DSfC - STP - NHS Staffordshire and Stoke on Trent CCGs - Comm
NHS Staffordshire and Stoke-on-Trent ICB · Sub ICB Location
Listed under NHS Staffordshire and Stoke-on-Trent Integrated Care Board.
Expired The latest version ended on 21 November 2024. The September 2026 register still lists the agreement, but its term has passed.
- Reference
- DARS-NIC-234915-J3K4V
- Latest version
- v3.2
- Term of latest version
- 22 November 2021 to 21 November 2024
- Start date
- 1 February 2019
- Data controller
- Joint Data Controller
- Commercial purposes
- No
- Sublicensing
- No
- Files released to date
- 0
Data controllers
- NHS Staffordshire and Stoke-on-Trent ICB (named in the register 6 times, as different sub-ICB locations)
Why the data was released
Objective for processing
Commissioning
Six Clinical Commissioning Groups (CCG) in Staffordshire have come together to improve health and care for their population and have moved to one senior management board.
The Six CCGs 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
The 6 CCGs work together under a Sustainability and Transformation Partnership. 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 the area 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 CCGs 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)
- National Diabetes Audit (NDA)
- Patient Reported Outcome Measures (PROMs)
- e-Referral Service (eRS)
- Personal Demographics Service (PDS)
- Summary Hospital-level Mortality Indicator (SHMI)
- Medicines Dispensed in Primary Care (NHSBSA Data)
- Adult Social Care Data
Processing of the Medicines Dispensed in Primary Care (NHSBSA Data) dataset is only permitted to provide intelligence about the safety and effectiveness of medicines, as specified by the NHS Business Services Authority (NHSBSA) Medicines Data Directions 2019.
The pseudonymised data is required to for the following purposes:
Population health management:
• Understanding the interdependency of care services
• Targeting care more effectively
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 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.
Provide intelligence about the safety and effectiveness of medicines.
Allow analysis of patient pathways across healthcare and social care.
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, Deloitte LLP and Optum Health Solutions UK Limited.
Deloitte LLP
The CCG's are also undergoing a project to inform Elective Recovery planning – this is contracted through NHS Stafford and Surrounds Clinical Commissioning Group with Deloitte. Analysis will include:
• Developing an independent forecast of the waiting list trajectory to March 2022 by Provider, Point of Delivery (PoD) and Specialty;
• Developing scenario modelling as part of the independent waiting list forecasts to enable the testing of different capacity scenarios and their associated impact on the waiting list; and
• Undertaking capacity release analytics to identify opportunities to increase elective capacity, increase productivity or reduce demand across outpatients, theatres and inpatients.
To support the analysis for the CCG's, both patient level SUS outpatient attendances and admitted patient care admissions datasets will be provided. This data will be used to identify opportunities to increase elective capacity, increase productivity or reduce demand across outpatients, theatres and inpatients.
Processing activities
Data must only be used as 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 Roles-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 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:
There is no requirement for the analytical teams to re-identify patients, but in the development of cohorts of patients considered to be at risk, the data controllers may need the facility to provide identifiable results back to direct healthcare professionals or local authority direct care staff only for the purpose of direct care. All re-id requests will be processed and authorised by the DSCRO on a case by case basis. National data opt outs are not applied in these cases as they are for the purposes of direct care which follows the legal basis of implied consent.
An example of a request for the re-id of patients for direct care may be;
A&E High Attendance usage
The CCG can filter data to show for example the number of A&E attendances in a given period for each patient. The CCG can then flag to the relevant GP of the patient any patients that require intervention. An outcome of this is earlier intervention in the patient(s) care thus potentially reducing future costs and minimising future risk.
Polypharmacy re-IDs
CCG's can request re-ID of a list of patients to be sent to the relevant GP with a high number of medications (ingredient count) and review the medication for these patients. This can help address the risk of polypharmacy which is recognised as an adverse risk factor for patient safety. A by-product of such reviews may be to reduce costs of medication.
The Re-identification process for direct care is as follows:
1. The CCG identifies a patient cohort (typically small numbers) to be re-identified for the purpose of direct care.
2. The CCG sends a re-id request to the DSCRO setting out clearly the direct care purpose and why re-identification is necessary for that purpose. This may be done through the CCG or CSU’s Business Intelligence (BI) Tool, or through a manual form.
3. The DSCRO (either through an automated system or manual checking in line with the request) assesses as to whether the request passes the specified re-identification process checks. Checks include if the requester is authorised to access identifiable data that the reidentified data is necessary for the stipulated direct care purpose, if the number of patients in the cohort is appropriate, and that the request does not seem inappropriate or outside of expected parameters, including for example around timings and the requestor’s relationship with patients in the data
4. If successful/approved, the DSCRO re-identifies the relevant data item(s) for the appropriate patients and returns the identifiable fields to Health or care professional with a legitimate relationship to the patient. The CCG does not see the identifiable record.
5. DSCROs retain an audit trail of all re-id requests
6. National Data opt outs are not applied for the purpose of direct care
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.
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.
LIMA Networks UK 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 provide Cloud Services for Midlands and Lancashire Commissioning Support Unit 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 and Amazon Web Services provide cloud services for Optum Health Solutions (UK) Limited and are therefore listed as processors. 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 (CRD) (Births)
13. Civil Registries Data (CRD) (Deaths)
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)
19. Medicines Dispensed in Primary Care (NHSBSA Data)
20. Adult Social Care Data
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 (CRD) (Births and Deaths), National Diabetes Audit (NDA), Patient Reported Outcome Measures (PROMs), e-Referral Service (eRS), Personal Demographics Service (PDS) and Summary Hospital-level Mortality Indicator (SHMI), Medicines Dispensed in Primary Care (NHSBSA Data) and Adult Social Care data only is securely transferred from the DSCRO to NHS Midlands and Lancashire Commissioning Support Unit.
2. NHS Midlands and Lancashire Commissioning Support Unit also receive GP data (see points).
3. NHS 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
4. Allowed linkage is between the data sets contained within point 1 and 2.
5. Midlands and Lancashire Commissioning Support Unit will then pass the processed, pseudonymised and linked data to the CCG.
6. Aggregation of required data for CCG management use will be completed by NHS Midlands & Lancashire Commissioning Support Unit or the CCG as instructed by the CCG.
7. Patient level data will not be shared outside of the CCG and will only be shared within the CCG on a need to know basis, as per the purposes stipulated within the Data Sharing Agreement. External aggregated reports only with small number suppression can be shared as set out within NHS Digital guidance applicable to each data set.
GP data
i. Identifiable GP data is submitted to Midlands and Lancashire Commissioning Support Unit in a ring-fenced area and pseudonymised using a pseudonymisation tool, different to that used by the DSCRO.
ii. There is a Data Processing Agreement in place between the GP’s and Midlands and Lancashire Commissioning Support Unit. A specific named individual within Midlands and Lancashire Commissioning Support Unit acts on behalf of the providers.
iii. This individual has access to a black box. The pseudonymised data is passed through the black box process where the pseudonymisation is mapped to the pseudonymisation used by the DSCRO.
iv. Once mapped, the data is passed into Midlands and Lancashire Commissioning Support Unit, but before Midlands and Lancashire Commissioning Support Unit will receive the data from the ring-fenced area, they require confirmation that the
identifiable data has been deleted.
v. The data is then passed into the non-ringfenced area with the pseudo algorithm specific to them.
Data Processor - Optum Health Solutions (UK) Ltd
1) Pseudonymised SUS, Mental Health Services Data Set (MHSDS), Community Services Data Set (CSDS), Local Provider data, GP & Adult Social Care data is securely transferred from Midlands and Lancashire Commissioning Support Unit to Optum Health Solutions (UK) Ltd.
2) Optum Health Solutions (UK) Ltd add derived fields, link SUS fields and provide analysis to:
• Whole population segmentation to assess population health needs
• Prospective risk scoring for individuals to indicate the likelihood of future adverse events
• Predictive modelling to determine individuals at risk and an understanding of the drivers of risk
• Longitudinal analysis of intersegmental drift - identifying individuals who move between complexity classifications and the drivers of these transitions
• The production of individual-level theographs to identify gaps in care
• Actuarial modelling to understand unmitigated and mitigated system-level activity and cost historically and in the future
3)Allowed linkage is between the data sets contained within point 1.
4) Optum Health Solutions (UK) Ltd then pass the processed, pseudonymised and linked data to the CCG.
5) Aggregation of required data for CCG management use will be completed by Optum Health Solutions (UK) Ltd 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.
Data Processor - Deloitte LLP
1) Pseudonymised SUS data is securely transferred from Midlands and Lancashire Commissioning Support Unit to Deloitte LLP.
2) Deloitte LLP add derived fields and provide analysis to:
• Develop an independent forecast of the waiting list trajectory to March 2022 by Provider, Point of Delivery (PoD) and Specialty;
• Develop scenario modelling as part of the independent waiting list forecasts to enable the testing of different capacity scenarios and their associated impact on the waiting list; and
• Undertake capacity release analytics to identify opportunities to increase elective capacity, increase productivity or reduce demand across outpatients, theatres and inpatients.
3) Deloitte LLP then pass the processed, pseudonymised and linked data to the CCGs.
4) Aggregation of required data for CCG management use will be completed by Deloitte LLP 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.
Expected output
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.
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. High cost activity uses (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. Validation for payment approval, ability to validate that claims are not being made after an individual has died, like Oxygen services.
14. Validation of programs implemented to improve patient pathway e.g. High users unable to validate if the process to help patients find the best support are working or did the patient die.
15. Clinical - understand reasons why patients are dying, what additional support services can be put in to support.
16. Understanding where patient are dying e.g. are patients dying at hospitals due to hospices closing due to Local authorities withdrawing support, or is there a problem at a particular trust.
17. Removal of patients from Risk Stratification reports.
18. Re births provide a one stop shop of information, Births are recorded in multiple sources covering hospital and home births, a chance to overlook activity.
19. 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.
20. Monitor the timing of key actions relating to referral letters. CCG’s are unable to see the contents of the referral letters.
21. 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.
22. Allow Commissioners to better protect or improve the public health of the total local patient population
23. Allow Commissioners to plan, evaluate and monitor health and social care policies, services, or interventions for the total local patient population
24. Allow Commissioners to compare their providers (trusts) mortality outcomes to the national baseline.
25. Investigate mortality outcomes for trusts.
26. Identify medication prescribing trends and their effectiveness.
27. Linking prescribing habits to entry points into the health and social care system
28. Identify, quantify and understand cohorts of patient’s high numbers of different medications (polypharmacy)
29. Monitoring, at a population level, particular cohorts of service users and designing analytical models which support more effective interventions in health and adult social care
30. Monitoring service and integrated care outcomes across a pathway or care setting involving adult social care.
Expected measurable 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. Providing greater understanding of the underlying courses and look to commission improved supportive networks, this would be ongoing work which would be continually assessed.
15. Insight to understand the numerous factors that play a role in the outcome for both datasets. The linkage will allow the reporting both prior to, during and after the activity, to provide greater assurance on predictive outcomes and delivery of best practice.
16. Provision of indicators of health problems, and patterns of risk within the commissioning region.
17. Support of benchmarking for evaluating progress in future years.
18. Allow reporting to drive changes and improve the quality of commissioned services and health outcomes for people.
19. Assists commissioners to make better decisions to support patients and drive changes in health care
20. Allows comparisons of providers performance to assist improvement in services – increase the quality
21. 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.
22. 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).
23. Monitoring of entire population, as opposed to only those that engage with services
24. 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.
25. Monitor the quality and safety of the delivery of healthcare services.
26. Allow focused commissioning support based on factual data rather than assumed and projected sources
27. Understand admissions linked to overprescribing.
28. Add value to the population health management workstream by adding prescribing data into linked dataset for segmentation and stratification.
29. Developing, through evaluation of person-level data, more effective prevention strategies and interventions across a pathway or care setting involving adult social care
30. Designing and implementing new payment models across health and adult social care
31. Understanding current and future population needs and resource utilisation for local strategic planning and commissioning purposes including for health, social care and public health needs.
Deloitte LLP
Deloitte will be undertaking capacity release analytics to identify opportunities to increase elective capacity, increase productivity or reduce demand across outpatients, theatres and inpatients. As part of this work Deloitte will undertake to analyse patient level outpatient appointments and admitted patient care admissions to enable opportunity identification at a patient delivery level.
The outpatients data will be used to determine outpatient productivity and capacity release, including:
• Improvements in clinical utilisation
• Reduction in appointments by routinely seen patients (frequent attenders)
• Reduction of procedures not routinely commissioned (using our national repository)
The inpatients data will be used to determine inpatient productivity and bed capacity opportunities, including avoidable admissions and effective discharge analytics. Deloitte require patient level data to profile the distribution of elective and non-elective patients by length of stay by specialty, procedure and patient characteristics to identify variation across treatment types.
Benefits reported so far
The CCGs has realised the measurable benefits for the data collection and the provided data has enabled services to be delivered to match the population requirements whilst planning for future needs.
Listed below is a number of further yielded benefits for commissioning;
1. Monitoring In year projects
2. Learning from and predicting likely patient pathways for certain conditions, in order to influence early interventions and other treatments for patients
3. Successful delivery of integrated care within the CCGs.
4. Better understanding of the health of and the variations in health outcomes within the population to help understand local population characteristics.
5. Insights into patient outcomes, and identification of the possible efficacy of outcomes-based contracting opportunities.
The CCGs will look to build on the yielded benefits of commissioning services that meet the needs of their local population, and that are effective in their delivery. The CCGs will use intelligence to add insight to strategic commissioning and service integration across the CCG Areas. This work will continue year on year to match the delivery/funding of targets services for the population within the CCG Areas.
The continued access to this data will enable the CCGs to further understand and improve service performance and delivery, including patient pathway design, re-design and patient experience.
Datasets on the latest version
Legal basis for provision: Health and Social Care Act 2012 - s261 - 'Other dissemination of information'
| Dataset | Type of data | Sensitivity | Frequency | Confidential data |
|---|---|---|---|---|
| Acute-Local Provider Flows | Anonymised - ICO Code Compliant | Sensitive | Frequent Adhoc Flow | Does not include the flow of confidential data |
| Adult Social Care | Anonymised - ICO Code Compliant | Sensitive | Frequent Adhoc Flow | Does not include the flow of confidential data |
| Ambulance-Local Provider Flows | Anonymised - ICO Code Compliant | Sensitive | Frequent Adhoc Flow | Does not include the flow of confidential data |
| Children and Young People Health | Anonymised - ICO Code Compliant | Sensitive | Frequent Adhoc Flow | Does not include the flow of confidential data |
| Civil Registration - Births | Anonymised - ICO Code Compliant | Sensitive | Frequent Adhoc Flow | Does not include the flow of confidential data |
| Civil Registrations of Death | Anonymised - ICO Code Compliant | Sensitive | Frequent Adhoc Flow | Does not include the flow of confidential data |
| Community Services Data Set (CSDS) | Anonymised - ICO Code Compliant | Sensitive | Frequent Adhoc Flow | Does not include the flow of confidential data |
| Community-Local Provider Flows | Anonymised - ICO Code Compliant | Sensitive | Frequent Adhoc Flow | Does not include the flow of confidential data |
| Demand for Service-Local Provider Flows | Anonymised - ICO Code Compliant | Sensitive | Frequent Adhoc Flow | Does not include the flow of confidential data |
| Diagnostic Imaging Data Set (DID) | Anonymised - ICO Code Compliant | Sensitive | Frequent Adhoc Flow | Does not include the flow of confidential data |
| Diagnostic Services-Local Provider Flows | Anonymised - ICO Code Compliant | Sensitive | Frequent Adhoc Flow | Does not include the flow of confidential data |
| e-Referral Service for Commissioning | Anonymised - ICO Code Compliant | Sensitive | Frequent Adhoc Flow | Does not include the flow of confidential data |
| Emergency Care-Local Provider Flows | Anonymised - ICO Code Compliant | Sensitive | Frequent Adhoc Flow | Does not include the flow of confidential data |
| Experience, Quality and Outcomes-Local Provider Flows | Anonymised - ICO Code Compliant | Sensitive | Frequent Adhoc Flow | Does not include the flow of confidential data |
| Improving Access to Psychological Therapies (IAPT) v1.5 | Anonymised - ICO Code Compliant | Sensitive | Frequent Adhoc Flow | Does not include the flow of confidential data |
| Maternity Services Data Set | Anonymised - ICO Code Compliant | Sensitive | Frequent Adhoc Flow | Does not include the flow of confidential data |
| Medicines dispensed in Primary Care (NHSBSA data) | Anonymised - ICO Code Compliant | Sensitive | Frequent Adhoc Flow | Does not include the flow of confidential data |
| Mental Health and Learning Disabilities Data Set (MHLDDS) | Anonymised - ICO Code Compliant | Sensitive | Frequent Adhoc Flow | Does not include the flow of confidential data |
| Mental Health Minimum Data Set (MHMDS) | Anonymised - ICO Code Compliant | Sensitive | Frequent Adhoc Flow | Does not include the flow of confidential data |
| Mental Health Services Data Set (MHSDS) | Anonymised - ICO Code Compliant | Sensitive | Frequent Adhoc Flow | Does not include the flow of confidential data |
| Mental Health-Local Provider Flows | Anonymised - ICO Code Compliant | Sensitive | Frequent Adhoc Flow | Does not include the flow of confidential data |
| National Cancer Waiting Times Monitoring DataSet (NCWTMDS) | Anonymised - ICO Code Compliant | Sensitive | Frequent Adhoc Flow | Does not include the flow of confidential data |
| National Diabetes Audit | Anonymised - ICO Code Compliant | Sensitive | Frequent Adhoc Flow | Does not include the flow of confidential data |
| Other Not Elsewhere Classified (NEC)-Local Provider Flows | Anonymised - ICO Code Compliant | Sensitive | Frequent Adhoc Flow | Does not include the flow of confidential data |
| Patient Reported Outcome Measures (PROMs) | Anonymised - ICO Code Compliant | Sensitive | Frequent Adhoc Flow | Does not include the flow of confidential data |
| Personal Demographic Service | Anonymised - ICO Code Compliant | Sensitive | Frequent Adhoc Flow | Does not include the flow of confidential data |
| Population Data-Local Provider Flows | Anonymised - ICO Code Compliant | Sensitive | Frequent Adhoc Flow | Does not include the flow of confidential data |
| Primary Care Services-Local Provider Flows | Anonymised - ICO Code Compliant | Sensitive | Frequent Adhoc Flow | Does not include the flow of confidential data |
| Public Health and Screening Services-Local Provider Flows | Anonymised - ICO Code Compliant | Sensitive | Frequent Adhoc Flow | Does not include the flow of confidential data |
| Summary Hospital-level Mortality Indicator (SHMI) | Anonymised - ICO Code Compliant | Sensitive | Frequent Adhoc Flow | Does not include the flow of confidential data |
| SUS for Commissioners | Anonymised - ICO Code Compliant | Sensitive | Frequent Adhoc Flow | Does not include the flow of confidential data |
Files released
Files released counts only files released externally by DARS. Access granted in NHS England's own systems, such as its Secure Data Environment, is not included.
No files recorded as released under this agreement.
Version history
The register lists each renewal of this agreement as a separate row. This site has 4 versions.
DARS-NIC-234915-J3K4V-v3.2 22 November 2021 to 21 November 2024
- Title
- DSfC - STP - NHS Staffordshire and Stoke on Trent CCGs - Comm
- Commercial
- No
- Sublicensing
- No
- Datasets
- 31
- Files released
- 0
Datasets: Acute-Local Provider Flows; Adult Social Care; Ambulance-Local Provider Flows; Children and Young People Health; Civil Registration - Births; Civil Registrations of Death; Community Services Data Set (CSDS); Community-Local Provider Flows; Demand for Service-Local Provider Flows; Diagnostic Imaging Data Set (DID); Diagnostic Services-Local Provider Flows; e-Referral Service for Commissioning; Emergency Care-Local Provider Flows; Experience, Quality and Outcomes-Local Provider Flows; Improving Access to Psychological Therapies (IAPT) v1.5; Maternity Services Data Set; Medicines dispensed in Primary Care (NHSBSA data); Mental Health and Learning Disabilities Data Set (MHLDDS); Mental Health Minimum Data Set (MHMDS); Mental Health Services Data Set (MHSDS); Mental Health-Local Provider Flows; National Cancer Waiting Times Monitoring DataSet (NCWTMDS); National Diabetes Audit; Other Not Elsewhere Classified (NEC)-Local Provider Flows; Patient Reported Outcome Measures (PROMs); Personal Demographic Service; Population Data-Local Provider Flows; Primary Care Services-Local Provider Flows; Public Health and Screening Services-Local Provider Flows; Summary Hospital-level Mortality Indicator (SHMI); SUS for Commissioners
What changed from DARS-NIC-234915-J3K4V-v2.2
Text removed is struck through; text added is underlined. Unchanged paragraphs are summarised rather than repeated.
| Field | Was | Became |
|---|---|---|
| Start date | 2021-11-22 | |
| End date | 2024-11-21 | |
| Personal Demographic Service: legal basis | Health and Social Care Act 2012 - s261 - 'Other dissemination of information' | |
| Summary Hospital-level Mortality Indicator (SHMI): legal basis | Health and Social Care Act 2012 - s261 - 'Other dissemination of information' |
Objective for processing
[72 paragraphs unchanged]
Processing for commissioning will be conducted by Midlands and Lancashire Commissioning Support
Unit
Unit, Deloitte LLP
and Optum Health Solutions UK Limited.
Deloitte LLP
The CCG's are also undergoing a project to inform Elective Recovery planning – this is contracted through NHS Stafford and Surrounds Clinical Commissioning Group with Deloitte. Analysis will include:
• Developing an independent forecast of the waiting list trajectory to March 2022 by Provider, Point of Delivery (PoD) and Specialty;
• Developing scenario modelling as part of the independent waiting list forecasts to enable the testing of different capacity scenarios and their associated impact on the waiting list; and
• Undertaking capacity release analytics to identify opportunities to increase elective capacity, increase productivity or reduce demand across outpatients, theatres and inpatients.
To support the analysis for the CCG's, both patient level SUS outpatient attendances and admitted patient care admissions datasets will be provided. This data will be used to identify opportunities to increase elective capacity, increase productivity or reduce demand across outpatients, theatres and inpatients.
Processing activities
[104 paragraphs unchanged] Data Processor - Deloitte LLP 1) Pseudonymised SUS data is securely transferred from Midlands and Lancashire Commissioning Support Unit to Deloitte LLP. 2) Deloitte LLP add derived fields and provide analysis to: • Develop an independent forecast of the waiting list trajectory to March 2022 by Provider, Point of Delivery (PoD) and Specialty; • Develop scenario modelling as part of the independent waiting list forecasts to enable the testing of different capacity scenarios and their associated impact on the waiting list; and • Undertake capacity release analytics to identify opportunities to increase elective capacity, increase productivity or reduce demand across outpatients, theatres and inpatients. 3) Deloitte LLP then pass the processed, pseudonymised and linked data to the CCGs. 4) Aggregation of required data for CCG management use will be completed by Deloitte LLP 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.
Expected measurable benefits
[48 paragraphs unchanged] Deloitte LLP Deloitte will be undertaking capacity release analytics to identify opportunities to increase elective capacity, increase productivity or reduce demand across outpatients, theatres and inpatients. As part of this work Deloitte will undertake to analyse patient level outpatient appointments and admitted patient care admissions to enable opportunity identification at a patient delivery level. The outpatients data will be used to determine outpatient productivity and capacity release, including: • Improvements in clinical utilisation • Reduction in appointments by routinely seen patients (frequent attenders) • Reduction of procedures not routinely commissioned (using our national repository) The inpatients data will be used to determine inpatient productivity and bed capacity opportunities, including avoidable admissions and effective discharge analytics. Deloitte require patient level data to profile the distribution of elective and non-elective patients by length of stay by specialty, procedure and patient characteristics to identify variation across treatment types.
Unchanged: Expected output, Benefits reported.
DARS-NIC-234915-J3K4V-v2.2 17 August 2021 to 16 August 2024
- Title
- DSfC - STP - NHS Staffordshire and Stoke on Trent CCGs - Comm
- Commercial
- No
- Sublicensing
- No
- Datasets
- 31
- Files released
- 0
Datasets: Acute-Local Provider Flows; Adult Social Care; Ambulance-Local Provider Flows; Children and Young People Health; Civil Registration - Births; Civil Registrations of Death; Community Services Data Set (CSDS); Community-Local Provider Flows; Demand for Service-Local Provider Flows; Diagnostic Imaging Data Set (DID); Diagnostic Services-Local Provider Flows; e-Referral Service for Commissioning; Emergency Care-Local Provider Flows; Experience, Quality and Outcomes-Local Provider Flows; Improving Access to Psychological Therapies (IAPT) v1.5; Maternity Services Data Set; Medicines dispensed in Primary Care (NHSBSA data); Mental Health and Learning Disabilities Data Set (MHLDDS); Mental Health Minimum Data Set (MHMDS); Mental Health Services Data Set (MHSDS); Mental Health-Local Provider Flows; National Cancer Waiting Times Monitoring DataSet (NCWTMDS); National Diabetes Audit; Other Not Elsewhere Classified (NEC)-Local Provider Flows; Patient Reported Outcome Measures (PROMs); Personal Demographic Service; Population Data-Local Provider Flows; Primary Care Services-Local Provider Flows; Public Health and Screening Services-Local Provider Flows; Summary Hospital-level Mortality Indicator (SHMI); SUS for Commissioners
What changed from DARS-NIC-234915-J3K4V-v1.3
Text removed is struck through; text added is underlined. Unchanged paragraphs are summarised rather than repeated.
| Field | Was | Became |
|---|---|---|
| Start date | 2021-08-17 | |
| End date | 2024-08-16 | |
| e-Referral Service for Commissioning: legal basis | Health and Social Care Act 2012 - s261 - 'Other dissemination of information' |
Datasets: + Adult Social Care; + Medicines dispensed in Primary Care (NHSBSA data)
Objective for processing
[1 paragraph unchanged]
The
Six Clinical Commissioning Groups (CCG) in
Staffordshire
CCG's
have come together to improve health and
care, currently they
care for their population and
have moved to one senior management
board and plan to formally join as one entity in 2020.
board.
The Six
CCG's
CCGs
are as follows:
[6 paragraphs unchanged]
The 6 CCGs work together under a Sustainability and Transformation Partnership.
Sustainability and Transformation Partnerships build on collaborative work that began under the
[42 words unchanged]
England (PHE) and the National Institute for Health and Care Excellence (NICE).
[4 paragraphs unchanged]
- Planning the demand and capacity across the healthcare system across the 6 CCGs to ensure
we
the area
have the right buildings, services and staff to cope with demand whilst reducing the impact on costs
[4 paragraphs unchanged]
The
CCG's
CCGs
will work proactively and collaboratively to redesign services across boundaries to integrate services. Collaborative sharing is required for CCGs to understand these requirements.
[32 paragraphs unchanged]
- Medicines Dispensed in Primary Care (NHSBSA Data)
- Adult Social Care Data
Processing of the Medicines Dispensed in Primary Care (NHSBSA Data) dataset is only permitted to provide intelligence about the safety and effectiveness of medicines, as specified by the NHS Business Services Authority (NHSBSA) Medicines Data Directions 2019.
[4 paragraphs unchanged]
• Using value as the redesign principle
[9 paragraphs unchanged]
Support measuring the health, mortality or care needs of the total local
populatio
population.
Provide intelligence about the safety and effectiveness of medicines.
Allow analysis of patient pathways across healthcare and social care.
[1 paragraph unchanged]
In addition to the dissemination of Cancer Waiting Times Data via the DSCRO, the CCG is able to access reports held within the CWT system in NHS Digital directly. Access within the CCG is limited to those with a need to process the data for the purposes described in this agreement
Processing for commissioning will be conducted by Midlands and Lancashire Commissioning Support Unit and Optum Health Solutions UK Limited.
Processing for commissioning will be conducted by Midlands and Lancashire Commissioning Support Unit.
Processing activities
Data must only be used as stipulated within this Data Sharing Agreement.
Any additional disclosure / publication will require further approval from NHS Digital.
[2 paragraphs unchanged]
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. Users can only access data authorised by their role and the tasks that they are required to undertake.
All access to data is managed under Roles-Based Access Controls
Patient level data will not be linked other than as specifically detailed within this 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.
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).
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:
There is no requirement for the analytical teams to re-identify patients, but in the development of cohorts of patients considered to be at risk, the data controllers may need the facility to provide identifiable results back to direct healthcare professionals or local authority direct care staff only for the purpose of direct care. All re-id requests will be processed and authorised by the DSCRO on a case by case basis. National data opt outs are not applied in these cases as they are for the purposes of direct care which follows the legal basis of implied consent.
An example of a request for the re-id of patients for direct care may be;
A&E High Attendance usage
The CCG can filter data to show for example the number of A&E attendances in a given period for each patient. The CCG can then flag to the relevant GP of the patient any patients that require intervention. An outcome of this is earlier intervention in the patient(s) care thus potentially reducing future costs and minimising future risk.
Polypharmacy re-IDs
CCG's can request re-ID of a list of patients to be sent to the relevant GP with a high number of medications (ingredient count) and review the medication for these patients. This can help address the risk of polypharmacy which is recognised as an adverse risk factor for patient safety. A by-product of such reviews may be to reduce costs of medication.
The Re-identification process for direct care is as follows:
1. The CCG identifies a patient cohort (typically small numbers) to be re-identified for the purpose of direct care.
2. The CCG sends a re-id request to the DSCRO setting out clearly the direct care purpose and why re-identification is necessary for that purpose. This may be done through the CCG or CSU’s Business Intelligence (BI) Tool, or through a manual form.
3. The DSCRO (either through an automated system or manual checking in line with the request) assesses as to whether the request passes the specified re-identification process checks. Checks include if the requester is authorised to access identifiable data that the reidentified data is necessary for the stipulated direct care purpose, if the number of patients in the cohort is appropriate, and that the request does not seem inappropriate or outside of expected parameters, including for example around timings and the requestor’s relationship with patients in the data
4. If successful/approved, the DSCRO re-identifies the relevant data item(s) for the appropriate patients and returns the identifiable fields to Health or care professional with a legitimate relationship to the patient. The CCG does not see the identifiable record.
5. DSCROs retain an audit trail of all re-id requests
6. National Data opt outs are not applied for the purpose of direct care
Aggregated reports only with small number suppression can be shared externally as set out within NHS Digital guidance applicable to each data set.
[11 paragraphs unchanged]
Microsoft Limited
supply
provide Cloud Services for Midlands and Lancashire Commissioning Support Unit and are
[34 words unchanged]
agreement. This includes granting of access to the database[s] containing the data.
Microsoft Limited and Amazon Web Services provide cloud services for Optum Health Solutions (UK) Limited and are therefore listed as processors. 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.
[32 paragraphs unchanged]
19. Medicines Dispensed in Primary Care (NHSBSA Data)
20. Adult Social Care Data
[2 paragraphs unchanged]
1. Pseudonymised SUS+, Local Provider data, Mental Health data (MHSDS, MHMDS, MHLDDS),
[50 words unchanged]
e-Referral Service (eRS), Personal Demographics Service (PDS) and Summary Hospital-level Mortality Indicator
(SHMI)
(SHMI), Medicines Dispensed in Primary Care (NHSBSA Data) and Adult Social Care
data only is securely transferred from the DSCRO to NHS Midlands and Lancashire Commissioning Support Unit.
2. NHS Midlands and Lancashire Commissioning Support Unit
will add derived fields, link
also receive GP
data
and provide analysis to:
(see points).
3. NHS Midlands and Lancashire Commissioning Support Unit will add derived fields, link data and provide analysis to:
[7 paragraphs unchanged]
3.
4.
Allowed linkage is between the data sets contained within point
1.
1 and 2.
4.
5.
Midlands and Lancashire Commissioning Support Unit will then pass the processed, pseudonymised and linked data to the CCG.
5.
6.
Aggregation of required data for CCG management use will be completed by NHS Midlands & Lancashire Commissioning Support Unit or the CCG as instructed by the CCG.
6.
7.
Patient level data will not be shared outside of the CCG and
[34 words unchanged]
as set out within NHS Digital guidance applicable to each data set.
GP data
i. Identifiable GP data is submitted to Midlands and Lancashire Commissioning Support Unit in a ring-fenced area and pseudonymised using a pseudonymisation tool, different to that used by the DSCRO.
ii. There is a Data Processing Agreement in place between the GP’s and Midlands and Lancashire Commissioning Support Unit. A specific named individual within Midlands and Lancashire Commissioning Support Unit acts on behalf of the providers.
iii. This individual has access to a black box. The pseudonymised data is passed through the black box process where the pseudonymisation is mapped to the pseudonymisation used by the DSCRO.
iv. Once mapped, the data is passed into Midlands and Lancashire Commissioning Support Unit, but before Midlands and Lancashire Commissioning Support Unit will receive the data from the ring-fenced area, they require confirmation that the
identifiable data has been deleted.
v. The data is then passed into the non-ringfenced area with the pseudo algorithm specific to them.
Data Processor - Optum Health Solutions (UK) Ltd
1) Pseudonymised SUS, Mental Health Services Data Set (MHSDS), Community Services Data Set (CSDS), Local Provider data, GP & Adult Social Care data is securely transferred from Midlands and Lancashire Commissioning Support Unit to Optum Health Solutions (UK) Ltd.
2) Optum Health Solutions (UK) Ltd add derived fields, link SUS fields and provide analysis to:
• Whole population segmentation to assess population health needs
• Prospective risk scoring for individuals to indicate the likelihood of future adverse events
• Predictive modelling to determine individuals at risk and an understanding of the drivers of risk
• Longitudinal analysis of intersegmental drift - identifying individuals who move between complexity classifications and the drivers of these transitions
• The production of individual-level theographs to identify gaps in care
• Actuarial modelling to understand unmitigated and mitigated system-level activity and cost historically and in the future
3)Allowed linkage is between the data sets contained within point 1.
4) Optum Health Solutions (UK) Ltd then pass the processed, pseudonymised and linked data to the CCG.
5) Aggregation of required data for CCG management use will be completed by Optum Health Solutions (UK) Ltd 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.
Expected output
[34 paragraphs unchanged]
13. Profiling population health and wider determinants to identify and target those most in need
13. Validation for payment approval, ability to validate that claims are not being made after an individual has died, like Oxygen services.
a. Understanding population profile and demographics
14. Validation of programs implemented to improve patient pathway e.g. High users unable to validate if the process to help patients find the best support are working or did the patient die.
b. Identify patient cohorts with specific needs or who may benefit from interventions
15. Clinical - understand reasons why patients are dying, what additional support services can be put in to support.
c. Identifying disease prevalence. health and care needs for population cohorts
16. Understanding where patient are dying e.g. are patients dying at hospitals due to hospices closing due to Local authorities withdrawing support, or is there a problem at a particular trust.
d. Contributing to Joint Strategic Needs Assessment (JSNA)
17. Removal of patients from Risk Stratification reports.
e. Geographical mapping and analysis
18. Re births provide a one stop shop of information, Births are recorded in multiple sources covering hospital and home births, a chance to overlook activity.
14. Identifying and managing preventable and existing conditions
19. 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.
a. Identifying types of individuals and population cohorts at risk of non-elective re-admission
20. Monitor the timing of key actions relating to referral letters. CCG’s are unable to see the contents of the referral letters.
b. Risk stratification to identify populations suitable for case management
21. 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.
c. Risk profiling and predictive modelling
22. Allow Commissioners to better protect or improve the public health of the total local patient population
d. Risk stratification for planning services for population cohorts
23. Allow Commissioners to plan, evaluate and monitor health and social care policies, services, or interventions for the total local patient population
e. Identification of disease incidence and diagnosis stratification
24. Allow Commissioners to compare their providers (trusts) mortality outcomes to the national baseline.
15. Reducing health inequalities
25. Investigate mortality outcomes for trusts.
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
26. Identify medication prescribing trends and their effectiveness.
b. Socio-demographic analysis
27. Linking prescribing habits to entry points into the health and social care system
16. Managing demand
28. Identify, quantify and understand cohorts of patient’s high numbers of different medications (polypharmacy)
a. Waiting times analysis
29. Monitoring, at a population level, particular cohorts of service users and designing analytical models which support more effective interventions in health and adult social care
b. Service demand and supply modelling
30. Monitoring service and integrated care outcomes across a pathway or care setting involving adult social care.
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
Expected measurable benefits
[30 paragraphs unchanged]
14. Reviewing current service provision
14. Providing greater understanding of the underlying courses and look to commission improved supportive networks, this would be ongoing work which would be continually assessed.
15. Cost-benefit analysis and service impact assessments to underpin service transformation across health economy
15. Insight to understand the numerous factors that play a role in the outcome for both datasets. The linkage will allow the reporting both prior to, during and after the activity, to provide greater assurance on predictive outcomes and delivery of best practice.
a. Service planning and re-design (development of NMoC and integrated care pathways, new partnerships, working with new providers etc.)
16. Provision of indicators of health problems, and patterns of risk within the commissioning region.
b. Impact analysis for different models or productivity measures, efficiency and experience
17. Support of benchmarking for evaluating progress in future years.
c. Service and pathway review
18. Allow reporting to drive changes and improve the quality of commissioned services and health outcomes for people.
d. Service utilisation review
19. Assists commissioners to make better decisions to support patients and drive changes in health care
16. Ensuring compliance with evidence and guidance
20. Allows comparisons of providers performance to assist improvement in services – increase the quality
a. Testing approaches with evidence and compliance with guidance.
21. 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.
17. Monitoring outcomes
22. 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).
a. Analysis of variation in outcomes across population group
23. Monitoring of entire population, as opposed to only those that engage with services
18. Understanding how services impact across the health economy
24. 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.
a. Service evaluation
25. Monitor the quality and safety of the delivery of healthcare services.
b. Programme reviews
26. Allow focused commissioning support based on factual data rather than assumed and projected sources
c. Analysis of productivity, outcomes, experience, plan, targets and actuals
27. Understand admissions linked to overprescribing.
d. Assessing value for money and efficiency gains
28. Add value to the population health management workstream by adding prescribing data into linked dataset for segmentation and stratification.
e. Understanding impact of services on health inequalities
29. Developing, through evaluation of person-level data, more effective prevention strategies and interventions across a pathway or care setting involving adult social care
19. Understanding how services impact on the health of the population and patient cohorts
30. Designing and implementing new payment models across health and adult social care
a. Measuring and assessing improvement in service provision, patient experience & outcomes and the cost to achieve this
31. Understanding current and future population needs and resource utilisation for local strategic planning and commissioning purposes including for health, social care and public health needs.
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
Benefits reported
Not stated in the previous version; added here.
The CCGs has realised the measurable benefits for the data collection and the provided data has enabled services to be delivered to match the population requirements whilst planning for future needs.
Listed below is a number of further yielded benefits for commissioning;
1. Monitoring In year projects
2. Learning from and predicting likely patient pathways for certain conditions, in order to influence early interventions and other treatments for patients
3. Successful delivery of integrated care within the CCGs.
4. Better understanding of the health of and the variations in health outcomes within the population to help understand local population characteristics.
5. Insights into patient outcomes, and identification of the possible efficacy of outcomes-based contracting opportunities.
The CCGs will look to build on the yielded benefits of commissioning services that meet the needs of their local population, and that are effective in their delivery. The CCGs will use intelligence to add insight to strategic commissioning and service integration across the CCG Areas. This work will continue year on year to match the delivery/funding of targets services for the population within the CCG Areas.
The continued access to this data will enable the CCGs to further understand and improve service performance and delivery, including patient pathway design, re-design and patient experience.
Objective for processing
Commissioning
Six Clinical Commissioning Groups (CCG) in Staffordshire have come together to improve health and care for their population and have moved to one senior management board.
The Six CCGs 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
The 6 CCGs work together under a Sustainability and Transformation Partnership. 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 the area 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 CCGs 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)
- National Diabetes Audit (NDA)
- Patient Reported Outcome Measures (PROMs)
- e-Referral Service (eRS)
- Personal Demographics Service (PDS)
- Summary Hospital-level Mortality Indicator (SHMI)
- Medicines Dispensed in Primary Care (NHSBSA Data)
- Adult Social Care Data
Processing of the Medicines Dispensed in Primary Care (NHSBSA Data) dataset is only permitted to provide intelligence about the safety and effectiveness of medicines, as specified by the NHS Business Services Authority (NHSBSA) Medicines Data Directions 2019.
The pseudonymised data is required to for the following purposes:
Population health management:
• Understanding the interdependency of care services
• Targeting care more effectively
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 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.
Provide intelligence about the safety and effectiveness of medicines.
Allow analysis of patient pathways across healthcare and social care.
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 and Optum Health Solutions UK Limited.
Expected output
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.
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. High cost activity uses (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. Validation for payment approval, ability to validate that claims are not being made after an individual has died, like Oxygen services.
14. Validation of programs implemented to improve patient pathway e.g. High users unable to validate if the process to help patients find the best support are working or did the patient die.
15. Clinical - understand reasons why patients are dying, what additional support services can be put in to support.
16. Understanding where patient are dying e.g. are patients dying at hospitals due to hospices closing due to Local authorities withdrawing support, or is there a problem at a particular trust.
17. Removal of patients from Risk Stratification reports.
18. Re births provide a one stop shop of information, Births are recorded in multiple sources covering hospital and home births, a chance to overlook activity.
19. 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.
20. Monitor the timing of key actions relating to referral letters. CCG’s are unable to see the contents of the referral letters.
21. 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.
22. Allow Commissioners to better protect or improve the public health of the total local patient population
23. Allow Commissioners to plan, evaluate and monitor health and social care policies, services, or interventions for the total local patient population
24. Allow Commissioners to compare their providers (trusts) mortality outcomes to the national baseline.
25. Investigate mortality outcomes for trusts.
26. Identify medication prescribing trends and their effectiveness.
27. Linking prescribing habits to entry points into the health and social care system
28. Identify, quantify and understand cohorts of patient’s high numbers of different medications (polypharmacy)
29. Monitoring, at a population level, particular cohorts of service users and designing analytical models which support more effective interventions in health and adult social care
30. Monitoring service and integrated care outcomes across a pathway or care setting involving adult social care.
Benefits reported
The CCGs has realised the measurable benefits for the data collection and the provided data has enabled services to be delivered to match the population requirements whilst planning for future needs.
Listed below is a number of further yielded benefits for commissioning;
1. Monitoring In year projects
2. Learning from and predicting likely patient pathways for certain conditions, in order to influence early interventions and other treatments for patients
3. Successful delivery of integrated care within the CCGs.
4. Better understanding of the health of and the variations in health outcomes within the population to help understand local population characteristics.
5. Insights into patient outcomes, and identification of the possible efficacy of outcomes-based contracting opportunities.
The CCGs will look to build on the yielded benefits of commissioning services that meet the needs of their local population, and that are effective in their delivery. The CCGs will use intelligence to add insight to strategic commissioning and service integration across the CCG Areas. This work will continue year on year to match the delivery/funding of targets services for the population within the CCG Areas.
The continued access to this data will enable the CCGs to further understand and improve service performance and delivery, including patient pathway design, re-design and patient experience.
DARS-NIC-234915-J3K4V-v1.3 1 August 2020 to 31 July 2023
- Title
- DSfC - STP - NHS Staffordshire and Stoke on Trent CCGs - Comm
- Commercial
- No
- Sublicensing
- No
- Datasets
- 29
- Files released
- 0
Datasets: Acute-Local Provider Flows; Ambulance-Local Provider Flows; Children and Young People Health; Civil Registration - Births; Civil Registrations of Death; Community Services Data Set (CSDS); Community-Local Provider Flows; Demand for Service-Local Provider Flows; Diagnostic Imaging Data Set (DID); Diagnostic Services-Local Provider Flows; e-Referral Service for Commissioning; Emergency Care-Local Provider Flows; Experience, Quality and Outcomes-Local Provider Flows; Improving Access to Psychological Therapies (IAPT) v1.5; Maternity Services Data Set; Mental Health and Learning Disabilities Data Set (MHLDDS); Mental Health Minimum Data Set (MHMDS); Mental Health Services Data Set (MHSDS); Mental Health-Local Provider Flows; National Cancer Waiting Times Monitoring DataSet (NCWTMDS); National Diabetes Audit; Other Not Elsewhere Classified (NEC)-Local Provider Flows; Patient Reported Outcome Measures (PROMs); Personal Demographic Service; Population Data-Local Provider Flows; Primary Care Services-Local Provider Flows; Public Health and Screening Services-Local Provider Flows; Summary Hospital-level Mortality Indicator (SHMI); SUS for Commissioners
What changed from DARS-NIC-234915-J3K4V-v0.13
Text removed is struck through; text added is underlined. Unchanged paragraphs are summarised rather than repeated.
| Field | Was | Became |
|---|---|---|
| Applicant organisation | NHS STAFFORDSHIRE AND STOKE-ON-TRENT ICB | |
| Organisation type | Sub ICB Location | |
| Start date | 2020-08-01 | |
| End date | 2023-07-31 | |
| Acute-Local Provider Flows: legal basis | Health and Social Care Act 2012 - s261 - 'Other dissemination of information' | |
| Ambulance-Local Provider Flows: legal basis | Health and Social Care Act 2012 - s261 - 'Other dissemination of information' | |
| Children and Young People Health: legal basis | Health and Social Care Act 2012 - s261 - 'Other dissemination of information' | |
| Civil Registration - Births: legal basis | Health and Social Care Act 2012 - s261 - 'Other dissemination of information' | |
| Civil Registrations of Death: legal basis | Health and Social Care Act 2012 - s261 - 'Other dissemination of information' | |
| Community Services Data Set (CSDS): legal basis | Health and Social Care Act 2012 - s261 - 'Other dissemination of information' | |
| Community-Local Provider Flows: legal basis | Health and Social Care Act 2012 - s261 - 'Other dissemination of information' | |
| Demand for Service-Local Provider Flows: legal basis | Health and Social Care Act 2012 - s261 - 'Other dissemination of information' | |
| Diagnostic Imaging Data Set (DID): legal basis | Health and Social Care Act 2012 - s261 - 'Other dissemination of information' | |
| Diagnostic Services-Local Provider Flows: legal basis | Health and Social Care Act 2012 - s261 - 'Other dissemination of information' | |
| Emergency Care-Local Provider Flows: legal basis | Health and Social Care Act 2012 - s261 - 'Other dissemination of information' | |
| Experience, Quality and Outcomes-Local Provider Flows: legal basis | Health and Social Care Act 2012 - s261 - 'Other dissemination of information' | |
| Improving Access to Psychological Therapies Data Set_v1.5: legal basis | Health and Social Care Act 2012 - s261 - 'Other dissemination of information' | |
| Maternity Services Data Set v1.5: legal basis | Health and Social Care Act 2012 - s261 - 'Other dissemination of information' | |
| Mental Health Minimum Data Set (MHMDS): legal basis | Health and Social Care Act 2012 - s261 - 'Other dissemination of information' | |
| Mental Health Services Data Set (MHSDS): legal basis | Health and Social Care Act 2012 - s261 - 'Other dissemination of information' | |
| Mental Health and Learning Disabilities Data Set (MHLDDS): legal basis | Health and Social Care Act 2012 - s261 - 'Other dissemination of information' | |
| Mental Health-Local Provider Flows: legal basis | Health and Social Care Act 2012 - s261 - 'Other dissemination of information' | |
| National Cancer Waiting Times Monitoring DataSet (NCWTMDS): legal basis | Health and Social Care Act 2012 - s261 - 'Other dissemination of information' | |
| Other Not Elsewhere Classified (NEC)-Local Provider Flows: legal basis | Health and Social Care Act 2012 - s261 - 'Other dissemination of information' | |
| Population Data-Local Provider Flows: legal basis | Health and Social Care Act 2012 - s261 - 'Other dissemination of information' | |
| Primary Care Services-Local Provider Flows: legal basis | Health and Social Care Act 2012 - s261 - 'Other dissemination of information' | |
| Public Health and Screening Services-Local Provider Flows: legal basis | Health and Social Care Act 2012 - s261 - 'Other dissemination of information' | |
| SUS for Commissioners: legal basis | Health and Social Care Act 2012 - s261 - 'Other dissemination of information' |
Datasets: + National Diabetes Audit; + Patient Reported Outcome Measures (PROMs); + Personal Demographic Service; + Summary Hospital-level Mortality Indicator (SHMI); + e-Referral Service for Commissioning
Objective for processing
[47 paragraphs unchanged]
- National Diabetes Audit (NDA)
- Patient Reported Outcome Measures (PROMs)
- e-Referral Service (eRS)
- Personal Demographics Service (PDS)
- Summary Hospital-level Mortality Indicator (SHMI)
[12 paragraphs unchanged]
Patient stratification and predictive modelling - to
identify specific
highlight cohorts of
patients at risk of requiring hospital admission and other avoidable factors such
[7 words unchanged]
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 populatio
[1 paragraph unchanged]
In addition to the dissemination of Cancer Waiting Times Data via the DSCRO, the CCG is able to access reports held within the CWT system in NHS Digital directly. Access within the CCG is limited to those with a need to process the data for the purposes described in this agreement
[1 paragraph unchanged]
Processing activities
[17 paragraphs unchanged]
For clarity,
LIMA Networks UK 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
by LIMA and Blackpool Teaching Hospitals
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 for Midlands and Lancashire Commissioning Support Unit 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.
[25 paragraphs unchanged]
12. Civil Registries Data
– Births and Deaths
(CRD)
(Births)
13. Civil Registries Data (CRD) (Deaths)
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)
[2 paragraphs unchanged]
1. Pseudonymised SUS+, Local Provider data, Mental Health data (MHSDS, MHMDS, MHLDDS),
[9 words unchanged]
(IAPT), Child and Young People’s Health data (CYPHS), Community Services Data Set
(CSDS).
(CSDS),
Diagnostic Imaging data (DIDS), National Cancer Waiting Times Monitoring Data Set
(CWT) and
(CWT),
Civil Registries Data
– Births
(CRD) (Births
and
Deaths (CRD)
Deaths), National Diabetes Audit (NDA), Patient Reported Outcome Measures (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
NHS
Midlands and Lancashire Commissioning Support Unit.
2.
NHS
Midlands and Lancashire Commissioning Support Unit will add derived fields, link data and provide analysis to:
[9 paragraphs unchanged]
5. Aggregation of required data for CCG management use will be completed by
NHS
Midlands & Lancashire Commissioning Support Unit or the CCG as instructed by the CCG.
[1 paragraph unchanged]
Expected output
[19 paragraphs unchanged]
8. GP Practice level dashboard
reports include high flyers.
reports.
[5 paragraphs unchanged]
b.
Most expensive patients
High cost activity uses
(top 15%)
[83 paragraphs unchanged]
Benefits reported
Stated in the previous version and removed here.
Yielded Benefits is not a requirement for new applications.
Unchanged: Expected measurable benefits.
Objective for processing
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)
- 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
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 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 populatio
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.
In addition to the dissemination of Cancer Waiting Times Data via the DSCRO, the CCG is able to access reports held within the CWT system in NHS Digital directly. Access within the CCG is limited to those with a need to process the data for the purposes described in this agreement
Processing for commissioning will be conducted by Midlands and Lancashire Commissioning Support Unit.
Expected output
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.
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. High cost activity uses (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
DARS-NIC-234915-J3K4V-v0.13 1 February 2019 to 31 January 2022
- Title
- DSfC - STP - NHS Staffordshire and Stoke on Trent CCGs - Comm
- Commercial
- No
- Sublicensing
- No
- Datasets
- 24
- Files released
- 0
Datasets: Acute-Local Provider Flows; Ambulance-Local Provider Flows; Children and Young People Health; Civil Registration - Births; Civil Registrations of Death; Community Services Data Set (CSDS); Community-Local Provider Flows; Demand for Service-Local Provider Flows; Diagnostic Imaging Data Set (DID); Diagnostic Services-Local Provider Flows; Emergency Care-Local Provider Flows; Experience, Quality and Outcomes-Local Provider Flows; Improving Access to Psychological Therapies (IAPT) v1.5; Maternity Services Data Set; Mental Health and Learning Disabilities Data Set (MHLDDS); Mental Health Minimum Data Set (MHMDS); Mental Health Services Data Set (MHSDS); Mental Health-Local Provider Flows; National Cancer Waiting Times Monitoring DataSet (NCWTMDS); Other Not Elsewhere Classified (NEC)-Local Provider Flows; Population Data-Local Provider Flows; Primary Care Services-Local Provider Flows; Public Health and Screening Services-Local Provider Flows; SUS for Commissioners
Objective for processing
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 output
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
Benefits reported
Yielded Benefits is not a requirement for new applications.
Register history
When this agreement appeared in, or was edited in, each monthly edition of the register. Built by comparing every edition this site holds, the earliest of which is July 2021.
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July 2021 —
already listed in the earliest edition this site holds, so it may be older. 2 versions: DARS-NIC-234915-J3K4V-v0.13, DARS-NIC-234915-J3K4V-v1.3
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December 2021
1 version added: DARS-NIC-234915-J3K4V-v2.2
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January 2022
1 version added: DARS-NIC-234915-J3K4V-v3.2
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October 2022
Succeeded Data controllers: NHS Cannock Chase CCG succeeded by NHS Staffordshire and Stoke-on-Trent ICB from 1 July 2022, according to NHS ODS. Not counted as a change.Succeeded Data controllers: NHS East Staffordshire CCG succeeded by NHS Staffordshire and Stoke-on-Trent ICB from 1 July 2022, according to NHS ODS. Not counted as a change.Succeeded Data controllers: NHS North Staffordshire CCG succeeded by NHS Staffordshire and Stoke-on-Trent ICB from 1 July 2022, according to NHS ODS. Not counted as a change.Succeeded Data controllers: NHS South East Staffordshire and Seisdon Peninsula CCG succeeded by NHS Staffordshire and Stoke-on-Trent ICB from 1 July 2022, according to NHS ODS. Not counted as a change.Succeeded Data controllers: NHS Stafford and Surrounds CCG succeeded by NHS Staffordshire and Stoke-on-Trent ICB from 1 July 2022, according to NHS ODS. Not counted as a change.Succeeded Data controllers: NHS Stoke on Trent CCG succeeded by NHS Staffordshire and Stoke-on-Trent ICB from 1 July 2022, according to NHS ODS. Not counted as a change.Succeeded Applicant organisation: NHS East Staffordshire CCG succeeded by NHS Staffordshire and Stoke-on-Trent ICB from 1 July 2022, according to NHS ODS. Not counted as a change.
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December 2022
Register-wide edit DARS-NIC-234915-J3K4V-v0.13, DARS-NIC-234915-J3K4V-v1.3, DARS-NIC-234915-J3K4V-v2.2 — Datasets: legal basis: “
s261(1) and” taken out. Made to 639 agreements in this edition, so it is reported once, on the changes page, and not counted as an amendment of this agreement.
Cite this page
NHS England (2026) Data Uses Register, September 2026 edition, agreement DARS-NIC-234915-J3K4V, “DSfC - STP - NHS Staffordshire and Stoke on Trent CCGs - Comm”. Read via NHS Data Access Explorer (unofficial), https://healthdatauses.uk/agreements/dars-nic-234915-j3k4v/ (accessed [date]).
This address stays the same, but the page is rebuilt with each monthly edition, so the citation names the edition it shows. Every edition's data is kept in the facts store.
Source: datausesregister_september2026.xlsx, September 2026 edition of the NHS England Data Uses Register. Search that workbook for DARS-NIC-234915-J3K4V to see the original rows.