DSfC - NHS Halton CCG; RS, Comm.
NHS Cheshire and Merseyside ICB · Sub ICB Location
Listed under NHS Cheshire and Merseyside Integrated Care Board.
Expired The latest version ended on 12 July 2023. The September 2026 register still lists the agreement, but its term has passed.
- Reference
- DARS-NIC-130743-V5C9T
- Latest version
- v2.2
- Term of latest version
- 13 July 2020 to 12 July 2023
- Start date
- Before 28 July 2018
- Data controller
- Sole Data Controller
- Commercial purposes
- No
- Sublicensing
- No
- Files released to date
- 0
Why the data was released
Objective for processing
Risk Stratification
Risk stratification is a tool for identifying and predicting which patients are at high risk or are likely to be at high risk and prioritising the management of their care in order to prevent worse outcomes.
To conduct risk stratification Secondary User Services (SUS+) data, identifiable at the level of NHS number is linked with Primary Care data (from GPs) and an algorithm is applied to produce risk scores. Risk Stratification provides focus for future demands by enabling commissioners to prepare plans for patients. Commissioners can then prepare plans for patients who may require high levels of care. Risk Stratification also enables General Practitioners (GPs) to better target intervention in Primary Care.
Risk Stratification will be conducted by Arden and Greater East Midlands Commissioning Support Unit and Midlands & Lancashire Commissioning Support Unit.
Commissioning
To use pseudonymised data to provide intelligence to support the commissioning of health services. The data (containing both clinical and financial information) is analysed so that health care provision can be planned to support the needs of the population within the CCG area.
The CCGs commission services from a range of providers covering a wide array of services. Each of the data flow categories requested supports the commissioned activity of one or more providers.
The following pseudonymised datasets are required to provide intelligence to support commissioning of health services:
- Secondary Uses Service (SUS+)
- Local Provider Flows
o Acute
o Ambulance
o Community
o Demand for Service
o Diagnostic Service
o Emergency Care
o Experience, Quality and Outcomes
o Mental Health
o Other Not Elsewhere Classified
o Population Data
o Primary Care Services
o Public Health Screening
- Mental Health Minimum Data Set (MHMDS)
- Mental Health Learning Disability Data Set (MHLDDS)
- Mental Health Services Data Set (MHSDS)
- Maternity Services Data Set (MSDS)
- Improving Access to Psychological Therapy (IAPT)
- Child and Young People Health Service (CYPHS)
- Community Services Data Set (CSDS)
- Diagnostic Imaging Data Set (DIDS)
- National Cancer Waiting Times Monitoring Data Set (CWT)
- Civil Registries Data (CRD) (Births)
- Civil Registries Data (CRD) (Deaths)
- National Diabetes Audit (NDA)
- Patient Reported Outcome Measures (PROMs)
- e-Referral Service (eRS)
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.
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.
Processing activities
Data must only be used as stipulated within this Data Sharing Agreement.
Data Processors must only act upon specific instructions from the Data Controller.
Data can only be stored at the addresses listed under storage addresses.
Patient level data will not be shared outside of the CCG unless it is for the purpose of Direct Care, where it may be shared only with those health professionals who have a legitimate relationship with the patient and a legitimate reason to access the data.
All access to data is managed under Roles-Based Access Controls
No patient level data will be linked other than as specifically detailed within this agreement. Data will only be shared with those parties listed and will only be used for the purposes laid out in the application/agreement. The data to be released from NHS Digital will not be national data, but only that data relating to the specific locality and that data required by the applicant.
NHS Digital reminds all organisations party to this agreement of the need to comply with the Data Sharing Framework Contract requirements, including those regarding the use (and purposes of that use) by “Personnel” (as defined within the Data Sharing Framework Contract ie: employees, agents and contractors of the Data Recipient who may have access to that data)
The DSCRO (part of NHS Digital) will apply National Opt-outs before any identifiable data leaves the DSCRO only for the purpose of Risk Stratification.
CCGs should work with general practices within their CCG to help them fulfil data controller responsibilities regarding flow of identifiable data into risk stratification tools.
The only identifier available in the data set is the NHS numbers. Any further identification of the patients will only be completed by the patient’s clinician on their own systems for the purpose of direct care with a legitimate relationship.
ONWARD SHARING:
Patient level data will not be shared outside of the CCG unless it is for the purpose of Direct Care, where it may be shared only with those health professionals who have a legitimate relationship with the patient and a legitimate reason to access the data.
Aggregated reports only with small number suppression can be shared externally as set out within NHS Digital guidance applicable to each data set.
Segregation
Where the Data Processor and/or the Data Controller hold both identifiable and pseudonymised data, the data will be held separately so data cannot be linked.
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 -
For the purpose of Commissioning:
• Patients who are normally registered and/or resident within the commissioner (including historical activity where the patient was previously registered or resident in another commissioner).
and/or
• Patients treated by a provider where the commissioner is the host/co-ordinating commissioner and/or has the primary responsibility for the provider services in the local health economy – this 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 the commissioner - this is only for commissioning and relates to both national and local flows.
For the purpose of Risk Stratification:
• Patients who are normally registered and/or resident within the commissioner (including historical activity where the patient was previously registered or resident in another commissioner
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.
A CCG user will be able to access the provider extracts from the portal for any provider where at least 1 patient for whom they are the registered CCG for that individuals GP practice appears in that setting
Although a CCG user may have access to pseudonymised patient information not related to that CCG, users should only process and analyse data for which they have a legitimate relationship (as described within Data Minimisation).
St Helen's and Knowsley NHS Foundation Trust (who hosts St Helens and Knowsley Health Informatics [also known as STHK Health Informatics Service]) supply IT infrastructure for NHS St Helens CCG and are therefore listed as data 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.
Lima Networks Ltd supply IT infrastructure and are therefore listed as a data processor. They supply support to the system, but do not access data. Therefore, any access to the data held under this agreement would be considered a breach of the agreement. This includes granting of access to the database[s] containing the data.
Microsoft Limited supply provide Cloud Services 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.
Risk Stratification
Data Processor 1 – Arden & GEM CSU:
1. Identifiable SUS+ data is obtained from the SUS Repository to the Data Services for Commissioners Regional Office (DSCRO).
2. Data quality management and standardisation of data is completed by the DSCRO and the data identifiable at the level of NHS number is transferred securely to Arden & GEM CSU, who hold the SUS+ data within the secure Data Centre on N3.
3. Identifiable GP Data is securely sent from the GP system to Arden & GEM CSU.
4. SUS+ data is linked to GP data in the risk stratification tool by the data processor.
5. As part of the risk stratification processing activity, GPs have access to the risk stratification tool within the data processor, which highlights patients with whom the GP has a legitimate relationship and have been classed as at risk. The only identifier available to GPs is the NHS numbers of their own patients. Any further identification of the patients will be completed by the GP on their own systems.
6. Once Arden & GEM CSU has completed the processing, the CCG can access the online system via a secure connection to access the data pseudonymised at patient level.
7. Arden and GEM CSU also pass identifiable risk stratification outputs to Midlands & Lancashire CSU (who are also a risk stratification supplier).
Data Processor 2 – Midlands & Lancashire CSU:
1. Identifiable risk stratification outputs are transferred securely to Midlands & Lancashire CSU (from Arden and GEM CSU) who apply the data to the BI tool to produce reports.
2. Once Midlands & Lancashire CSU has completed the processing, the CCG can access the online system via a secure N3 connection to access the data pseudonymised at patient level and aggregated reports.
3. As part of the risk stratification processing activity, GPs have access to the Midlands and Lancashire CSU risk stratification tool within the data processor, which highlights patients with whom the GP has a legitimate relationship and have been classed as at risk. The only identifier available to GPs is the NHS numbers of their own patients. Any further identification of the patients will be completed by the GP on their own systems.
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)
Data quality management and pseudonymisation is completed within the DSCRO and is then disseminated as follows:
Data Processor – 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) and National Cancer Waiting Times Monitoring Data Set (CWT), Civil Registries Data (CRD) (Births and Deaths), National Diabetes Audit (NDA), Patient Reported Outcome Measures (PROMs) and e-Referral Service (eRS) data is securely transferred from the DSCRO to Midlands and Lancashire Commissioning Support Unit.
2. Midlands and Lancashire Commissioning Support Unit add derived fields, link data and provide analysis to:
a. See patient journeys for pathways or service design, re-design and de-commissioning.
b. Check recorded activity against contracts or invoices and facilitate discussions with providers.
c. Undertake population health management
d. Undertake data quality and validation checks
e. Thoroughly investigate the needs of the population
f. Understand cohorts of residents who are at risk
g. Conduct Health Needs Assessments
3. Allowed linkage is between the data sets contained within point 1.
4. Midlands and Lancashire Commissioning Support Unit then pass the processed, pseudonymised and linked data to the CCG.
5. Aggregation of required data for CCG management use will be completed by Midlands and Lancashire Commissioning Support Unit or the CCG as instructed by the CCG.
6. Patient level data will not be shared outside of the CCG and will only be shared within the CCG on a need to know basis, as per the purposes stipulated within the Data Sharing Agreement. External aggregated reports only with small number suppression can be shared as set out within NHS Digital guidance applicable to each data set.
Expected output
Risk Stratification
1. As part of the risk stratification processing activity detailed above, GPs have access to the risk stratification tool which highlights patients for whom the GP is responsible and have been classed as at risk. The only identifier available to GPs is the NHS numbers of their own patients. Any further identification of the patients will be completed by the GP on their own systems.
2. GP Practices will be able to view the risk scores for individual patients with the ability to display the underlying SUS+ data for the individual patients when it is required for direct care purposes by someone who has a legitimate relationship with the patient.
CCGs will be able to:
3. Target specific vulnerable patient groups and enable clinicians with the duty of care for the patient to offer appropriate interventions.
4. Reduce hospital readmissions and targeting clinical interventions to high risk patients.
5. Identify patients at risk of deterioration and providing effective care.
6. Reduce in the difference in the quality of care between those with the best and worst outcomes.
7. Re-design care to reduce admissions.
8. Set up capitated budgets – budgets based on care provided to the specific population.
9. Identify health determinants of risk of admission to hospital, or other adverse care outcomes.
10. Monitor vulnerable groups of patients including but not limited to frailty, COPD, Diabetes, elderly.
11. Health needs assessments – identifying numbers of patients with specific health conditions or combination of conditions.
12. Classify vulnerable groups based on: disease profiles; conditions currently being treated; current service use; pharmacy use and risk of future overall cost.
13. Production of Theographs – a visual timeline of a patients encounters with hospital providers.
14. Analyse based on specific diseases
In addition:
- The risk stratification tool will provide aggregate reporting of number and percentage of population found to be at risk.
- Record level output (pseudonymised) will be available for commissioners (of the CCG), pseudonymised at patient level. Onward sharing of this data is not permitted
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:
o Patients at highest risk of admission
o High cost activity uses (top 15%)
o Frail and elderly
o Patients that are currently in hospital
o Patients with most referrals to secondary care
o Patients with most emergency activity
o Patients with most expensive prescriptions
o Patients recently moving from one care setting to another
i. Discharged from hospital
ii. Discharged from community
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.
Expected measurable benefits
Risk Stratification
Risk stratification promotes improved case management in primary care and will lead to the following benefits being realised:
1. Improved planning by better understanding patient flows through the healthcare system, thus allowing commissioners to design appropriate pathways to improve patient flow and allowing commissioners to identify priorities and identify plans to address these.
2. Improved quality of services through reduced emergency readmissions, especially avoidable emergency admissions. This is achieved through mapping of frequent users of emergency services thus allowing early intervention.
3. Improved access to services by identifying which services may be in demand but have poor access, and from this identify areas where improvement is required.
4. Supports the commissioner to meets its requirement to reduce premature mortality in line with the CCG Outcome Framework by allowing for more targeted intervention in primary care.
5. Better understanding of local population characteristics through analysis of their health and healthcare outcomes
All of the above lead to improved patient experience through more effective commissioning of services.
Commissioning
1.1. 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
20. Help drive changes in healthcare
21. Allows comparisons of providers performance to assist improvement in services – increase the quality
22. Inform commissioners and improve services
23. 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.
24. Understanding the interdependency of care services
25. Targeting care more effectively
26. Using value as the redesign principle
27. Thoroughly investigating the needs of the population, to ensure the right services are available for individuals when and where they need them
28. 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
29. 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
30. Service redesign
31. Health Needs Assessment – identification of underlying disease prevalence within the local population
32. 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).
Benefits reported so far
Not stated in the register.
Datasets on the latest version
Legal basis for provision: Health and Social Care Act 2012 – s261(2)(b)(ii); Health and Social Care Act 2012 – s261(7); National Health Service Act 2006 - s251 - 'Control of patient information'.
| Dataset | Type of data | Sensitivity | Frequency | Confidential data |
|---|---|---|---|---|
| Acute-Local Provider Flows | Anonymised - ICO Code Compliant | Non-Sensitive | Frequent Adhoc Flow | Mixture of confidential data flow(s) with support under section 251 NHS Act 2006 and non-confidential data flow(s) |
| Ambulance-Local Provider Flows | Anonymised - ICO Code Compliant | Non-Sensitive | Frequent Adhoc Flow | Mixture of confidential data flow(s) with support under section 251 NHS Act 2006 and non-confidential data flow(s) |
| Children and Young People Health | Anonymised - ICO Code Compliant | Non-Sensitive | Frequent Adhoc Flow | Mixture of confidential data flow(s) with support under section 251 NHS Act 2006 and non-confidential data flow(s) |
| Civil Registration - Births | Anonymised - ICO Code Compliant | Sensitive | Frequent Adhoc Flow | Mixture of confidential data flow(s) with support under section 251 NHS Act 2006 and non-confidential data flow(s) |
| Civil Registrations of Death | Anonymised - ICO Code Compliant | Sensitive | Frequent Adhoc Flow | Mixture of confidential data flow(s) with support under section 251 NHS Act 2006 and non-confidential data flow(s) |
| Community Services Data Set (CSDS) | Anonymised - ICO Code Compliant | Non-Sensitive | Frequent Adhoc Flow | Mixture of confidential data flow(s) with support under section 251 NHS Act 2006 and non-confidential data flow(s) |
| Community-Local Provider Flows | Anonymised - ICO Code Compliant | Non-Sensitive | Frequent Adhoc Flow | Mixture of confidential data flow(s) with support under section 251 NHS Act 2006 and non-confidential data flow(s) |
| Demand for Service-Local Provider Flows | Anonymised - ICO Code Compliant | Non-Sensitive | Frequent Adhoc Flow | Mixture of confidential data flow(s) with support under section 251 NHS Act 2006 and non-confidential data flow(s) |
| Diagnostic Imaging Data Set (DID) | Anonymised - ICO Code Compliant | Non-Sensitive | Frequent Adhoc Flow | Mixture of confidential data flow(s) with support under section 251 NHS Act 2006 and non-confidential data flow(s) |
| Diagnostic Services-Local Provider Flows | Anonymised - ICO Code Compliant | Non-Sensitive | Frequent Adhoc Flow | Mixture of confidential data flow(s) with support under section 251 NHS Act 2006 and non-confidential data flow(s) |
| e-Referral Service for Commissioning | Anonymised - ICO Code Compliant | Sensitive | Frequent Adhoc Flow | Mixture of confidential data flow(s) with support under section 251 NHS Act 2006 and non-confidential data flow(s) |
| Emergency Care-Local Provider Flows | Anonymised - ICO Code Compliant | Non-Sensitive | Frequent Adhoc Flow | Mixture of confidential data flow(s) with support under section 251 NHS Act 2006 and non-confidential data flow(s) |
| Experience, Quality and Outcomes-Local Provider Flows | Anonymised - ICO Code Compliant | Non-Sensitive | Frequent Adhoc Flow | Mixture of confidential data flow(s) with support under section 251 NHS Act 2006 and non-confidential data flow(s) |
| Improving Access to Psychological Therapies (IAPT) v1.5 | Anonymised - ICO Code Compliant | Non-Sensitive | Frequent Adhoc Flow | Mixture of confidential data flow(s) with support under section 251 NHS Act 2006 and non-confidential data flow(s) |
| Maternity Services Data Set | Anonymised - ICO Code Compliant | Non-Sensitive | Frequent Adhoc Flow | Mixture of confidential data flow(s) with support under section 251 NHS Act 2006 and non-confidential data flow(s) |
| Mental Health and Learning Disabilities Data Set (MHLDDS) | Anonymised - ICO Code Compliant | Non-Sensitive | Frequent Adhoc Flow | Mixture of confidential data flow(s) with support under section 251 NHS Act 2006 and non-confidential data flow(s) |
| Mental Health Minimum Data Set (MHMDS) | Anonymised - ICO Code Compliant | Non-Sensitive | Frequent Adhoc Flow | Mixture of confidential data flow(s) with support under section 251 NHS Act 2006 and non-confidential data flow(s) |
| Mental Health Services Data Set (MHSDS) | Anonymised - ICO Code Compliant | Non-Sensitive | Frequent Adhoc Flow | Mixture of confidential data flow(s) with support under section 251 NHS Act 2006 and non-confidential data flow(s) |
| Mental Health-Local Provider Flows | Anonymised - ICO Code Compliant | Non-Sensitive | Frequent Adhoc Flow | Mixture of confidential data flow(s) with support under section 251 NHS Act 2006 and non-confidential data flow(s) |
| National Cancer Waiting Times Monitoring DataSet (NCWTMDS) | Anonymised - ICO Code Compliant | Non-Sensitive | Frequent Adhoc Flow | Mixture of confidential data flow(s) with support under section 251 NHS Act 2006 and non-confidential data flow(s) |
| National Diabetes Audit | Anonymised - ICO Code Compliant | Sensitive | Frequent Adhoc Flow | Mixture of confidential data flow(s) with support under section 251 NHS Act 2006 and non-confidential data flow(s) |
| Other Not Elsewhere Classified (NEC)-Local Provider Flows | Anonymised - ICO Code Compliant | Non-Sensitive | Frequent Adhoc Flow | Mixture of confidential data flow(s) with support under section 251 NHS Act 2006 and non-confidential data flow(s) |
| Patient Reported Outcome Measures (PROMs) | Anonymised - ICO Code Compliant | Sensitive | Frequent Adhoc Flow | Mixture of confidential data flow(s) with support under section 251 NHS Act 2006 and non-confidential data flow(s) |
| Population Data-Local Provider Flows | Anonymised - ICO Code Compliant | Non-Sensitive | Frequent Adhoc Flow | Mixture of confidential data flow(s) with support under section 251 NHS Act 2006 and non-confidential data flow(s) |
| Primary Care Services-Local Provider Flows | Anonymised - ICO Code Compliant | Non-Sensitive | Frequent Adhoc Flow | Mixture of confidential data flow(s) with support under section 251 NHS Act 2006 and non-confidential data flow(s) |
| Public Health and Screening Services-Local Provider Flows | Anonymised - ICO Code Compliant | Non-Sensitive | Frequent Adhoc Flow | Mixture of confidential data flow(s) with support under section 251 NHS Act 2006 and non-confidential data flow(s) |
| SUS for Commissioners | Anonymised - ICO Code Compliant | Non-Sensitive | Frequent Adhoc Flow | Mixture of confidential data flow(s) with support under section 251 NHS Act 2006 and non-confidential data flow(s) |
| SUS for Commissioners | Identifiable | Non-Sensitive | Frequent Adhoc Flow | Mixture of confidential data flow(s) with support under section 251 NHS Act 2006 and non-confidential data flow(s) |
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 2 versions — earlier versions existed before this site's records begin.
DARS-NIC-130743-V5C9T-v2.2 13 July 2020 to 12 July 2023
- Title
- DSfC - NHS Halton CCG; RS, Comm.
- Commercial
- No
- Sublicensing
- No
- Datasets
- 28
- 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); Population Data-Local Provider Flows; Primary Care Services-Local Provider Flows; Public Health and Screening Services-Local Provider Flows; SUS for Commissioners; SUS for Commissioners
What changed from DARS-NIC-130743-V5C9T-v1.5
Text removed is struck through; text added is underlined. Unchanged paragraphs are summarised rather than repeated.
| Field | Was | Became |
|---|---|---|
| Start date | 2020-07-13 | |
| End date | 2023-07-12 | |
| Acute-Local Provider Flows: sensitivity | Non-Sensitive | |
| Acute-Local Provider Flows: common law duty of confidentiality | Mixture of confidential data flow(s) with support under section 251 NHS Act 2006 and non-confidential data flow(s) | |
| Ambulance-Local Provider Flows: sensitivity | Non-Sensitive | |
| Ambulance-Local Provider Flows: common law duty of confidentiality | Mixture of confidential data flow(s) with support under section 251 NHS Act 2006 and non-confidential data flow(s) | |
| Children and Young People Health: sensitivity | Non-Sensitive | |
| Children and Young People Health: common law duty of confidentiality | Mixture of confidential data flow(s) with support under section 251 NHS Act 2006 and non-confidential data flow(s) | |
| Community Services Data Set (CSDS): sensitivity | Non-Sensitive | |
| Community Services Data Set (CSDS): common law duty of confidentiality | Mixture of confidential data flow(s) with support under section 251 NHS Act 2006 and non-confidential data flow(s) | |
| Community-Local Provider Flows: sensitivity | Non-Sensitive | |
| Community-Local Provider Flows: common law duty of confidentiality | Mixture of confidential data flow(s) with support under section 251 NHS Act 2006 and non-confidential data flow(s) | |
| Demand for Service-Local Provider Flows: sensitivity | Non-Sensitive | |
| Demand for Service-Local Provider Flows: common law duty of confidentiality | Mixture of confidential data flow(s) with support under section 251 NHS Act 2006 and non-confidential data flow(s) | |
| Diagnostic Imaging Data Set (DID): sensitivity | Non-Sensitive | |
| Diagnostic Imaging Data Set (DID): common law duty of confidentiality | Mixture of confidential data flow(s) with support under section 251 NHS Act 2006 and non-confidential data flow(s) | |
| Diagnostic Services-Local Provider Flows: sensitivity | Non-Sensitive | |
| Diagnostic Services-Local Provider Flows: common law duty of confidentiality | Mixture of confidential data flow(s) with support under section 251 NHS Act 2006 and non-confidential data flow(s) | |
| Emergency Care-Local Provider Flows: sensitivity | Non-Sensitive | |
| Emergency Care-Local Provider Flows: common law duty of confidentiality | Mixture of confidential data flow(s) with support under section 251 NHS Act 2006 and non-confidential data flow(s) | |
| Experience, Quality and Outcomes-Local Provider Flows: sensitivity | Non-Sensitive | |
| Experience, Quality and Outcomes-Local Provider Flows: common law duty of confidentiality | Mixture of confidential data flow(s) with support under section 251 NHS Act 2006 and non-confidential data flow(s) | |
| Improving Access to Psychological Therapies Data Set_v1.5: sensitivity | Non-Sensitive | |
| Improving Access to Psychological Therapies Data Set_v1.5: common law duty of confidentiality | Mixture of confidential data flow(s) with support under section 251 NHS Act 2006 and non-confidential data flow(s) | |
| Maternity Services Data Set v1.5: sensitivity | Non-Sensitive | |
| Maternity Services Data Set v1.5: common law duty of confidentiality | Mixture of confidential data flow(s) with support under section 251 NHS Act 2006 and non-confidential data flow(s) | |
| Mental Health Minimum Data Set (MHMDS): sensitivity | Non-Sensitive | |
| Mental Health Minimum Data Set (MHMDS): common law duty of confidentiality | Mixture of confidential data flow(s) with support under section 251 NHS Act 2006 and non-confidential data flow(s) | |
| Mental Health Services Data Set (MHSDS): sensitivity | Non-Sensitive | |
| Mental Health Services Data Set (MHSDS): common law duty of confidentiality | Mixture of confidential data flow(s) with support under section 251 NHS Act 2006 and non-confidential data flow(s) | |
| Mental Health and Learning Disabilities Data Set (MHLDDS): sensitivity | Non-Sensitive | |
| Mental Health and Learning Disabilities Data Set (MHLDDS): common law duty of confidentiality | Mixture of confidential data flow(s) with support under section 251 NHS Act 2006 and non-confidential data flow(s) | |
| Mental Health-Local Provider Flows: sensitivity | Non-Sensitive | |
| Mental Health-Local Provider Flows: common law duty of confidentiality | Mixture of confidential data flow(s) with support under section 251 NHS Act 2006 and non-confidential data flow(s) | |
| National Cancer Waiting Times Monitoring DataSet (NCWTMDS): sensitivity | Non-Sensitive | |
| National Cancer Waiting Times Monitoring DataSet (NCWTMDS): common law duty of confidentiality | Mixture of confidential data flow(s) with support under section 251 NHS Act 2006 and non-confidential data flow(s) | |
| Other Not Elsewhere Classified (NEC)-Local Provider Flows: sensitivity | Non-Sensitive | |
| Other Not Elsewhere Classified (NEC)-Local Provider Flows: common law duty of confidentiality | Mixture of confidential data flow(s) with support under section 251 NHS Act 2006 and non-confidential data flow(s) | |
| Population Data-Local Provider Flows: sensitivity | Non-Sensitive | |
| Population Data-Local Provider Flows: common law duty of confidentiality | Mixture of confidential data flow(s) with support under section 251 NHS Act 2006 and non-confidential data flow(s) | |
| Primary Care Services-Local Provider Flows: sensitivity | Non-Sensitive | |
| Primary Care Services-Local Provider Flows: common law duty of confidentiality | Mixture of confidential data flow(s) with support under section 251 NHS Act 2006 and non-confidential data flow(s) | |
| Public Health and Screening Services-Local Provider Flows: sensitivity | Non-Sensitive | |
| Public Health and Screening Services-Local Provider Flows: common law duty of confidentiality | Mixture of confidential data flow(s) with support under section 251 NHS Act 2006 and non-confidential data flow(s) | |
| SUS for Commissioners: sensitivity | Non-Sensitive | |
| SUS for Commissioners: common law duty of confidentiality | Mixture of confidential data flow(s) with support under section 251 NHS Act 2006 and non-confidential data flow(s) |
Datasets: + Civil Registration - Births; + Civil Registrations of Death; + National Diabetes Audit; + Patient Reported Outcome Measures (PROMs); + e-Referral Service for Commissioning
Objective for processing
[31 paragraphs unchanged]
- Civil Registries Data (CRD) (Births)
- Civil Registries Data (CRD) (Deaths)
- National Diabetes Audit (NDA)
- Patient Reported Outcome Measures (PROMs)
- e-Referral Service (eRS)
[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.
[1 paragraph unchanged]
Processing for commissioning will be conducted by
Midlands and Lancashire Commissioning Support Unit.
- Arden and Greater East Midlands Commissioning Support Unit
- Midlands and Lancashire Commissioning Support Unit
- Advancing Quality Alliance (AQuA)
- The Academic Health Sciences Network
Processing activities
[7 paragraphs unchanged]
The DSCRO (part of NHS Digital) will apply
Type 2 objections
National Opt-outs
before any identifiable data leaves the DSCRO only for the purpose of Risk Stratification.
[1 paragraph unchanged]
The only identifier available in the data set is the NHS numbers. Any further identification of the patients will only be completed by the patient’s clinician on their own systems for the purpose of direct care with a legitimate relationship.
ONWARD SHARING:
Patient level data will not be shared outside of the CCG unless it is for the purpose of Direct Care, where it may be shared only with those health professionals who have a legitimate relationship with the patient and a legitimate reason to access the data.
Aggregated reports only with small number suppression can be shared externally as set out within NHS Digital guidance applicable to each data set.
[13 paragraphs unchanged]
The above relates to data requested only (Table 3B). Data currently held (Table 3A) will have the following Data Minimisation:
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.
• CCG of residence and/or registration.
A CCG user will be able to access the provider extracts from the portal for any provider where at least 1 patient for whom they are the registered CCG for that individuals GP practice appears in that setting
For clarity, any access by Blackpool Victoria Hospital to data held under this agreement would be considered a breach of the agreement. This includes granting of access to the database[s] containing the data.
Although a CCG user may have access to pseudonymised patient information not related to that CCG, users should only process and analyse data for which they have a legitimate relationship (as described within Data Minimisation).
St Helen's and Knowsley NHS Foundation Trust (who hosts St Helens and Knowsley Health Informatics [also known as STHK Health Informatics Service]) supply IT infrastructure for NHS St Helens CCG and are therefore listed as data 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.
Lima Networks Ltd supply IT infrastructure and are therefore listed as a data processor. They supply support to the system, but do not access data. Therefore, any access to the data held under this agreement would be considered a breach of the agreement. This includes granting of access to the database[s] containing the data.
Microsoft Limited supply provide Cloud Services 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.
[38 paragraphs unchanged]
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)
[1 paragraph unchanged]
Data Processor
2
– Midlands and Lancashire Commissioning Support Unit
1. Pseudonymised SUS+, Local Provider data, Mental Health data (MHSDS, MHMDS, MHLDDS),
[22 words unchanged]
Diagnostic Imaging data (DIDS) and National Cancer Waiting Times Monitoring Data Set
(CWT) only
(CWT), Civil Registries Data (CRD) (Births and Deaths), National Diabetes Audit (NDA), Patient Reported Outcome Measures (PROMs) and e-Referral Service (eRS) data
is securely transferred from the DSCRO to Midlands and Lancashire Commissioning Support Unit.
[12 paragraphs unchanged]
Data Processor 3 – Advancing Quality Alliance (AQuA) via Arden and Greater East Midlands Commissioning Support
1) Pseudonymised SUS, Local Provider data and Mental Health data (MHSDS, MHMDS, MHLDDS) only is securely transferred from the DSCRO to Arden and Greater East Midlands Commissioning Support.
2) Arden and Greater East Midlands Commissioning Support add derived fields, link data and provide analysis.
3) Allowed linkage is between the data sets contained within point 1.
4) Arden and Greater East Midlands Commissioning Support then pass the processed, pseudonymised and linked data to Advancing Quality Alliance (AQuA). Advancing Quality Alliance (AQuA) provide support for a range of quality improvement programmes including the NW Advancing Quality Programme. Advancing Quality Alliance (AQuA) identifies cohorts of patients within specific disease groups for further analysis to help drive quality improvements across the region.
5) Aggregation of required data for CCG management use will be completed by Advancing Quality Alliance (AQuA).
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 4 – The Academic Health Sciences Network via Arden and Greater East Midlands Commissioning Support
1) Pseudonymised SUS only is securely transferred from the DSCRO to Arden and Greater East Midlands Commissioning Support.
2) Arden and Greater East Midlands Commissioning Support add derived fields, link data and provide analysis.
3) Allowed linkage is between the data sets contained within point 1.
4) Arden and Greater East Midlands Commissioning Support then pass the processed, pseudonymised and linked data to The Academic Health Sciences Network. The Academic Health Sciences Network analyse the data to look at processes rather than patients, for example, A&E performance, process times, bed days as well as ‘deep dives’ to support clinical reviews for CCGs.
5) Aggregation of required data for CCG management use will be completed by The Academic Health Sciences Network.
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
[2 paragraphs unchanged]
2. Output from the risk stratification tool will provide aggregate reporting of number and percentage of population found to be at risk.
2. GP Practices will be able to view the risk scores for individual patients with the ability to display the underlying SUS+ data for the individual patients when it is required for direct care purposes by someone who has a legitimate relationship with the patient.
3. Record level output will be available for commissioners (of the CCG), pseudonymised at patient level.
CCGs will be able to:
4. GP Practices will be able to view the risk scores for individual patients with the ability to display the underlying SUS data for the individual patients when it is required for direct care purposes by someone who has a legitimate relationship with the patient.
3. Target specific vulnerable patient groups and enable clinicians with the duty of care for the patient to offer appropriate interventions.
5. The CCG will be able to target specific patient groups and enable clinicians with the duty of care for the patient to offer appropriate interventions. The CCG will also be able to:
4. Reduce hospital readmissions and targeting clinical interventions to high risk patients.
o Stratify populations based on: disease profiles; conditions currently being treated; current service use; pharmacy use and risk of future overall cost
5. Identify patients at risk of deterioration and providing effective care.
o Plan work for commissioning services and contracts
6. Reduce in the difference in the quality of care between those with the best and worst outcomes.
o Set up capitated budgets
7. Re-design care to reduce admissions.
o Identify health determinants of risk of admission to hospital, or other adverse care outcomes.
8. Set up capitated budgets – budgets based on care provided to the specific population.
9. Identify health determinants of risk of admission to hospital, or other adverse care outcomes.
10. Monitor vulnerable groups of patients including but not limited to frailty, COPD, Diabetes, elderly.
11. Health needs assessments – identifying numbers of patients with specific health conditions or combination of conditions.
12. Classify vulnerable groups based on: disease profiles; conditions currently being treated; current service use; pharmacy use and risk of future overall cost.
13. Production of Theographs – a visual timeline of a patients encounters with hospital providers.
14. Analyse based on specific diseases
In addition:
- The risk stratification tool will provide aggregate reporting of number and percentage of population found to be at risk.
- Record level output (pseudonymised) will be available for commissioners (of the CCG), pseudonymised at patient level. Onward sharing of this data is not permitted
[19 paragraphs unchanged]
8. GP Practice level dashboard
reports include high flyers.
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:
o Patients at highest risk of admission
o High cost activity uses (top 15%)
o Frail and elderly
o Patients that are currently in hospital
o Patients with most referrals to secondary care
o Patients with most emergency activity
o Patients with most expensive prescriptions
o Patients recently moving from one care setting to another
i. Discharged from hospital
ii. Discharged from community
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.
Expected measurable benefits
[3 paragraphs unchanged]
2. Improved quality of services through reduced emergency readmissions, especially avoidable emergency admissions. This is achieved through mapping of frequent users of emergency services
and
thus allowing
early
intervention of appropriate care.
intervention.
[1 paragraph unchanged]
4.
Potentially reduced premature mortality by more targeted intervention in primary care, which supports
Supports
the commissioner to meets its requirement to reduce premature mortality in line with the CCG Outcome
Framework.
Framework by allowing for more targeted intervention in primary care.
5. Better understanding of
the health of and the variations in health outcomes within the population to help understand
local population
characteristics.
characteristics through analysis of their health and healthcare outcomes
[2 paragraphs unchanged]
1.
1.1.
Supporting Quality Innovation Productivity and Prevention (QIPP) to review demand management, integrated care and pathways.
[12 paragraphs unchanged]
b.
Financial and
Non-financial validation of activity.
[8 paragraphs unchanged]
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
20. Help drive changes in healthcare
21. Allows comparisons of providers performance to assist improvement in services – increase the quality
22. Inform commissioners and improve services
23. 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.
24. Understanding the interdependency of care services
25. Targeting care more effectively
26. Using value as the redesign principle
27. Thoroughly investigating the needs of the population, to ensure the right services are available for individuals when and where they need them
28. 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
29. 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
30. Service redesign
31. Health Needs Assessment – identification of underlying disease prevalence within the local population
32. 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).
DARS-NIC-130743-V5C9T-v1.5 28 July 2018 to 27 July 2021
- Title
- DSfC - NHS Halton CCG; RS, Comm.
- Commercial
- No
- Sublicensing
- No
- Datasets
- 23
- Files released
- 0
Datasets: Acute-Local Provider Flows; Ambulance-Local Provider Flows; Children and Young People Health; 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; SUS for Commissioners
Objective for processing
Risk Stratification
Risk stratification is a tool for identifying and predicting which patients are at high risk or are likely to be at high risk and prioritising the management of their care in order to prevent worse outcomes.
To conduct risk stratification Secondary User Services (SUS+) data, identifiable at the level of NHS number is linked with Primary Care data (from GPs) and an algorithm is applied to produce risk scores. Risk Stratification provides focus for future demands by enabling commissioners to prepare plans for patients. Commissioners can then prepare plans for patients who may require high levels of care. Risk Stratification also enables General Practitioners (GPs) to better target intervention in Primary Care.
Risk Stratification will be conducted by Arden and Greater East Midlands Commissioning Support Unit and Midlands & Lancashire Commissioning Support Unit.
Commissioning
To use pseudonymised data to provide intelligence to support the commissioning of health services. The data (containing both clinical and financial information) is analysed so that health care provision can be planned to support the needs of the population within the CCG area.
The CCGs commission services from a range of providers covering a wide array of services. Each of the data flow categories requested supports the commissioned activity of one or more providers.
The following pseudonymised datasets are required to provide intelligence to support commissioning of health services:
- Secondary Uses Service (SUS+)
- Local Provider Flows
o Acute
o Ambulance
o Community
o Demand for Service
o Diagnostic Service
o Emergency Care
o Experience, Quality and Outcomes
o Mental Health
o Other Not Elsewhere Classified
o Population Data
o Primary Care Services
o Public Health Screening
- Mental Health Minimum Data Set (MHMDS)
- Mental Health Learning Disability Data Set (MHLDDS)
- Mental Health Services Data Set (MHSDS)
- Maternity Services Data Set (MSDS)
- Improving Access to Psychological Therapy (IAPT)
- Child and Young People Health Service (CYPHS)
- Community Services Data Set (CSDS)
- Diagnostic Imaging Data Set (DIDS)
- National Cancer Waiting Times Monitoring Data Set (CWT)
The pseudonymised data is required to for the following purposes:
Population health management:
• Understanding the interdependency of care services
• Targeting care more effectively
• Using value as the redesign principle
Data Quality and Validation – allowing data quality checks on the submitted data
Thoroughly investigating the needs of the population, to ensure the right services are available for individuals when and where they need them
Understanding cohorts of residents who are at risk of becoming users of some of the more expensive services, to better understand and manage those needs
Monitoring population health and care interactions to understand where people may slip through the net, or where the provision of care may be being duplicated
Modelling activity across all data sets to understand how services interact with each other, and to understand how changes in one service may affect flows through another
Service redesign
Health Needs Assessment – identification of underlying disease prevalence within the local population
Patient stratification and predictive modelling - to identify specific patients at risk of requiring hospital admission and other avoidable factors such as risk of falls, computed using algorithms executed against linked de-identified data, and identification of future service delivery models
The pseudonymised data is required to ensure that analysis of health care provision can be completed to support the needs of the health profile of the population within the CCG area based on the full analysis of multiple pseudonymised datasets.
Processing for commissioning will be conducted by
- Arden and Greater East Midlands Commissioning Support Unit
- Midlands and Lancashire Commissioning Support Unit
- Advancing Quality Alliance (AQuA)
- The Academic Health Sciences Network
Expected output
Risk Stratification
1. As part of the risk stratification processing activity detailed above, GPs have access to the risk stratification tool which highlights patients for whom the GP is responsible and have been classed as at risk. The only identifier available to GPs is the NHS numbers of their own patients. Any further identification of the patients will be completed by the GP on their own systems.
2. Output from the risk stratification tool will provide aggregate reporting of number and percentage of population found to be at risk.
3. Record level output will be available for commissioners (of the CCG), pseudonymised at patient level.
4. GP Practices will be able to view the risk scores for individual patients with the ability to display the underlying SUS data for the individual patients when it is required for direct care purposes by someone who has a legitimate relationship with the patient.
5. The CCG will be able to target specific patient groups and enable clinicians with the duty of care for the patient to offer appropriate interventions. The CCG will also be able to:
o Stratify populations based on: disease profiles; conditions currently being treated; current service use; pharmacy use and risk of future overall cost
o Plan work for commissioning services and contracts
o Set up capitated budgets
o Identify health determinants of risk of admission to hospital, or other adverse care outcomes.
Commissioning
1. Commissioner reporting:
a. Summary by provider view - plan & actuals year to date (YTD).
b. Summary by Patient Outcome Data (POD) view - plan & actuals YTD.
c. Summary by provider view - activity & finance variance by POD.
d. Planned care by provider view - activity & finance plan & actuals YTD.
e. Planned care by POD view - activity plan & actuals YTD.
f. Provider reporting.
g. Statutory returns.
h. Statutory returns - monthly activity return.
i. Statutory returns - quarterly activity return.
j. Delayed discharges.
k. Quality & performance referral to treatment reporting.
2. Readmissions analysis.
3. Production of aggregate reports for CCG Business Intelligence.
4. Production of project / programme level dashboards.
5. Monitoring of acute / community / mental health quality matrix.
6. Clinical coding reviews / audits.
7. Budget reporting down to individual GP Practice level.
8. GP Practice level dashboard reports include high flyers.
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.
-
July 2021 —
already listed in the earliest edition this site holds, so it may be older. 2 versions: DARS-NIC-130743-V5C9T-v1.5, DARS-NIC-130743-V5C9T-v2.2
-
October 2022
Succeeded Applicant organisation: NHS Halton CCG succeeded by NHS Cheshire and Merseyside ICB from 1 July 2022, according to NHS ODS. Not counted as a change.Succeeded Data controllers: NHS Halton CCG succeeded by NHS Cheshire and Merseyside ICB from 1 July 2022, according to NHS ODS. Not counted as a change.
-
December 2022
Register-wide edit DARS-NIC-130743-V5C9T-v1.5, DARS-NIC-130743-V5C9T-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-130743-V5C9T, “DSfC - NHS Halton CCG; RS, Comm.”. Read via NHS Data Access Explorer (unofficial), https://healthdatauses.uk/agreements/dars-nic-130743-v5c9t/ (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-130743-V5C9T to see the original rows.