NHS through the ICB. MLCSU and the Strategy Unit provide services on behalf of NHSE to other (client) organisations within the health and social care sector. These client organisations will be involved as funders and customers for projects undertaken by the Strategy Unit. The processing under this Agreement is necessary for MLCSU and the Strategy Unit to perform the tasks required of it by NHS England, namely provision of analytical intelligence and support to ICBs and other health and social care organisations. These tasks are objectively necessary for the effective functioning of a publicly-funded healthcare system and as such fall under Article 6(1)(e), performance of a task carried out in the public interest or in the exercise of official authority vested in the controller. The provision of accurate analytical intelligence is necessary for the effective management of the health and social care system and as such the processing under this Agreement falls under Article 9(2)(h) of the GDPR. The data controller have determined that the data requested is the minimum amount necessary and the least intrusive way to achieve the objective of this Agreement. The data is pseudonymised and is required for the effective operation and administration of a publicly-funded healthcare system. The data-sets supplied under this Agreement have an established history of use within the UK healthcare system. Microsoft Limited supply Cloud Services to Midlands and Lancashire Commissioning Support Unit are therefore listed as a data processor. They supply support to the system, but do not access data. Therefore, any access to the data held under this agreement would be considered a breach of the agreement. This includes granting of access to the database[s] containing the data. Lima Networks Ltd supply IT infrastructure and are therefore listed as a data processor. They supply support to the system, but do not access data. Therefore, any access to the data held under this agreement would be considered a breach of the agreement. This includes granting of access to the database[s] containing the data. (Commissioning Support Unit (CSU), internal NHS transfer)

Sensitive: Non Sensitive, and Sensitive, and Non-Sensitive

When:DSA runs 2019-09-01 — 2021-01-31 2017.09 — 2024.09.

Access method: One-Off, Ongoing

Data-controller type: NHS MIDLANDS AND LANCASHIRE COMMISSIONING SUPPORT UNIT, NHS ENGLAND (QUARRY HOUSE)

Sublicensing allowed: No, Yes

Datasets:

1. Bespoke Extract : SUS PbR A&E
2. Bespoke Extract : SUS PbR APC Episodes
3. Bespoke Extract : SUS PbR APC Spells
4. Bespoke Extract : SUS PbR OP
5. Bridge file: Hospital Episode Statistics to Diagnostic Imaging Dataset
6. Office for National Statistics Mortality Data
7. Bridge file: Hospital Episode Statistics to Mortality Data from the Office of National Statistics
8. Hospital Episode Statistics Outpatients
9. Hospital Episode Statistics Accident and Emergency
10. Hospital Episode Statistics Admitted Patient Care
11. Diagnostic Imaging Dataset
12. Hospital Episode Statistics Critical Care
13. Patient Reported Outcome Measures
14. HES:Civil Registration (Deaths) bridge
15. Civil Registration - Deaths
16. Mental Health Services Data Set
17. Mental Health Minimum Data Set
18. Patient Reported Outcome Measures (Linkable to HES)
19. Bridge file: Hospital Episode Statistics to Mental Health Minimum Data Set
20. Secondary Uses Service Payment By Results Episodes
21. Secondary Uses Service Payment By Results Outpatients
22. Secondary Uses Service Payment By Results Spells
23. Secondary Uses Service Payment By Results Accident & Emergency
24. Mental Health and Learning Disabilities Data Set
25. Bespoke Monthly Extract : SUS PbR A&E
26. Bespoke Monthly Extract : SUS PbR APC Episodes
27. Bespoke Monthly Extract : SUS PbR OP
28. Bespoke Monthly Extract : SUS PbR APC Spells
29. Civil Registration (Deaths) - Secondary Care Cut
30. Secondary Uses Service Payment By Results Accident & Emergency
31. Emergency Care Data Set (ECDS)
32. HES-ID to MPS-ID HES Accident and Emergency
33. HES-ID to MPS-ID HES Admitted Patient Care
34. HES-ID to MPS-ID HES Outpatients
35. Civil Registrations of Death - Secondary Care Cut
36. Diagnostic Imaging Data Set (DID)
37. Hospital Episode Statistics Accident and Emergency (HES A and E)
38. Hospital Episode Statistics Admitted Patient Care (HES APC)
39. Hospital Episode Statistics Critical Care (HES Critical Care)
40. Hospital Episode Statistics Outpatients (HES OP)
41. Mental Health and Learning Disabilities Data Set (MHLDDS)
42. Mental Health Minimum Data Set (MHMDS)
43. Mental Health Services Data Set (MHSDS)
44. Patient Reported Outcome Measures (PROMs)

Objectives:

To support contractual and strategic benchmarking across Midlands and Lancashire, for programmes such as planning commissioning and productivity, service quality and performance improvement, and activity and outcomes monitoring for local populations.
The CSU needs:
• The provision of analytically based intelligence for a range of Clinical Commissioning Groups (CCGs) for benchmarking of similar health economies or populations in England, not just in the CSU’s area.
• To provide in depth analysis of all aspects of a specific service areas and allow comparisons with other CCG areas or health economies known to have better outcomes or new/different pathways.
• To support large scale transformation projects that may impact several commissioners (CCGs)
• Descriptive analyses of healthcare needs, demands or supply including comparisons between providers, commissioners and geographical areas, analysis over time and of the characteristics of patients and the services they receive.
• Retrospective analyses exploring the reasons for observed changes in healthcare provision and health outcomes
• Prospective modelling of the impact of planned or proposed changes in healthcare services on healthcare activity, travel times and resource use
• Quantitative evaluations and monitoring estimating the impact of service redesign of improvement initiatives on healthcare and outcomes
• To develop tools and information packs to support patients, clinicians, commissioners and providers to make informed decisions about healthcare service provision, organisation and strategy

The specific services and products that will utilise the data are the following :-
A. QIPP (Quality Innovation Productivity and Prevention) opportunity packs which provide a summary of performance, cost and activity levels for individual CCGs/trusts compared to other local CCGs/trusts. The packs include aggregate analysis in relation to QIPP priorities covering Inpatient, Outpatient and A&E but are subject to change in line with the QIPP programme. These packs were originally produced for those CCGs within the CSU's core geography (Birmingham and the Black County). However the CSU have now been requested to provide packs for a wider range of CCGs and trusts including all Staffordshire, Lancashire, Herefordshire, Worcestershire, Shropshire and Telford and Wrekin. The CSU have also had requests from as far afield as Cornwall. The value of these packs (as demonstrated by the willingness to pay) in supporting CCGs/trusts to assist with their statutory duty to commission/provide high quality and best value services for their populations is clearly proven and as such the CSU will be offering the packs to all CCGs trusts in England. In addition to the wider provision of packs the CSU's existing customers have also requested that the packs be enhanced to offer comparisons against national nearest neighbour comparators or bespoke comparators (for example Birmingham combined CCGs compared with other large cities). Customers for the packs also can request ‘deep dive’ analyses to explore identified opportunities in greater detail

B. Development of decision support tools for clinicians to help them make better decisions when deciding whether a patient is suitable for Hip or knee replacement procedures. The development of the tools requires sophisticated statistical analysis to establish the relationship between a range of patient characteristics and procedure outcomes (as measured by PROMs data). The statistical relationships will be used within the tools whereby it will allow a clinician to input patient characteristics and provide an estimate of the likely benefit of the procedure for the patient. This additional information can help both the patient and their clinician make the best informed decision about whether to proceed with the operation. In order to ensure that that relationship is as robust as possible and to maximise the predictive power of the tool (which is vital given that the tool will be used to support important decisions about patient care) a full national dataset is required. In order to further validate the relationship and establish its robustness over time (which will be important for clinician and patient confidence in the tool) the CSU will be carrying out the analysis on all data years. The development of these tools will establish a prototype for the development of other similar products for other procedures where data is available through the PROMS dataset such as Varicose vein surgery etc. However for the purposes of this request the CSU are requesting only PROMS data relating to hip and knee procedures. A number of Local CCGs with programmes aimed at improving orthopaedic services (across all of Staffordshire for example) have confirmed that they plan to put this tool into practice on an initial pilot basis as soon as it is available. The CSU have also been approached by a number of other CCGs who have indicated that they would also be interested in applying the tool once its efficacy has been established.

C. Projects on behalf of CCGs and Strategic Clinical Networks (part of NHS England) to model expected future Mental Health activity levels and capacity requirements within a CCG after taking into account the impact of projected demographic changes and also the potential impact of mental health prevention strategies, admission avoidance strategies and length/intensity of treatment reduction strategies. An integral part of this work is to elicit modelling parameters from clinicians and commissioning stakeholders relating to expected impacts on activity levels as a result of planned changes or interventions. In order to do this the CSU produce a range of supporting analyses to help them to understand current activity levels, trends in activity and also how they compare with others. Provision of this supporting data is key to helping stakeholders to make considered and robust estimates based on a clear understanding of past progress and performance against other relevant comparators. In order to provide this comparative benchmarking the CSU require full national datasets covering multiple years. As the CSU are requesting the full set of historical data, they felt it important to clarify their rationale for doing so. In terms of the number of years of data requested, the CSU's professional experience has shown that providing longer term trends (in excess of 5 years) is often important, given the level of variation that exists, in order to evidence general trends. Being able to show local trends in the context of national trends is also essential for sophisticated interpretation. Shorter time series can often be misleading in this respect and as such could result in incorrect assumptions about future levels of demand.

D. Projects on behalf of CCGs to understand how the nature and scale of healthcare utilisation changes as a result of changes in demographics. One specific aim of this work (for which ONS mortality data is required) is to investigate how patient need, demand and service utilisation changes towards the end of a person’s life. In addition it will also allow the CSU to develop a new approach to estimating the likely impact of an ageing population on future healthcare demand. The new approach will take into account not only the future size and age structure of a population but also changes in the proportion of the population who are estimated to be in their final months of life. It is also worth noting that NHS England have expressed interest in the CSU's development of this method of forecasting future demand as part of their national Fit for the Future programme (FFF).

The project requires national level datasets in order that the analysis is as statistically robust as possible. It will also allow the CSU to establish the extent to which utilisation prior to death varies across the country. Benchmarking analysis (including historical trend analysis) will be carried out in order to provide estimates of the potential scale of opportunity for reducing acute healthcare activity (or developing alternatives to acute provision) for those patients at the end of life. Benchmarking and trend analysis will also enable the identification of those Trusts or CCGs who may be more advanced in end of life care provision. As part of this project the CSU will also be considering how patterns of utilisation at the end of life have changed over time (advances in medical technology and new treatments will certainly have had an impact on levels of service utilisation particularly for older people). Long term trends in excess of five years will be important in order to identify and have confidence in historical trends and applying these trends to future estimates.

E. Other specific projects are:

1 Describing changes in acute utilisation over the long term provides insights that are lost when focusing on the most recent past. Striking reductions, for example, in casemix-adjusted length of stay following an emergency acute hospital admission or the frequency of admissions to psychiatric inpatient units only really become apparent when viewed over a long time frame. These longer-term perspectives demonstrate the enormous positive changes that have been achieved in the past and can motivate and guide health economies seeking improvements in areas that seem equally intractable. To delete older data would eliminate the potential for these insights. The CSU have deployed this kind of longitudinal analysis (going back to pre-2000) recently in support of several Sustainability and Transformation Plans (STPs - compromising of CCGs, trusts, Foundation Trusts, Local Authorities and other key local partners) as they seek to address the requirements placed upon them nationally.

2 When explaining historical acute hospital utilisation rates, or forecasting future rates, the longer the time series, the more robust (on average) the explanation or forecast. Whilst for time series models, it might be argued the diminishing returns result from adding very old data points, this is not necessarily the case for causal models.

3 The CSU are frequently asked to model the potential implications of new models of care. These ‘new’ models are more commonly reinventions or adaptations of earlier models. The ‘NHS Five Year Forward View’ describes a number of new care models which move away from a purchaser-provider split in favour of lead-provider arrangements. To many these proposed models mirror or approximate arrangements that existed in the NHS prior to the development of primary care trusts. If analysed and interpreted appropriately, data relating to these earlier periods can provide useful insights into the unintended consequences of ‘new’ care models and the CSU are being asked to do this to support STPs and national Vanguards in meeting the national requirements placed upon them.

Data will only be used for the purposes outlined above, and any requirement to change the purpose will be subject to a separate request to NHS Digital.

Yielded Benefits:

MLCSU's work is dedicated to helping commissioners, providers, charities, and government to solve complex problems by providing evidence-informed analysis and advice. This is carried out as better evidence leads to improved decision making and implementation. A. QIPP opportunity packs—these reports have facilitated commissioners to target interventions for reducing acute hospital activity. B. PROMS—this work has helped commissioners to determine whether rates of orthopaedic surgery are the result of differences in clinical thresholds for surgery. C. Mental Health activity modelling—the modelling in this area has provided powerful evidence of the need to integrate mental and physical health care, this is borne out by NHS England commissioning the CSU to produce reports for all 44 STPs. D: Impact of demography—this work has been used by commissioners in strategic plans that set the foundations for planning and contracting future levels of healthcare activity and expenditure. MLCSU suggests that that the value of its work is perhaps best judged based on feedback from its customers. "The Strategy Unit are inspiring in their commitment, dedication to evidence and use of innovative analysis as a way to improve health and care." Professor Sir Bruce Keogh—National Medical Director, NHS England "I just wanted to drop you a note to say how impressed I have been with the work you and the team have recently undertaken on the 'Making the Case for Integrating Mental and Physical Health Care' report. The product you provided was extremely well researched and presented. The delivery to the MH Alliance board generated significant discussion across the system. On a personal level, the presentation and report gave me a more informed narrative and evidence to help me further drive the West Midlands Mental Health Commission priorities whilst ensuring we adopt a whole population level health promotion approach. I would urge other areas to commission this report as a baseline assessment to develop a better understanding of the potential integration opportunities across STP and local footprints". Superintendent Sean Russell—Mental Health Lead, West Midlands Combined Authority "The Black Country STP has been an early adopter of this important study by the Strategy Unit. We saw its potential to inspire a transformation of our response to the physical health needs of mental health service users so we commissioned an earlier version to inform the development of our plan. Some of the differentials in both health outcomes and health service utilisation are chastening but we have been able to use these findings (and the summary of the evidence base provided) to begin building a broad coalition of local partners to identify and implement practical changes. I commend it enthusiastically to colleagues as a catalyst for much needed change". Andy Williams—Accountable Officer, Sandwell and West Birmingham CCG.

Expected Benefits:

As described by the examples listed above, the CSU’s work provides customers (CCGs, Trusts, Local Authorities for the purposes of public health and social care, CQC, Sustainability and Transformation Partnerships, Public Health England, Department of Health, Clinical senates, Strategic clinical networks, NHS England, NHS Improvement, and health charities) with understanding and insight that enables them to make the best decisions about the healthcare services they commission or provide. Improved decision making will have a direct effect on the quality of care and outcomes for patients. The CSU's work is limited to the health and social care arena and outputs will be used only by health and social care organisations.

With respect to the outputs listed above, the CSU wishes to highlight the following benefits:

A. QIPP opportunity packs – these reports continue to help focus commissioner plans and direct resources to areas most likely to lead to improvements in quality, outcomes, and cost savings.

B. PROMS decision support tool – the CSU’s work on a decision support tool is aimed at helping patients and clinicians make improved joint decisions about whether to undergo joint replacement surgery. This would help to minimise both the financial cost of such procedures and avoid unnecessary pain and risk for some patients who are unlikely to experience benefits.

C. Mental Health activity modelling – in 2017, NHS England commissioned the CSU to produce a report titled "Making the Case for Integrating Mental and Physical Health Care" for all 44 STPs. The objectives of the report were to highlight the level of health inequalities experienced by users of specialist mental health services, to provide insight on the use of acute hospital services by mental health service users, identify groups that could benefit from targeted interventions, and provide a summary of effective interventions for improving the physical health of mental health service users. An integrated mental and physical health approach is one of the three priority actions described in the Five Year Forward View for Mental Health. In 2017-18, the CSU are committed to further work focussed on some of the issues uncovered by the report.

D. Impact of demography – understanding how demographic change will impact on population healthcare use is a central question for healthcare planners. It sets the scale of the financial challenge in health economy plans and underpins all large scale healthcare reconfigurations and long-term healthcare contracts. Overstating or understating the impact of demographic pressures may lead a health economy to set unduly radical or conservative plans for cost savings, by helping health economies to produce improved estimates of the likely impact this risk is mitigated.

Palliative and end of life care – improving palliative and end of life care is a Department of Health commitment
https://www.gov.uk/government/publications/choice-in-end-of-life-care-government-response The CSU’s reports in this area highlight variation and provide greater transparency around current practice.

The CSU are not the end user of the outputs they produce, however they regularly receive positive feedback from their customers and currently receive repeat business from around 75% of customers.

Note on CSU's customer base:
With the introduction of STPs, the CSU's customer base has rapidly become a collective local 'health and care economy', comprising a number of different organisation types within the NHS. For this reason, all parties involved in STPs are referred to as customers. Depending on how an individual STP chooses to operates, different members may be directed to take responsibly for particular programmes of work such that the CSU could be directly commissioned by any member organisation on behalf of the collective STP.

In 2016, Public Health England appointed the CSU to a 4-year framework agreement to supply data science and health impact assessment services.

In 2017, NHS England commissioned the CSU to produce a report titled "Making the Case for Integrating Mental and Physical Health Care" for all 44 Sustainability and Transformation Partnerships. The end users of these reports are all the organisations that make up local STPs.

Outputs:

Previous outputs:
A. QIPP opportunity packs – these reports provide in-depth information to support commissioning organisations in developing their strategic plans. The focus of these reports is comparative information on utilisation rates for subsets of acute hospital activity (inpatient, outpatient, and A&E) that are amenable to interventions targeted at reducing levels of acute hospital activity.

The reports are bespoke to individual commissioning organisations and are only provided to those organisations that place an order. The CSU have produced similar reports for several years. In a typical year the CSU might expect to produce about 30 such reports.

B. PROMS decision support tool – development work to test the concept of a tool that allows clinicians to use patient characteristics to obtain an estimate of likely benefit from receiving a joint replacement procedure.

C. Mental Health activity modelling – in 2017, the CSU undertook a substantial project looking at the physical health of people who use mental health services. The CSU produced a series of analyses that highlighted significantly poorer health outcomes for mental health patients. The CSU produced locally-focussed reports for a number of commissioning organisations, before NHS England commissioned the CSU to produce a report titled "Making the Case for Integrating Mental and Physical Health Care" for all 44 STPs.

D. Impact of demography – for several years the CSU have produced reports that provide in-depth analysis of the likely impact of demographic changes on future acute healthcare utilisation. The focus of these reports is the effect of changes in population size, age structure and health status on levels of acute healthcare activity across a range of delivery points. The reports are bespoke to individual commissioning organisations and are only provided to those organisations that place an order. In a typical year the CSU might expect to produce about 30 such reports.

In 2017, the CSU produced a report for NHS England describing the context and status of end of life care services across the West Midlands Region. Sustainability and Transformation Partnerships need to include proposals to improve choice in end of life care in their strategic plans. A second report focussed on palliative and end of life care for children and young people was later commissioned by NHS England to help understand characteristics and levels of resource required by children with life-limiting and or life-threatening conditions.

All our reports/outputs conform to relevant legislation and guidance with respect to confidentiality and other important considerations.


Planned outputs:
A. QIPP opportunity packs – as in previous years, the CSU has been tasked with producing reports that provide in-depth information to support commissioning organisations in developing their strategic plans. The CSU expect to produce about 30 such reports in 2017-18. In 2017-18, the CSU have been asked to further develop the reports to include a version suitable for Sustainability and Transformation Partnerships (STPs).

B. PROMS decision support tool – development work to test the concept of a tool that allows clinicians to use patient characteristics to obtain an estimate of likely benefit from receiving a joint replacement procedure. In particular, the CSU have been asked to consider the relationship between surgeon specialisation and patient outcomes. Most studies looking at the relationship between surgical activity and outcomes have focussed on procedure volume i.e. the volume-outcome relationship. But recently, the existence of a specialisation-outcome that is independent of the volume-outcome relationship has been advanced.

C. Mental Health activity modelling – the CSU expect to produce a number of follow-up analyses based on previous work looking at the physical health of people who use mental health services. The exact focus of this work is yet to be confirmed but may include in-depth reviews of specific patient groups e.g. CAMHS, substance misuse; pathway modelling; or exploring relationships with other datasets e.g. primary care, IAPT.

D. Impact of demography – as in previous years, the CSU has been tasked with producing reports that provide in-depth analysis of the likely impact of demographic changes on future acute healthcare utilisation. The CSU expect to produce about 30 such reports in 2017-18. In 2017-18, the CSU have been asked to further develop the reports to include a version suitable for Sustainability and Transformation Partnerships (STPs).

In 2017-18 the CSU has been tasked with further developing our methods for understanding the impact of demographic changes on future healthcare utilisation. The CSU intend to do this by drawing on the relationship between healthcare use and proximity to death. The proposed methods will require combining mortality data and hospital activity data.

Processing:

The data will be stored on a secure server and accessed through a SQL server database by a small group of named analytical staff working within the Strategy Unit of the CSU. Those staff are based at the premises detailed in this application (Kingston House). The data in its raw form will not be loaded into any tool or provided as part of any product or output. All outputs will contain only data which is aggregated, with small numbers suppressed in line with the HES Analysis Guide.

SUS PBR
As detailed in the “Objectives section” (Objective A) accessing the national SUS PBR data will enable the CSU to offer the QIPP packs to all CCGs/trusts in England as well as allowing the CSU to improve the packs through the use of better comparative groups (i.e. nearest neighbours). In producing these packs the data required is extracted using SQL server and analysed using MS Excel to produce the charts and tables included within the packs.

PROMS
As detailed in the “Objectives” section (Objective B) the PROMS data will be used to develop a decision support tool, PROMS data will be extracted from SQL server and analysed using appropriate statistical analysis software (STATA or R) in order to establish the relationship between a range of patient characteristics (e.g. age, gender, co-morbidities) and the procedure outcomes based on PROM scores.
The tool that will be developed will not contain any patient data. The tool that will be provided to the customer(s) will only contain a mathematical algorithm based on the established statistical relationships between patient characteristics and outcomes.

Mental Health Minimum Dataset (MHMDS)
The MHMDS will be used to model expected future activity levels and capacity requirements within CCGs after taking into account the impact of projected demographic changes and also the potential impact of mental health prevention strategies, admission avoidance strategies and length/intensity of treatment reduction strategies. Patient level data is required to enable the CSU to adjust and remove activity in line with expected changes. Using patient level data also allows the CSU minimise the impact of overestimating impacts as a result of double counting which is not possible with aggregate data.
As outlined in the “objective” section the data will be used in two ways firstly it will be used to provide supporting benchmarking and historical trend analyses to support modelling parameter setting. For this aspect of the project data extracts will be produced using SQL server and downloaded into MS Excel to produce the charts and tables required.

Secondly it will be used to create a model to estimate future activity levels after accounting for changes in demographics and the impact of changes to service provision. The model will be constructed using SQL server to process the data applying any modelling factors and parameters. Aggregate output files from SQL server will be downloaded and analysed in MS Excel in order to produce the required charts and tables for inclusion in reports.
The dataset will also be used to develop prospective intervention specific models to estimate changes in mental health team activity levels and the scale of potential savings as a result of the introduction of specific strategies to reduce the need for mental health services. These strategies may include, for example, schemes to increase early diagnosis of mental health conditions. This will help the CCG to better understand the costs and benefits of proposed changes allowing them to make better decisions about the effective use of commissioning resources. As with the higher level modelling in order to develop specific intervention impact models requires the production of benchmarking and trend analyses to help the customer to make judgments on the likely scale of impact of specific interventions. These judgments are incorporated into the model so it is important that they are based as far as possible on the best available data available. These prospective models will be constructed within SQL server and aggregate outputs downloaded into MS Excel to produce required outputs. The reports and any accompanying data tables will contain only data which is aggregated, with small numbers suppressed in line with the HES Analysis Guide.

ONS mortality data
As detailed in the “Objectives” section (Objective D) the ONS mortality data combined with the national HES data will be used to understand how the nature and scale of healthcare utilisation changes as a result of changes in demographics. It will also allow the CSU to develop a new approach to estimating the impact of an ageing population on future healthcare demand. As with the other datasets the ONS data will be stored within a SQL server database and the data required for this analysis will be extracted and analysed within MS Excel or other appropriate statistical software packages such as STATA or R in order to establish the mathematical relationship between proximity to death and healthcare utilisation which can be used in future (and potentially some of the current modelling work outlined in this document). During these data transfers into appropriate analysis software packages the data will not leave the secure environment.

Any other projects that may make use of this work (for example the NHS England Fit For the Future programme) would only utilise the methodology derived from this project and would not use the actual ONS data. The reports and any accompanying data tables will contain only data which is aggregated, with small numbers suppressed in line with the HES Analysis Guide.

Across all of the above processing, processing will be only carried out by CSU staff with the appropriate governance and access.

The data will not be used to link at record level to other datasets (other than where already provided in linked or bridging form by NHS Digital). The data may however be linked to organisational level data such as already exists within the public domain.

For clarity, the DSCRO may not process the data for the CSU other than initially downloading the data and storing it on the servers accessible by the CSU, and hence is not listed as a data processor.

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DSfC - STP - NHS Staffordshire and Stoke on Trent CCGs - Comm — DARS-NIC-234915-J3K4V

Type of data: information not disclosed for TRE projects

Opt outs honoured: No - data flow is not identifiable, Anonymised - ICO Code Compliant (Does not include the flow of confidential data)

Legal basis: Health and Social Care Act 2012 – s261(1) and s261(2)(b)(ii), Health and Social Care Act 2012 - s261 - 'Other dissemination of information', Health and Social Care Act 2012 – s261(1) and s261(2)(b)(ii), Health and Social Care Act 2012 – s261(2)(b)(ii)

Purposes: No (Commissioning Support Unit (CSU), Clinical Commissioning Group (CCG), Sub ICB Location)

Sensitive: Sensitive

When:DSA runs 2019-02-01 — 2022-01-31 2020.02 — 2021.05.

Access method: Frequent Adhoc Flow, One-Off

Data-controller type: NHS CANNOCK CHASE CCG, NHS EAST STAFFORDSHIRE CCG, NHS NORTH STAFFORDSHIRE CCG, NHS SOUTH EAST STAFFORDSHIRE AND SEISDON PENINSULA CCG, NHS STAFFORD AND SURROUNDS CCG, NHS STOKE ON TRENT CCG, NHS STAFFORDSHIRE AND STOKE-ON-TRENT ICB - 04Y, NHS STAFFORDSHIRE AND STOKE-ON-TRENT ICB - 05D, NHS STAFFORDSHIRE AND STOKE-ON-TRENT ICB - 05G, NHS STAFFORDSHIRE AND STOKE-ON-TRENT ICB - 05Q, NHS STAFFORDSHIRE AND STOKE-ON-TRENT ICB - 05V, NHS STAFFORDSHIRE AND STOKE-ON-TRENT ICB - 05W

Sublicensing allowed: No

Datasets:

1. Acute-Local Provider Flows
2. Ambulance-Local Provider Flows
3. Children and Young People Health
4. Civil Registration - Births
5. Civil Registration - Deaths
6. Community Services Data Set
7. Community-Local Provider Flows
8. Demand for Service-Local Provider Flows
9. Diagnostic Imaging Dataset
10. Diagnostic Services-Local Provider Flows
11. Emergency Care-Local Provider Flows
12. Experience, Quality and Outcomes-Local Provider Flows
13. Improving Access to Psychological Therapies Data Set
14. Maternity Services Data Set
15. Mental Health and Learning Disabilities Data Set
16. Mental Health Minimum Data Set
17. Mental Health Services Data Set
18. Mental Health-Local Provider Flows
19. National Cancer Waiting Times Monitoring DataSet (CWT)
20. Other Not Elsewhere Classified (NEC)-Local Provider Flows
21. Population Data-Local Provider Flows
22. Primary Care Services-Local Provider Flows
23. Public Health and Screening Services-Local Provider Flows
24. SUS for Commissioners
25. e-Referral Service for Commissioning
26. National Diabetes Audit
27. Patient Reported Outcome Measures
28. Personal Demographic Service
29. Summary Hospital-level Mortality Indicator
30. National Cancer Waiting Times Monitoring DataSet (NCWTMDS)
31. Improving Access to Psychological Therapies Data Set_v1.5
32. Adult Social Care
33. Medicines dispensed in Primary Care (NHSBSA data)
34. Civil Registrations of Death
35. Community Services Data Set (CSDS)
36. Diagnostic Imaging Data Set (DID)
37. Improving Access to Psychological Therapies (IAPT) v1.5
38. Mental Health and Learning Disabilities Data Set (MHLDDS)
39. Mental Health Minimum Data Set (MHMDS)
40. Mental Health Services Data Set (MHSDS)
41. Patient Reported Outcome Measures (PROMs)
42. Summary Hospital-level Mortality Indicator (SHMI)

Objectives:

Commissioning
The Staffordshire CCG's have come together to improve health and care, currently they have moved to one senior management board and plan to formally join as one entity in 2020.

The Six CCG's are as follows:
NHS East Staffordshire CCG
NHS Cannock Chase CCG
NHS North Staffordshire CCG
NHS South East Staffs & Seisdon Peninsula CCG
NHS Stafford and Surrounds CCG
NHS Stoke on Trent CCG

Sustainability and Transformation Partnerships build on collaborative work that began under the NHS Shared Planning Guidance for 2016/17 – 2020/21, to support implementation of the Five Year Forward View. They are supported by six national health and care bodies: NHS England; NHS Improvement; the Care Quality Commission (CQC); Health Education England (HEE); Public Health England (PHE) and the National Institute for Health and Care Excellence (NICE).

The joint collaboration will be responsible for implementing large parts of the 5 year forward view from NHS England. The collaboration will be implementing several initiatives:

- Putting the patient at the heart of the health system
- Working across organisational boundaries to deliver care and including social care, public Health, providers and GPs as well as CCGs
- Reviewing patient pathways to improve patient experience whilst reducing costs e.g. reduce the number of standard tests a patient may have and only have the ones they need
- Planning the demand and capacity across the healthcare system across the 6 CCGs to ensure we have the right buildings, services and staff to cope with demand whilst reducing the impact on costs
- Working to prevent or capture conditions early as they are cheaper to treat
- Introduce initiatives to change behaviours e.g. move more care into the community
- Patient pathway planning for the above

To ensure the patient is at the heart of care, the collaboration is focussing on where services are required across the geographical region. This assists to ensure delivery of care in the right place for patients who may move and change services across CCGs.

The CCG's will work proactively and collaboratively to redesign services across boundaries to integrate services. Collaborative sharing is required for CCGs to understand these requirements.

The CCGs will 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 geographical area of Staffordshire.
The CCGs commission services from a range of providers covering a wide array of services. Each of the data flow categories requested supports the commissioned activity of one or more providers.

The following pseudonymised datasets are required to provide intelligence to support commissioning of health services:
- Secondary Uses Service (SUS+)
- Local Provider Flows
o Acute
o Ambulance
o Community
o Demand for Service
o Diagnostic Service
o Emergency Care
o Experience, Quality and Outcomes
o Mental Health
o Other Not Elsewhere Classified
o Population Data
o Primary Care Services
o Public Health Screening
- Mental Health Minimum Data Set (MHMDS)
- Mental Health Learning Disability Data Set (MHLDDS)
- Mental Health Services Data Set (MHSDS)
- Maternity Services Data Set (MSDS)
- Improving Access to Psychological Therapy (IAPT)
- Child and Young People Health Service (CYPHS)
- Community Services Data Set (CSDS)
- Diagnostic Imaging Data Set (DIDS)
- National Cancer Waiting Times Monitoring Data Set (CWT)
- Civil Registration Births and Deaths Data (CRD)

The pseudonymised data is required to for the following purposes:
 Population health management:
• Understanding the interdependency of care services
• Targeting care more effectively
• Using value as the redesign principle
 Data Quality and Validation – allowing data quality checks on the submitted data
 Thoroughly investigating the needs of the population, to ensure the right services are available for individuals when and where they need them
 Understanding cohorts of residents who are at risk of becoming users of some of the more expensive services, to better understand and manage those needs
 Monitoring population health and care interactions to understand where people may slip through the net, or where the provision of care may be being duplicated
 Modelling activity across all data sets to understand how services interact with each other, and to understand how changes in one service may affect flows through another
 Service redesign
 Health Needs Assessment – identification of underlying disease prevalence within the local population
 Patient stratification and predictive modelling - to identify specific patients at risk of requiring hospital admission and other avoidable factors such as risk of falls, computed using algorithms executed against linked de-identified data, and identification of future service delivery models

The pseudonymised data is required to ensure that analysis of health care provision can be completed to support the needs of the health profile of the population within the CCG area based on the full analysis of multiple pseudonymised datasets.

Processing for commissioning will be conducted by Midlands and Lancashire Commissioning Support Unit.

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.
14. Reviewing current service provision
15. Cost-benefit analysis and service impact assessments to underpin service transformation across health economy
a. Service planning and re-design (development of NMoC and integrated care pathways, new partnerships, working with new providers etc.)
b. Impact analysis for different models or productivity measures, efficiency and experience
c. Service and pathway review
d. Service utilisation review
16. Ensuring compliance with evidence and guidance
a. Testing approaches with evidence and compliance with guidance.
17. Monitoring outcomes
a. Analysis of variation in outcomes across population group
18. Understanding how services impact across the health economy
a. Service evaluation
b. Programme reviews
c. Analysis of productivity, outcomes, experience, plan, targets and actuals
d. Assessing value for money and efficiency gains
e. Understanding impact of services on health inequalities
19. Understanding how services impact on the health of the population and patient cohorts
a. Measuring and assessing improvement in service provision, patient experience & outcomes and the cost to achieve this
b. Propensity matching and scoring
c. Triple aim analysis
20. Understanding future drivers for change across health economy
a. Forecasting health and care needs for population and population cohorts across STPs
b. Identifying changes in disease trends and prevalence
c. Efficiencies that can be gained from procuring services across wider footprints, from new innovations
d. Predictive modelling
21. Delivering services that meet changing needs of population
a. Analysis to support policy development
b. Ethical and equality impact assessments
c. Implementation of NMOC
d. What do next years contracts need to include?
e. Workforce planning
22. Maximising services and outcomes within financial envelopes across health economy
a. What-if analysis
b. Cost-benefit analysis
c. Health economics analysis
d. Scenario planning and modelling
e. Investment and disinvestment in services analysis
f. Opportunity analysis

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:
a. Patients at highest risk of admission
b. Most expensive patients (top 15%)
c. Frail and elderly
d. Patients that are currently in hospital
e. Patients with most referrals to secondary care
f. Patients with most emergency activity
g. Patients with most expensive prescriptions
h. Patients recently moving from one care setting to another
i. Discharged from hospital
ii. Discharged from community
13. Profiling population health and wider determinants to identify and target those most in need
a. Understanding population profile and demographics
b. Identify patient cohorts with specific needs or who may benefit from interventions
c. Identifying disease prevalence. health and care needs for population cohorts
d. Contributing to Joint Strategic Needs Assessment (JSNA)
e. Geographical mapping and analysis
14. Identifying and managing preventable and existing conditions
a. Identifying types of individuals and population cohorts at risk of non-elective re-admission
b. Risk stratification to identify populations suitable for case management
c. Risk profiling and predictive modelling
d. Risk stratification for planning services for population cohorts
e. Identification of disease incidence and diagnosis stratification
15. Reducing health inequalities
a. Identifying cohorts of patients who have worse health outcomes typically deprived, ethnic groups, homeless, travellers etc. to enable services to proactively target their needs
b. Socio-demographic analysis
16. Managing demand
a. Waiting times analysis
b. Service demand and supply modelling
c. Understanding cross-border and overseas visitor
d. Winter planning
e. Emergency preparedness, business continuity, recovery and contingency planning
17. Care co-ordination and planning
a. Planning packages of care
b. Service planning
c. Planning care co-ordination
18. Monitoring individual patient health, service utilisation, pathway compliance experience & outcomes across the heath and care system
a. Patient pathway analysis across health and care
b. Outcomes & experience analysis
c. Analysis to support services to react to terror situations
d. Analysis to identify vulnerable patients with potential safeguarding issues
e. Understanding equity of care and unwarranted variation
f. Modelling patient flow
g. Tracking patient pathways
h. Monitoring to support New Models of Care (NMOC), Accountable Care Organisations (ACO), Sustainable Transformation Partnerships (STP)
i. Identifying duplications in care
j. Identifying gaps in care, missed diagnoses and triple fail events
k. Analysing individual and aggregated timelines
19. Undertaking budget planning, management and reporting
a. Tracking financial performance against plans
b. Budget reporting
c. Tariff development
d. Developing and monitoring capitated budgets
e. Developing and monitoring individual-level budgets
f. Future budget planning and forecasting
g. Paying for care of overseas visitors and cross-border flow
20. Monitoring the value for money
a. Service-level costing & comparisons
b. Identification of cost pressures
c. Cost benefit analysis
d. Equity of spend across services and population cohorts
e. Finance impact assessment
21. Comparing population groups, peers, national and international best practice
a. Identification of variation in productivity, cost, outcomes, quality, experience, compared with peers, national and international & best practice
b. Benchmarking against other parts of the country
c. Identifying unwarranted variations
22. Comparing expected levels
a. Standardised comparisons for prevalence, activity, cost, quality, experience, outcomes for given populations
23. Comparing local targets & plan
a. Monitoring of local variation in productivity, cost, outcomes, quality and experience
b. Local performance dashboards by service provider, commissioner, geography, NMOC, STPs
24. Monitoring activity and cost compliance against contract and agreed plans
a. Contract monitoring
b. Contract reconciliation and challenge
c. Invoice validation
25. Monitoring provider quality, demand, experience and outcomes against contract and agreed plans
a. Performance dashboards
b. CQUIN reporting
c. Clinical audit
d. Patient experience surveys
e. Demand, supply, outcome & experience analysis
f. Monitoring cross-border flows and overseas visitor activity
26. Improving provider data quality
a. Coding audit
b. Data quality validation and review
c. Checking validity of patient identity and commissioner assignment

Processing:

Data must only be used as stipulated within this Data Sharing Agreement.
 
Data Processors must only act upon specific instructions from the Data Controller.
 
Data can only be stored at the addresses listed under storage addresses.
 
Patient level data will not be shared outside of the CCG unless it is for the purpose of Direct Care, where it may be shared only with those health professionals who have a legitimate relationship with the patient and a legitimate reason to access the data.
 
All access to data is managed under Roles-Based Access Controls
 
No patient level data will be linked other than as specifically detailed within this agreement. Data will only be shared with those parties listed and will only be used for the purposes laid out in the application/agreement. The data to be released from NHS Digital will not be national data, but only that data relating to the specific locality and that data required by the applicant.
 
NHS Digital reminds all organisations party to this agreement of the need to comply with the Data Sharing Framework Contract requirements, including those regarding the use (and purposes of that use) by “Personnel” (as defined within the Data Sharing Framework Contract ie: employees, agents and contractors of the Data Recipient who may have access to that data)

Segregation
Where the Data Processor and/or the Data Controller hold both identifiable and pseudonymised data, the data will be held separately so data cannot be linked.

All access to data is auditable by NHS Digital.

Data Minimisation
Data Minimisation in relation to the data sets listed within section 3 are listed below. This also includes the purpose on which they would be applied -

• Patients who are normally registered and/or resident within NHS East Staffordshire CCG, NHS Cannock Chase CCG, NHS North Staffordshire CCG, NHS South East Staffs & Seisdon Peninsula CCG, NHS Stafford and Surrounds CCG and NHS Stoke on Trent CCG (including historical activity where the patient was previously registered or resident in another commissioner).
and/or
• Patients treated by a provider where NHS East Staffordshire CCG, NHS Cannock Chase CCG, NHS North Staffordshire CCG, NHS South East Staffs & Seisdon Peninsula CCG, NHS Stafford and Surrounds CCG and NHS Stoke on Trent CCG is the host/co-ordinating commissioner and/or has the primary responsibility for the provider services in the local health economy – this is only for commissioning and relates to both national and local flows.
and/or
• Activity identified by the provider and recorded as such within national systems (such as SUS+) as for the attention of NHS East Staffordshire CCG, NHS Cannock Chase CCG, NHS North Staffordshire CCG, NHS South East Staffs & Seisdon Peninsula CCG, NHS Stafford and Surrounds CCG and NHS Stoke on Trent CCG - this is only for commissioning and relates to both national and local flows.


For clarity, any access by LIMA and Blackpool Teaching Hospitals to data held under this agreement would be considered a breach of the agreement. This includes granting of access to the database[s] containing the data.

Commissioning
The Data Services for Commissioners Regional Office (DSCRO) obtains the following data sets:
1. SUS+
2. Local Provider Flows (received directly from providers)
a. Acute
b. Ambulance
c. Community
d. Demand for Service
e. Diagnostic Service
f. Emergency Care
g. Experience, Quality and Outcomes
h. Mental Health
i. Other Not Elsewhere Classified
j. Population Data
k. Primary Care Services
l. Public Health Screening
3. Mental Health Minimum Data Set (MHMDS)
4. Mental Health Learning Disability Data Set (MHLDDS)
5. Mental Health Services Data Set (MHSDS)
6. Maternity Services Data Set (MSDS)
7. Improving Access to Psychological Therapy (IAPT)
8. Child and Young People Health Service (CYPHS)
9. Community Services Data Set (CSDS)
10. Diagnostic Imaging Data Set (DIDS)
11. National Cancer Waiting Times Monitoring Data Set (CWT)
12. Civil Registries Data – Births and Deaths (CRD)
Data quality management and pseudonymisation is completed within the DSCRO and is then disseminated as follows:

Midlands and Lancashire Commissioning Support Unit
1. Pseudonymised SUS+, Local Provider data, Mental Health data (MHSDS, MHMDS, MHLDDS), Maternity data (MSDS), Improving Access to Psychological Therapies data (IAPT), Child and Young People’s Health data (CYPHS), Community Services Data Set (CSDS). Diagnostic Imaging data (DIDS), National Cancer Waiting Times Monitoring Data Set (CWT) and Civil Registries Data – Births and Deaths (CRD) only is securely transferred from the DSCRO to Midlands and Lancashire Commissioning Support Unit.
2. Midlands and Lancashire Commissioning Support Unit will add derived fields, link data and provide analysis to:
a. See patient journeys for pathways or service design, re-design and de-commissioning
b. Check recorded activity against contracts or invoices and facilitate discussions with providers.
c. Undertake population health management
d. Undertake data quality and validation checks
e. Thoroughly investigate the needs of the population
f. Understand cohorts of residents who are at risk
g. Conduct Health Needs Assessments
3. Allowed linkage is between the data sets contained within point 1.
4. Midlands and Lancashire Commissioning Support Unit will then pass the processed, pseudonymised and linked data to the CCG.
5. Aggregation of required data for CCG management use will be completed by Midlands & Lancashire Commissioning Support Unit or the CCG as instructed by the CCG.
6. Patient level data will not be shared outside of the CCG and will only be shared within the CCG on a need to know basis, as per the purposes stipulated within the Data Sharing Agreement. External aggregated reports only with small number suppression can be shared as set out within NHS Digital guidance applicable to each data set.

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Black Country Joint Data Sharing agreement with NHS Dudley CCG, NHS Sandwell and West Birmingham CCG, NHS Walsall CCG and NHS Wolverhampton CCG. — DARS-NIC-377038-J7G7X

Type of data: information not disclosed for TRE projects

Opt outs honoured: No - data flow is not identifiable, Anonymised - ICO Code Compliant (Does not include the flow of confidential data)

Legal basis: Health and Social Care Act 2012 - s261 - 'Other dissemination of information'

Purposes: No (Commissioning Support Unit (CSU))

Sensitive: Sensitive

When:DSA runs 2020-07-01 — 2023-06-30 2021.01 — 2021.03.

Access method: One-Off, Frequent Adhoc Flow

Data-controller type: NHS BLACK COUNTRY AND WEST BIRMINGHAM CCG, NHS BLACK COUNTRY ICB - D2P2L

Sublicensing allowed: No

Datasets:

1. Acute-Local Provider Flows
2. Ambulance-Local Provider Flows
3. Children and Young People Health
4. Civil Registration - Births
5. Civil Registration - Deaths
6. Community Services Data Set
7. Community-Local Provider Flows
8. Demand for Service-Local Provider Flows
9. Diagnostic Imaging Dataset
10. Diagnostic Services-Local Provider Flows
11. Emergency Care-Local Provider Flows
12. e-Referral Service for Commissioning
13. Experience, Quality and Outcomes-Local Provider Flows
14. Improving Access to Psychological Therapies Data Set
15. Maternity Services Data Set
16. Mental Health and Learning Disabilities Data Set
17. Mental Health Minimum Data Set
18. Mental Health Services Data Set
19. Mental Health-Local Provider Flows
20. National Cancer Waiting Times Monitoring DataSet (CWT)
21. National Diabetes Audit
22. Other Not Elsewhere Classified (NEC)-Local Provider Flows
23. Patient Reported Outcome Measures
24. Personal Demographic Service
25. Population Data-Local Provider Flows
26. Primary Care Services-Local Provider Flows
27. Public Health and Screening Services-Local Provider Flows
28. Summary Hospital-level Mortality Indicator
29. SUS for Commissioners
30. National Cancer Waiting Times Monitoring DataSet (NCWTMDS)
31. Improving Access to Psychological Therapies Data Set_v1.5
32. Civil Registrations of Death
33. Community Services Data Set (CSDS)
34. Diagnostic Imaging Data Set (DID)
35. Improving Access to Psychological Therapies (IAPT) v1.5
36. Mental Health and Learning Disabilities Data Set (MHLDDS)
37. Mental Health Minimum Data Set (MHMDS)
38. Mental Health Services Data Set (MHSDS)
39. Patient Reported Outcome Measures (PROMs)
40. Summary Hospital-level Mortality Indicator (SHMI)

Objectives:

Commissioning
The Black Country CCG's have come together to improve health and care, and are currently moving to one senior management board and plan to formally join at some point in the future.
The four CCG's are as follows:
NHS Wolverhampton CCG, NHS Walsall CCG, NHS Sandwell and West Birmingham CCG, NHS Dudley CCG
The joint collaboration will be responsible for implementing large parts of the 5 year forward view from NHS England. The collaboration will be implementing several initiatives:
- Putting the patient at the heart of the health system
- Working across organisational boundaries to deliver care and including social care, public Health, providers and GPs as well as CCGs
- Reviewing patient pathways to improve patient experience whilst reducing costs e.g. reduce the number of standard tests a patient may have and only have the ones, they need
- Planning the demand and capacity across the healthcare system across the 4 CCGs to ensure the right buildings, services and staff are available to cope with demand whilst reducing the impact on costs - Working to prevent or capture conditions early as they are cheaper to treat
- Introduce initiatives to change behaviours e.g. move more care into the community
- Patient pathway planning for the above

To ensure the patient is at the heart of care, the collaboration is focussing on where services are required across the geographical region. This assists to ensure delivery of care in the right place for patients who may move and change services across CCGs. The CCG's will work proactively and collaboratively to redesign services across boundaries to integrate services. Collaborative sharing is required for CCGs to understand these requirements.
The CCGs will 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 geographical area of Black Country.

The CCGs commission services from a range of providers covering a wide array of services. Each of the data flow categories requested supports the commissioned activity of one or more providers.

The following pseudonymised datasets are required to provide intelligence to support commissioning of health services: - Secondary Uses Service (SUS+)
- Local Provider Flows
o Acute
o Ambulance
o Community
o Demand for Service
o Diagnostic Service
o Emergency Care
o Experience, Quality and Outcomes o Mental Health
o Other Not Elsewhere Classified o Population Data
o Primary Care Services
o Public Health Screening
- Mental Health Minimum Data Set (MHMDS)
- Mental Health Learning Disability Data Set (MHLDDS)
- Mental Health Services Data Set (MHSDS)
- Maternity Services Data Set (MSDS)
- Improving Access to Psychological Therapy (IAPT)
- Child and Young People Health Service (CYPHS)
- Community Services Data Set (CSDS)
- Diagnostic Imaging Data Set (DIDS)
- National Cancer Waiting Times Monitoring Data Set (CWT)
- Civil Registries Data (CRD) (Births)
- Civil Registries Data (CRD) (Deaths)
- National Diabetes Audit (NDA)
- Patient Reported Outcome Measures (PROMs)
- e-Referral Service (eRS)
- Personal Demographics Service (PDS)
- Summary Hospital-level Mortality Indicator (SHMI)

The pseudonymised data is required to for the following purposes:
- Population Health management
- Understanding the interdependency of care services
- Targeting care more effectively
- Using Value as the redesign principle
- Data quality and validation – allowing data quality checks on the submitted data
- Investigation on 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 to another.
- Service Redesign
- Health needs assessment – identification of underlying disease prevalence within the local population
- Patient stratification and predictive modelling – to highlight cohorts of 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.
- Demand Management - to improve the care service for patients by predicting the impact on certain care pathways and support the secondary care system in ensuring enough capacity to manage the demand.
- Support measuring the health, mortality or care needs of the total local population

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 Midlands and Lancashire Commissioning Support Unit.

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.
6͘. Feedback to NHS Service providers on data quality at an aggregate and individual record level on 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.
14. Reviewing current service provision
15. Cost-benefit analysis and service impact assessments to underpin service transformation across health
economy
a. Service planning and re-design (development of NMoC and integrated care pathways, new partnerships, working with new providers etc.)
b. Impact analysis for different models or productivity measures, efficiency and experience
c. Service and pathway review
d. Service utilisation review
16. Ensuring compliance with evidence and guidance
a. Testing approaches with evidence and compliance with guidance.
17. Monitoring outcomes
a. Analysis of variation in outcomes across population group
18. Understanding how services impact across the health economy
a. Service evaluation
b. Programme reviews
c. Analysis of productivity, outcomes, experience, plan, targets and actuals
d. Assessing value for money and efficiency gains
e. Understanding impact of services on health inequalities
19. Understanding how services impact on the health of the population and patient cohorts
a. Measuring and assessing improvement in service provision, patient experience & outcomes and the cost to achieve this
b. Propensity matching and scoring
c. Triple aim analysis
20. Understanding future drivers for change across health economy
a. Forecasting health and care needs for population and population cohorts across STPs
b. Identifying changes in disease trends and prevalence
c. Efficiencies that can be gained from procuring services across wider footprints, from new innovationsd.
Predictive modelling
21. Delivering services that meet changing needs of population
a. Analysis to support policy development
b. Ethical and equality impact assessments
c. Implementation of NMOC
d. What do next years contracts need to include?
e. Workforce planning
22. Maximising services and outcomes within financial envelopes across health economy a. What-if analysis b. Cost-benefit analysis
c. Health economics analysis
d. Scenario planning and modelling
e. Investment and disinvestment in services analysis
23. Allow reporting to drive changes and improve the quality of commissioned services and health outcomes for people.
24. Assists commissioners to make better decisions to support patients and drive changes in health care
25. Allows comparisons of providers performance to assist improvement in services – increase the quality
26. Allow analysis of health care provision to 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.
27. To evaluate the impact of new services and innovations (e.g. if commissioners implement a new service or type of procedure with a provider, they can evaluate whether it improves outcomes for patients compared to the previous one).
28. Monitoring of entire population, as a pose to only those that engage with services
29. Enable Commissioners to be able to see early indications of potential practice resilience issues in that an early warning marker can often be a trend of patients re-registering themselves at a neighbouring practice.
30. Monitor the quality and safety of the delivery of healthcare services.
31. Allow focused commissioning support based on factual data rather than assumed and projected sources

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 data11. Contract Management and Modelling12. Patient Stratification, such as:
a. Patients at highest risk of admission
b. Most expensive patients (top 15%)
c. Frail and elderly
d. Patients that are currently in hospital
e. Patients with most referrals to secondary care
f. Patients with most emergency activity
g. Patients with most expensive prescriptions
h. Patients recently moving from one care setting to another
i. Discharged from hospital
j. Discharged from community
13. Profiling population health and wider determinants to identify and target those most in need
a. Understanding population profile and demographics
b. Identify patient cohorts with specific needs or who may benefit from interventions
c. Identifying disease prevalence. health and care need’s for population cohorts
d. Contributing to Joint Strategic Needs Assessment (JSNA)
e. Geographical mapping and analysis
14. Identifying and managing preventable and existing conditions
a. Identifying types of individuals and population cohorts at risk of non-elective re-admission
b. Risk stratification to identify populations suitable for case management
c. Risk profiling and predictive modelling
d. Risk stratification for planning services for population cohorts
e. Identification of disease incidence and diagnosis stratification
15. Reducing health inequalities
a. Identifying cohorts of patients who have worse health outcomes typically deprived, ethnic groups, homeless, travellers etc. to enable services to proactively target their needs b. Socio-demographic analysis
16. Managing demand
a. Waiting times analysis
b. Service demand and supply modelling
c. Understanding cross-border and overseas visitor
d. Winter planning
e. Emergency preparedness, business continuity, recovery and contingency planning17. Care co-ordination and planning a. Planning packages of care
b. Service planning
c. Planning care co-ordination
18. Monitoring individual patient health, service utilisation, pathway compliance experience & outcomes across the heath and care system
a. Patient pathway analysis across health and care
b. Outcomes & experience analysis
c. Analysis to support services to react to terror situations
d. Analysis to identify vulnerable patients with potential safeguarding issues
e. Understanding equity of care and unwarranted variation
f. Modelling patient flow
g. Tracking patient pathways
h. Monitoring to support New Models of Care (NMOC), Accountable Care Organisations (ACO), Sustainable Transformation
Partnerships (STP)
i. Identifying duplications in care
j. Identifying gaps in care, missed diagnoses and triple fail events
k. Analysing individual and aggregated timelines
19. Undertaking budget planning, management and reporting
a. Tracking financial performance against plans
b. Budget reporting
c. Tariff development
d. Developing and monitoring capitated budgets
e. Developing and monitoring individual-level budgets
f. Future budget planning and forecasting
g. Paying for care of overseas visitors and cross-border flow
20. Monitoring the value for money
a. Service-level costing & comparisons
b. Identification of cost pressures
c. Cost benefit analysis
d. Equity of spend across services and population cohorts
e. Finance impact assessment
21. Comparing population groups, peers, national and international best practice
a. Identification of variation in productivity, cost, outcomes, quality, experience, compared with peers, national and international & best practice
b. Benchmarking against other parts of the country
c. Identifying unwarranted variations
22. Comparing expected levels
a. Standardised comparisons for prevalence, activity, cost, quality, experience, outcomes for given populations
23. Comparing local targets & plan
a. Monitoring of local variation in productivity, cost, outcomes, quality and experience
b. Local performance dashboards by service provider, commissioner, geography, NMOC, STPs24. Monitoring activity and cost compliance against contract and agreed plans a. Contract monitoring b. Contract reconciliation and challenge
c. Invoice validation
25. Monitoring provider quality, demand, experience and outcomes against contract and agreed plans a. Performance dashboards
b. CQUIN reporting
c. Clinical audit
d. Patient experience surveys
e. Demand, supply, outcome & experience analysis
f. Monitoring cross-border flows and overseas visitor activity26. Improving provider data quality a. Coding audit
b. Data quality validation and review
c. Checking validity of patient identity and commissioner assignment
26. Manage demand, by understanding the quantity of assessments required CCGs are able to improve the care service for patients by predicting the impact on certain care pathways and ensure the secondary care system has enough capacity to manage the demand.
27. Monitor the timing of key actions relating to referral letters. CCG’s are unable to see the contents of the referral letters.
28. Identify low priority procedures which could be directed to community-based alternatives and as such commission these services and deflect referrals for low priority procedures resulting in a reduction in hospital referrals.
29. Allow Commissioners to better protect or improve the public health of the total local patient population
30. Allow Commissioners to plan, evaluate and monitor health and social care policies, services, or interventions for the total local patient population
31. Allow Commissioners to compare their providers (trusts) mortality outcomes to the national baseline.
32. Investigate mortality outcomes for trusts

Processing:

PROCESSING CONDITIONS:
Data must only be used for the purposes stipulated within this Data Sharing Agreement. Any additional disclosure / publication will require further approval from NHS Digital.

Data Processors must only act upon specific instructions from the Data Controller.

Data can only be stored at the addresses listed under storage addresses.

All access to data is managed under Role-Based Access Controls. Users can only access data authorised by their role and the tasks that they are required to undertake.

Patient level data will not be linked other than as specifically detailed within this Data Sharing Agreement. Data released will only be shared with those parties listed and will only be used for the purposes laid out in the application/agreement.

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 ͞Personnel͟ (as defined within the Data Sharing Framework Contract ie: employees, agents and contractors of the Data Recipient who may have access to that data)

ONWARD SHARING:
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.

Aggregated reports only with small number suppression can be shared externally as set out within NHS Digital guidance applicable to each data set.

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.

Where the Data Processor and/or the Data Controller hold identifiable data with opt outs applied and identifiable data with opt outs not applied, the data will be held separately so data cannot be linked.

All access to data is auditable by NHS Digital.

DATA MINIMISATION:
Data Minimisation in relation to the data sets listed within the application are listed below. This also includes the purpose on which they would be applied -

For the purpose of Commissioning:
Patients who are normally registered and/or resident within the NHS Wolverhampton CCG, NHS Walsall CCG, NHS Sandwell & West Birmingham CCG and NHS Dudley CCG region (including historical activity where the patient was previously registered or resident in another commissioner).

and/or

ͻPatients treated by a provider where NHS Wolverhampton CCG, NHS Walsall CCG, NHS Sandwell & West Birmingham CCG and NHS Dudley CCG are the host/co-ordinating commissioners and/or have the primary responsibility for the provider services in the local health economy ʹthis is only for commissioning and relates to both national and local flows.

and/or

Activity identified by the provider and recorded as such within national systems (such as SUS+) as for the attention of NHS Wolverhampton CCG, NHS Walsall CCG, NHS Sandwell & West Birmingham CCG and NHS Dudley CCG - this is only for commissioning and relates to both national and local flows.

LIMA Networks Ltd supply IT infrastructure and are therefore listed as a data processor. They supply support to the system, but do not access data. Therefore, any access to the data held under this agreement would be considered a breach of the agreement. This includes granting of access to the database[s] containing the data.

Microsoft Limited supply provide Cloud Services and are therefore listed as a data processor. They supply support to the system, but do not access data. Therefore, any access to the data held under this agreement would be considered a breach of the agreement. This includes granting of access to the database[s] containing the data.

Commissioning
The Data Services for Commissioners Regional Office (DSCRO) obtains the following data sets:
1. SUS+
2. Local Provider Flows (received directly from providers)
a. Acute
b. Ambulance
c. Community
d. Demand for Service
e. Diagnostic Service
f. Emergency Care
g. Experience, Quality and Outcomes
h. Mental Health
i. Other Not Elsewhere Classified
j. Population Data
k. Primary Care Services
l. Public Health Screening
3. Mental Health Minimum Data Set (MHMDS)
4. Mental Health Learning Disability Data Set (MHLDDS)
5. Mental Health Services Data Set (MHSDS)
6. Maternity Services Data Set (MSDS)
7. Improving Access to Psychological Therapy (IAPT)
8. Child and Young People Health Service (CYPHS)
9. Community Services Data Set (CSDS)
10. Diagnostic Imaging Data Set (DIDS)
11. National Cancer Waiting Times Monitoring Data Set (CWT)
12͘. Civil Registries Data Births and Deaths (CRD)
14. National Diabetes Audit (NDA)
15. Patient Reported Outcome Measures (PROMs)
16. e-Referral Service (eRS)
17. Personal Demographics Service (PDS)
18. Summary Hospital-level Mortality Indicator (SHMI)

Data quality management and pseudonymisation is completed within the DSCRO and is then disseminated as follows:
Midlands and Lancashire Commissioning Support Unit

1.Pseudonymised SUS+, Local Provider data, Mental Health data (MHSDS, MHMDS, MHLDDS), Maternity data (MSDS),
Improving Access to Psychological Therapies data (IAPT), Child and Young People’s Health data (CYPHS), Community
Services Data Set (CSDS). Diagnostic Imaging data (DIDS), National Cancer Waiting Times Monitoring Data Set (CWT), Civil Registries data Births and Deaths (CRD), National Diabetes Audit (NDA), Patient Reported Outcomes Measure (PROMs), e-Referral Service (eRS), Personal Demographics Service (PDS) and Summary Hospital-level Mortality Indicator (SHMI) data only is securely transferred from the DSCRO to Midlands and Lancashire Commissioning Support Unit.

1. Midlands and Lancashire Commissioning Support Unit will add derived fields, link data and provide analysis to:
a. See patient journeys for pathways or service design, re-design and de-commissioning
b. Check recorded activity against contracts or invoices and facilitate discussions with providers.
c. Undertake population health management
d. Undertake data quality and validation checks
e. Thoroughly investigate the needs of the population
f. Understand cohorts of residents who are at risk
g. Conduct Health Needs Assessments
2. Allowed linkage is between the data sets contained within point 1.
3. Midlands and Lancashire Commissioning Support Unit will then pass the processed, pseudonymised and linked data to the CCG.
4. Aggregation of required data for CCG management use will be completed by Midlands & Lancashire Commissioning Support Unit or the CCG as instructed by the CCG.
5. Patient level data will not be shared outside of the CCG and will only be shared within the CCG on a need to know basis, as per the purposes stipulated within the Data Sharing Agreement. External aggregated reports only with small number suppression can be shared as set out within NHS Digital guidance applicable to each data set.

---

DSfC - Worcestershire and Herefordshire STP - Comm — DARS-NIC-317048-N8P0R

Type of data: information not disclosed for TRE projects

Opt outs honoured: No - data flow is not identifiable, Anonymised - ICO Code Compliant (Does not include the flow of confidential data)

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

Purposes: No (Commissioning Support Unit (CSU))

Sensitive: Sensitive

When:DSA runs 2019-08-01 — 2022-07-31 2020.02 — 2020.03.

Access method: Frequent Adhoc Flow

Data-controller type: NHS HEREFORDSHIRE AND WORCESTERSHIRE CCG, NHS HEREFORDSHIRE AND WORCESTERSHIRE ICB - 18C

Sublicensing allowed: No

Datasets:

1. Acute-Local Provider Flows
2. Ambulance-Local Provider Flows
3. Children and Young People Health
4. Civil Registration - Births
5. Civil Registration - Deaths
6. Community Services Data Set
7. Community-Local Provider Flows
8. Demand for Service-Local Provider Flows
9. Diagnostic Imaging Dataset
10. Diagnostic Services-Local Provider Flows
11. Emergency Care-Local Provider Flows
12. Experience, Quality and Outcomes-Local Provider Flows
13. Improving Access to Psychological Therapies Data Set
14. Maternity Services Data Set
15. Mental Health and Learning Disabilities Data Set
16. Mental Health Minimum Data Set
17. Mental Health Services Data Set
18. Mental Health-Local Provider Flows
19. National Cancer Waiting Times Monitoring DataSet (CWT)
20. National Diabetes Audit
21. Other Not Elsewhere Classified (NEC)-Local Provider Flows
22. Patient Reported Outcome Measures
23. Population Data-Local Provider Flows
24. Primary Care Services-Local Provider Flows
25. Public Health and Screening Services-Local Provider Flows
26. SUS for Commissioners
27. National Cancer Waiting Times Monitoring DataSet (NCWTMDS)
28. Improving Access to Psychological Therapies Data Set_v1.5
29. Civil Registrations of Death
30. Community Services Data Set (CSDS)
31. Diagnostic Imaging Data Set (DID)
32. Improving Access to Psychological Therapies (IAPT) v1.5
33. Mental Health and Learning Disabilities Data Set (MHLDDS)
34. Mental Health Minimum Data Set (MHMDS)
35. Mental Health Services Data Set (MHSDS)
36. Patient Reported Outcome Measures (PROMs)

Objectives:

Commissioning
The Worcestershire and Hereford CCG's have come together to improve health and care, and are currently moving to one senior management board and plan to formally join at some point in the future.
The four CCG's are as follows:
NHS Herefordshire CCG, NHS Redditch and Bromsgrove CCG, NHS South Worcestershire CCG, NHS Wyre Forest CCG

The joint collaboration will be responsible for implementing large parts of the 5 year forward view from NHS England. The collaboration will be implementing several initiatives:

- Putting the patient at the heart of the health system
- Working across organisational boundaries to deliver care and including social care, public Health, providers and GPs as well as CCGs
- Reviewing patient pathways to improve patient experience whilst reducing costs e.g. reduce the number of standard tests a patient may have and only have the ones they need
- Planning the demand and capacity across the healthcare system across the 4 CCGs to ensure we have the right buildings, services and staff to cope with demand whilst reducing the impact on costs - Working to prevent or capture conditions early as they are cheaper to treat
- Introduce initiatives to change behaviours e.g. move more care into the community
- Patient pathway planning for the above

To ensure the patient is at the heart of care, the collaboration is focussing on where services are required across the geographical region. This assists to ensure delivery of care in the right place for patients who may move and change services across CCGs.

The CCG's will work proactively and collaboratively to redesign services across boundaries to integrate services. Collaborative sharing is required for CCGs to understand these requirements.

The CCGs will 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 geographical area of Worcestershire and Herefordshire.

The CCGs commission services from a range of providers covering a wide array of services. Each of the data flow categories requested supports the commissioned activity of one or more providers.

The following pseudonymised datasets are required to provide intelligence to support commissioning of health services:
- Secondary Uses Service (SUS+)
- Local Provider Flows
o Acute
o Ambulance
o Community
o Demand for Service
o Diagnostic Service
o Emergency Care
o Experience, Quality and Outcomes
o Mental Health
o Other Not Elsewhere Classified
o Population Data
o Primary Care Services
o Public Health Screening
- Mental Health Minimum Data Set (MHMDS)
- Mental Health Learning Disability Data Set (MHLDDS)
- Mental Health Services Data Set (MHSDS)
- Maternity Services Data Set (MSDS)
- Improving Access to Psychological Therapy (IAPT)
- Child and Young People Health Service (CYPHS)
- Community Services Data Set (CSDS)
- Diagnostic Imaging Data Set (DIDS)
- National Cancer Waiting Times Monitoring Data Set (CWT)
- Civil Registries Data (CRD) (Births)
- Civil Registries Data (CRD) (Deaths)
- National Diabetes Audit (NDA)
- Patient Reported Outcome Measures (PROMs)
 
The pseudonymised data is required to for the following purposes:
§ Population health management:
· Understanding the interdependency of care services
· Targeting care more effectively
· Using value as the redesign principle
§ Data Quality and Validation – allowing data quality checks on the submitted data
§ Thoroughly investigating the needs of the population, to ensure the right services are available for individuals when and where they need them
§ Understanding cohorts of residents who are at risk of becoming users of some of the more expensive services, to better understand and manage those needs
§ Monitoring population health and care interactions to understand where people may slip through the net, or where the provision of care may be being duplicated
§ Modelling activity across all data sets to understand how services interact with each other, and to understand how changes in one service may affect flows through another
§ Service redesign
§ Health Needs Assessment – identification of underlying disease prevalence within the local population
§ Patient stratification and predictive modelling - to highlight 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 Midlands and Lancashire Commissioning Support Unit.

Expected Benefits:

i. Benefits Type:
ii. Expected Measurable Benefits to Health and/or Social Care Including Target Date:
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 ʹonly 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.
14. Reviewing current service provision
15. Cost-benefit analysis and service impact assessments to underpin service transformation across health
economy
a. Service planning and re-design (development of NMoC and integrated care pathways, new partnerships, working with new providers etc.)
b. Impact analysis for different models or productivity measures, efficiency and experiencec. Service and pathway review
d. Service utilisation review
16. Ensuring compliance with evidence and guidance
a. Testing approaches with evidence and compliance with guidance.
17. Monitoring outcomes
a. Analysis of variation in outcomes across population group
18. Understanding how services impact across the health economy
a. Service evaluation
b. Programme reviews
c. Analysis of productivity, outcomes, experience, plan, targets and actuals
d. Assessing value for money and efficiency gains
e. Understanding impact of services on health inequalities
19. Understanding how services impact on the health of the population and patient cohorts
a. Measuring and assessing improvement in service provision, patient experience & outcomes and the cost to achieve this
b. Propensity matching and scoring
c. Triple aim analysis
20. Understanding future drivers for change across health economy
a. Forecasting health and care needs for population and population cohorts across STPs
b. Identifying changes in disease trends and prevalence
c. Efficiencies that can be gained from procuring services across wider footprints, from new innovationsd. Predictive modelling
21. Delivering services that meet changing needs of population
a. Analysis to support policy development
b. Ethical and equality impact assessments
c. Implementation of NMOC
d. What do next years contracts need to include?
e. Workforce planning
22. Maximising services and outcomes within financial envelopes across health economy a. What-if analysis
b. Cost-benefit analysis
c. Health economics analysis
d. Scenario planning and modelling
e. Investment and disinvestment in services analysis
f. Opportunity analysis

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 Modelling12. Patient Stratification, such as:
a. Patients at highest risk of admission
b. Most expensive patients (top 15%)
c. Frail and elderly
d. Patients that are currently in hospital
e. Patients with most referrals to secondary care
f. Patients with most emergency activity
g. Patients with most expensive prescriptions
h. Patients recently moving from one care setting to another
i. Discharged from hospitalii. Discharged from community
13. Profiling population health and wider determinants to identify and target those most in need
a. Understanding population profile and demographics
b. Identify patient cohorts with specific needs or who may benefit from interventions
c. Identifying disease prevalence. health and care needs for population cohorts
d. Contributing to Joint Strategic Needs Assessment (JSNA)
e. Geographical mapping and analysis
14. Identifying and managing preventable and existing conditions
a. Identifying types of individuals and population cohorts at risk of non-elective re-admission
b. Risk stratification to identify populations suitable for case management
c. Risk profiling and predictive modelling
d. Risk stratification for planning services for population cohorts
e. Identification of disease incidence and diagnosis stratification
15. Reducing health inequalities
a. Identifying cohorts of patients who have worse health outcomes typically deprived, ethnic groups, homeless, travellers etc. to enable services to proactively target their needs b. Socio-demographic analysis
16. Managing demand
a. Waiting times analysis
b. Service demand and supply modelling
c. Understanding cross-border and overseas visitor
d. Winter planning
e. Emergency preparedness, business continuity, recovery and contingency planning17. Care co-ordination and planning
a. Planning packages of care
b. Service planning
c. Planning care co-ordination
18. Monitoring individual patient health, service utilisation, pathway compliance experience & outcomes across the heath and care system
a. Patient pathway analysis across health and care
b. Outcomes & experience analysis
c. Analysis to support services to react to terror situations
d. Analysis to identify vulnerable patients with potential safeguarding issues
e. Understanding equity of care and unwarranted variation
f. Modelling patient flow
g. Tracking patient pathways
h. Monitoring to support New Models of Care (NMOC), Accountable Care Organisations (ACO), Sustainable Transformation
Partnerships (STP)
i. Identifying duplications in care
j. Identifying gaps in care, missed diagnoses and triple fail events
k. Analysing individual and aggregated timelines
19. Undertaking budget planning, management and reporting
a. Tracking financial performance against plans
b. Budget reporting
c. Tariff development
d. Developing and monitoring capitated budgets
e. Developing and monitoring individual-level budgets
f. Future budget planning and forecasting
g. Paying for care of overseas visitors and cross-border flow
20. Monitoring the value for money
a. Service-level costing & comparisons
b. Identification of cost pressures
c. Cost benefit analysis
d. Equity of spend across services and population cohorts
e. Finance impact assessment
21. Comparing population groups, peers, national and international best practice
a. Identification of variation in productivity, cost, outcomes, quality, experience, compared with peers, national and international & best practice
b. Benchmarking against other parts of the country
c. Identifying unwarranted variations
22. Comparing expected levels
a. Standardised comparisons for prevalence, activity, cost, quality, experience, outcomes for given populations
23. Comparing local targets & plan
a. Monitoring of local variation in productivity, cost, outcomes, quality and experience
b. Local performance dashboards by service provider, commissioner, geography, NMOC, STPs24. Monitoring activity and cost compliance against contract and agreed plans a. Contract monitoring
b. Contract reconciliation and challenge
c. Invoice validation
25. Monitoring provider quality, demand, experience and outcomes against contract and agreed plans a. Performance dashboards
b. CQUIN reporting
c. Clinical audit
d. Patient experience surveys
e. Demand, supply, outcome & experience analysis
f. Monitoring cross-border flows and overseas visitor activity26. Improving provider data quality
a. Coding audit
b. Data quality validation and review
c. Checking validity of patient identity and commissioner assignment

Processing:

PROCESSING CONDITIONS:
Data must only be used for the purposes stipulated within this Data Sharing Agreement. Any additional disclosure / publication will require further approval from NHS Digital.
 
Data Processors must only act upon specific instructions from the Data Controller.
 
Data can only be stored at the addresses listed under storage addresses.
 
All access to data is managed under Role-Based Access Controls. Users can only access data authorised by their role and the tasks that they are required to undertake.
 
Patient level data will not be linked other than as specifically detailed within this Data Sharing Agreement. Data released will only be shared with those parties listed and will only be used for the purposes laid out in the application/agreement.
 
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)

ONWARD SHARING:
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.
 
Aggregated reports only with small number suppression can be shared externally as set out within NHS Digital guidance applicable to each data set.
 
 
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.
 
Where the Data Processor and/or the Data Controller hold identifiable data with opt outs applied and identifiable data with opt outs not applied, the data will be held separately so data cannot be linked.
 
All access to data is auditable by NHS Digital.

DATA MINIMISATION:
Data Minimisation in relation to the data sets listed within the application are listed below. This also includes the purpose on which they would be applied -

For the purpose of Commissioning:
• Patients who are normally registered and/or resident within the NHS Herefordshire CCG, NHS Redditch and Bromsgrove CCG, NHS South Worcestershire CCG and NHS Wyre Forest CCG region (including historical activity where the patient was previously registered or resident in another commissioner).
and/or
• Patients treated by a provider where NHS Herefordshire CCG, NHS Redditch and Bromsgrove CCG, NHS South Worcestershire CCG and NHS Wyre Forest CCG are the host/co-ordinating commissioners and/or have the primary responsibility for the provider services in the local health economy – this is only for commissioning and relates to both national and local flows.
and/or
• Activity identified by the provider and recorded as such within national systems (such as SUS+) as for the attention of NHS Herefordshire CCG, NHS Redditch and Bromsgrove CCG, NHS South Worcestershire CCG and NHS Wyre Forest CCG - this is only for commissioning and relates to both national and local flows.

LIMA Networks Ltd supply IT infrastructure and are therefore listed as a data processor. They supply support to the system, but do not access data. Therefore, any access to the data held under this agreement would be considered a breach of the agreement. This includes granting of access to the database[s] containing the data.

Commissioning
The Data Services for Commissioners Regional Office (DSCRO) obtains the following data sets:
1. SUS+
2. Local Provider Flows (received directly from providers)
a. Acute
b. Ambulance
c. Community
d. Demand for Service
e. Diagnostic Service
f. Emergency Care
g. Experience, Quality and Outcomes
h. Mental Health
i. Other Not Elsewhere Classified
j. Population Data
k. Primary Care Services
l. Public Health Screening
3. Mental Health Minimum Data Set (MHMDS)
4. Mental Health Learning Disability Data Set (MHLDDS)
5. Mental Health Services Data Set (MHSDS)
6. Maternity Services Data Set (MSDS)
7. Improving Access to Psychological Therapy (IAPT)
8. Child and Young People Health Service (CYPHS)
9. Community Services Data Set (CSDS)
10. Diagnostic Imaging Data Set (DIDS)
11. National Cancer Waiting Times Monitoring Data Set (CWT)
12. Civil Registries Data ʹBirths and Deaths (CRD)
Data quality management and pseudonymisation is completed within the DSCRO and is then disseminated as follows:
Midlands and Lancashire Commissioning Support Unit
1. Pseudonymised SUS+, Local Provider data, Mental Health data (MHSDS, MHMDS, MHLDDS), Maternity data (MSDS), Improving Access to Psychological Therapies data (IAPT), Child and Young People͛s Health data (CYPHS), Community Services Data Set (CSDS). Diagnostic Imaging data (DIDS), National Cancer Waiting Times Monitoring Data Set (CWT), Civil Registries Data ʹBirths and Deaths (CRD), National Diabetes Audit (NDA) and Patient Reported Outcome Measures (PROMs) only is securely transferred from the DSCRO to Midlands and Lancashire Commissioning Support Unit.
2. Midlands and Lancashire Commissioning Support Unit will add derived fields, link data and provide analysis to:
a. See patient journeys for pathways or service design, re-design and de-commissioning
b. Check recorded activity against contracts or invoices and facilitate discussions with providers.
c. Undertake population health management
d. Undertake data quality and validation checks
e. Thoroughly investigate the needs of the population
f. Understand cohorts of residents who are at risk
g. Conduct Health Needs Assessments
3. Allowed linkage is between the data sets contained within point 1.
4. Midlands and Lancashire Commissioning Support Unit will then pass the processed, pseudonymised and linked data to the CCG.
5. Aggregation of required data for CCG management use will be completed by Midlands & Lancashire Commissioning Support Unit or the CCG as instructed by the CCG.
6. Patient level data will not be shared outside of the CCG and will only be shared within the CCG on a need to know basis, as per the purposes stipulated within the Data Sharing Agreement. External aggregated reports only with small number suppression can be shared as set out within NHS Digital guidance applicable to each data set.

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