NHS Digital Data Release Register - reformatted
NHS Wolverhampton Ccg projects
- DSfC - NHS Wolverhampton CCG and Wolverhampton City Council - Comm
- DSfC - NHS Wolverhampton CCG IV,RS, Comm
- GDPPR COVID-19 CCG - Pseudo
- Project 4
- Project 5
971 data files in total were disseminated unsafely (information about files used safely is missing for TRE/"system access" projects).
🚩 NHS Wolverhampton Ccg was sent multiple files from the same dataset, in the same month, both with optouts respected and with optouts ignored. NHS Wolverhampton Ccg may not have compared the two files, but the identifiers are consistent between datasets, and outside of a good TRE NHS Digital can not know what recipients actually do.
DSfC - NHS Wolverhampton CCG and Wolverhampton City Council - Comm — DARS-NIC-218988-L5K0G
Type of data: information not disclosed for TRE projects
Opt outs honoured: No - data flow is not identifiable, Anonymised - ICO Code Compliant (Does not include the flow of confidential data)
Legal basis: Health and Social Care Act 2012 – s261(1) and s261(2)(b)(ii), Health and Social Care Act 2012 - s261 - 'Other dissemination of information', Health and Social Care Act 2012 s261(1) and s261(2)(b)(ii), Health and Social Care Act 2012 s261(2)(b)(ii)
Purposes: No (Clinical Commissioning Group (CCG), Sub ICB Location)
Sensitive: Sensitive
When:DSA runs 2019-10-14 — 2022-10-13 2019.01 — 2021.04.
Access method: Frequent Adhoc Flow, One-Off
Data-controller type: CITY OF WOLVERHAMPTON COUNCIL, NHS BLACK COUNTRY AND WEST BIRMINGHAM CCG, CITY OF WOLVERHAMPTON COUNCIL, DUDLEY METROPOLITAN BOROUGH COUNCIL, NHS BLACK COUNTRY AND WEST BIRMINGHAM CCG, CITY OF WOLVERHAMPTON COUNCIL, NHS BLACK COUNTRY ICB - D2P2L, CITY OF WOLVERHAMPTON COUNCIL, DUDLEY METROPOLITAN BOROUGH COUNCIL, NHS BLACK COUNTRY ICB - D2P2L
Sublicensing allowed: No
Datasets:
- SUS for Commissioners
Objectives:
Commissioning
To use pseudonymised data to provide intelligence to support commissioning of health services. The pseudonymised data is required to ensure that analysis of health care provision can be completed to support the needs of the health profile of the population within the CCG area based on the full analysis of multiple pseudonymised datasets.
The CCGs commission services from a range of providers covering a wide array of services. Each of the data flow categories requested supports the commissioned activity of one or more providers.
Pseudonymised data will also be used to provide Health and Social Care tools that will support Clinical Commissioning Group and Local Authority in improving integrated working and the delivery of integrated health and social care in order to improve outcomes in ways such as those set out in the Better Care Fund (BCF).
Analyses of health and social care activity through population profiling will provide benefits that support care initiatives. It will support identification of areas of improvement, for example reablement, emergency admissions, reduction in length of stay and transfer of care delays. Analysis will assist to: improve integrated health and Social Care; improve outcomes (BCF related); profile the population to support care initiatives; and transfer care delays and reduce length of stay.
The analyses will benefit the local health economies by allowing them to baseline their current health and social care provision. They will provide an understanding of the interfaces between health and social care services and the areas that are most amenable to joint commissioning. Linked data can be used to predict the impact of any planned changes and monitor this once implemented. Understanding the baseline of health and care activities will enable the key partners to provide assurance that they have identified the correct areas and services of focus for integrated working and to evidence improvement as initiatives are implemented.
Health and Social Care Population Profiling
NHS Digital and the Local Government Association are working together to raise the importance of adult social care and support the delivery of person-centred care through digital technology both across councils and with social care providers.
To this end the Social Care Digital Innovation Programme is being run by NHS Digital in partnership with the Local Government Association and has been developed to provide funding for local authorities to support innovative uses of digital technology in the design and delivery of adult social care.
The work of the Social Care Programme focuses on improving digital maturity and supports the better understanding and use of digital technology across the social care sector.
It is intended to support the health and care sectors to share information securely between different systems and to simplify and standardise the information they collect and use.
There are many links between the health and care system, such as when someone is discharged from hospital into social care, but it's often difficult for health and care professionals to share information about patients and people accessing services.
A range of projects have recently been approved which aim to make transfers of care smoother and safer, improve people’s experience of care, support better care decisions and save care professionals’ time.
Purpose and approach
The grant funding award to Wolverhampton under the Social Care Digital Innovation Programme will be used to demonstrate how predictive analytics and digital information sharing can improve care and support for people needing social care services.
The Wolverhampton project approach is based on a collaborative working between the city council’s Adult Social Care team, NHS Wolverhampton CCG and Predict X*, who have extensive experience in machine learning and predictive analysis.
The Machine learning approach uses the study of algorithms and mathematical models that computer systems use to progressively improve their performance on a specific task. Machine learning algorithms build a mathematical model of sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to perform the task
Predictive analytics is a form of advanced analytics that uses both new and historical data to forecast activity, behaviour and trends. It involves applying statistical analysis techniques, analytical queries and automated machine learning algorithms to data sets to create predictive models on the likelihood of a particular event happening in the future. Predictive analytics does not tell you what will happen in the future. It forecasts what might happen in the future with an acceptable level of reliability and includes what-if scenarios and risk assessment.
*PredictX is the trading name for PI Limited. PI Limited are a legal entity and are registered with Companies House - company number 01728605.
PredictX will be referred to by the legal name PI Limited throughout the Data Sharing Agreement.
Yielded Benefits:
1. A better understanding of pressure points in the existing care system. - Dashboards combining health and social care data show key metrics such as A&E attendances, hospital admissions, hospital discharges, Delayed Transfers of Care (DTOCs) and capacity in care homes. This intelligence has given Wolverhampton City Council a better understanding of how the system can be improved. 2. A machine learning model predicting how many A&E patients end up being admitted to hospital. - City of Wolverhampton Council can use this data to provide best-fitting social care packages for each patient. 3. A new approach to population health involving the creation of care service user profiles to better determine service need in a geographical area. - The work has involved analysing the data of 3,000 users of domiciliary care and showing insight into: - The services they use. - Touchpoints they have with organisations in the system - Socio-Economic data such as indices of deprivation. This work has generated seven key profiles and unearthed an insight - amongst several others - that there are residents with long-term health conditions who do not access many services, whilst there are other residents with no conditions who access multiple services. The richness of data makes it possible to drill down into this further, investigate and re-organise services to address this. The team are now looking to use the insight from the profiles and apply them to real-life situations to see whether they can inform the way that services can be delivered. The commissioning team plan to use the data to better manage the health and care needs of the CCG’s communities to help people stay independent for longer and take pressure off more stretched services.
Expected Benefits:
At the core of the Wolverhampton project is the use of pseudonymised health and social care data to develop predictive models which enable the early identification of adults with complex morbidities. This will help to inform service design and the improvement of intervention and prevention programmes.
This programme is designed to support the comments made by James Palmer, Head of the Social Care Programme at NHS Digital who said: “The successful projects span a wide range of areas and give a glimpse into the future of social care.’’
“There is great potential for these projects to be replicated easily to deliver benefits quickly for the system and pave the way for a truly integrated future.’’
‘’The work on predictive analytics is significant given its potential to support people at earlier stages which may help to reduce the need for long-term social care. Through the use of predictive models that forecast service need and target interventions, we have the chance to help people remain independent, in their own homes, for longer.”
Additional benefits include
Health and Social Care Population Profiling
- Supporting identification of areas of improvement including but not limited to:
• Reablement
• Emergency admissions
• Reduction in length of stay
• Transfer of care delays
• Supporting the objectives of Wolverhampton LA and Wolverhampton CCG collaboration plan.
• Analysis to support full business cases
• Develop Business models
• Learning from and predicting likely patient pathways for certain conditions, in order to influence early interventions and other support for patients.
• Analysis of outcome measures for different treatments, accounting for the patient pathway
• Monitoring of outcome indicators
• Monitoring financial and non-financial validation of activity
• Monitoring of successful delivery of integrated care within the health and care community within Wolverhampton.
• Monitoring frequent or multiple attendances to improve early interventions and avoid admissions.
• Support Care Service planning
• Support improved planning to better understand patient flows through the healthcare system, thus allow supporting organisations to design appropriate pathways to improve patient flow and provide commissioners to identify priorities and identify plans to address identified issues.
• Improved quality of services , by providing supportive information to introduce early intervention of appropriate care.
• 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.
• Better understanding of the health of and the variations in health outcomes within the population to help understand local population characteristics.
• Enables the identification of pressure points in the care and health system
• Provides a geographical understanding of service usage
• Understanding the baseline of health and care activities will enable the key partners to provide assurance that they have identified the correct areas and services of focus for integrated working and to evidence improvement as initiatives are implemented.
• 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
Outputs:
Health and Social Care Population Profiling
- Supporting identification of areas of improvement including but not limited to:
• Reablement
• Emergency admissions
• Reduction in length of stay
• Transfer of care delays
• Baseline of current health and social care provision for local health economies
• Understanding Interfaces between health and social care services
• Understanding the baseline of health and care activities will enable the key partners to provide assurance that they have identified the correct areas and services of focus for integrated working and to evidence improvement as initiatives are implemented.
• See patient journeys for pathway or service design, re-design and de-commissioning
• Undertake data quality and validation checks.
• Investigate the needs of the population
• Understand health needs of residents who are at risk
• Conduct Health needs Assessments
• The production of joint strategic needs assessments and joint health and well- being strategies.
• Planning and delivering effective health services, public health services and social care services.
Processing:
Data must only be used as stipulated within this Data Sharing Agreement.
Data Processors must only act upon specific instructions from the Data Controller.
Data can only be stored at the addresses listed under storage addresses.
Patient level data will not be shared outside of the CCG unless it is for the purpose of Direct Care, where it may be shared only with those health professionals who have a legitimate relationship with the patient and a legitimate reason to access the data.
All access to data is managed under Roles-Based Access Controls
No patient level data will be linked other than as specifically detailed within this agreement. Data will only be shared with those parties listed and will only be used for the purposes laid out in the application/agreement. The data to be released from NHS Digital will not be national data, but only that data relating to the specific locality and that data required by the applicant.
NHS Digital reminds all organisations party to this agreement of the need to comply with the Data Sharing Framework Contract requirements, including those regarding the use (and purposes of that use) by “Personnel” (as defined within the Data Sharing Framework Contract ie: employees, agents and contractors of the Data Recipient who may have access to that data)
Segregation
Where the Data Processor and/or the Data Controller hold both identifiable and pseudonymised data, the data will be held separately so data cannot be linked.
All access to data is auditable by NHS Digital.
Data Minimisation
Data Minimisation in relation to the data sets listed within section 3 are listed below. This also includes the purpose on which they would be applied -
• Patients who are normally registered and/or resident within 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 clarity, any access by Lima Networks Ltd and Equinix to data held under this agreement would be considered a breach of the agreement. This includes granting of access to the database[s] containing the data.
Commissioning
The Data Services for Commissioners Regional Office (DSCRO) obtains the following data sets:
1) SUS
Data quality management of data is completed by the DSCRO. The SUS data is then pseudonymised using University of Nottingham open pseudonymiser tool - a standalone windows desktop application which creates a digest of one or more columns of a CSV file, using a shared key (SALT file) controlled by the Data Services for Commissioners Regional Office. The DSCRO then disseminated as follows:
1) Pseudonymised SUS, only is securely transferred from the DSCRO to PI Limited via Midlands and Lancashire Commissioning Support Unit which is used as a landing point only due to DSCRO regional processing restrictions.
2) Data quality management of social care data is completed by the Local Authority. The social care data is then pseudonymised using University of Nottingham open pseudonymiser tool. The pseudonymised Social Care Data is then sent to PI Limited direct from the Local Authority via secure FTP
3) The pseudonymisation key cannot be used to re-identify data as the tool does not allow for this to happen, it only allows for one way pseudonymisation.
4) PI Limited then link the data using the common pseudo link, which is undertaken within a controlled environment by a named member of staff, who then produce online reports using CareTrak data analysis tool to provide the CCG and Local authority with a range of high level commissioning intelligence based on integrated pathways of care.
5) Predictive analytics will be applied which is a form of advanced analytics that uses both new and historical data to forecast activity, behavior and trends. It involves applying statistical analysis techniques, analytical queries and automated machine learning algorithms to data sets to create predictive models on the likelihood of a particular event happening in the future. Predictive analytics does not tell you what will happen in the future. It forecasts what might happen in the future with an acceptable level of reliability and includes what-if scenarios and risk assessment.
6) PI Limited send pseudonymised outputs to the Local Authority.
7) PI Limited then aggregate the data and send aggregated reports with small number suppression to the CCG.
8) Patient level data will not be shared outside of PI Limitedor the Local Authority and will only be shared within PI Limited and the Local Authority 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.
DSfC - NHS Wolverhampton CCG IV,RS, Comm — DARS-NIC-41158-X3V7D
Type of data: information not disclosed for TRE projects
Opt outs honoured: No - data flow is not identifiable, Yes - patient objections upheld, Anonymised - ICO Code Compliant, Identifiable (Section 251, Section 251 NHS Act 2006, Mixture of confidential data flow(s) with support under section 251 NHS Act 2006 and non-confidential data flow(s))
Legal basis: Health and Social Care Act 2012 – s261(1) and s261(2)(b)(ii), Health and Social Care Act 2012 – s261(7), National Health Service Act 2006 - s251 - 'Control of patient information'. , Health and Social Care Act 2012 s261(1) and s261(2)(b)(ii), Health and Social Care Act 2012 s261(7), Health and Social Care Act 2012 s261(7); National Health Service Act 2006 - s251 - 'Control of patient information'., Health and Social Care Act 2012 s261(2)(b)(ii)
Purposes: No (Clinical Commissioning Group (CCG), Sub ICB Location)
Sensitive: Sensitive
When:DSA runs 2018-12-05 — 2021-06-27 2018.06 — 2021.03.
Access method: Frequent adhoc flow, Frequent Adhoc Flow, One-Off
Data-controller type: NHS BLACK COUNTRY AND WEST BIRMINGHAM CCG, NHS BLACK COUNTRY ICB - D2P2L
Sublicensing allowed: No
Datasets:
- Acute-Local Provider Flows
- Ambulance-Local Provider Flows
- Children and Young People Health
- Community-Local Provider Flows
- Demand for Service-Local Provider Flows
- Diagnostic Imaging Dataset
- Diagnostic Services-Local Provider Flows
- Emergency Care-Local Provider Flows
- Experience, Quality and Outcomes-Local Provider Flows
- Improving Access to Psychological Therapies Data Set
- Maternity Services Data Set
- Mental Health and Learning Disabilities Data Set
- Mental Health Minimum Data Set
- Mental Health Services Data Set
- Mental Health-Local Provider Flows
- National Cancer Waiting Times Monitoring DataSet (CWT)
- 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
- Community Services Data Set
- SUS for Commissioners
- Civil Registration - Births
- Civil Registration - Deaths
- National Diabetes Audit
- Patient Reported Outcome Measures
- National Cancer Waiting Times Monitoring DataSet (NCWTMDS)
- Improving Access to Psychological Therapies Data Set_v1.5
- Community Services Data Set (CSDS)
- Diagnostic Imaging Data Set (DID)
- Improving Access to Psychological Therapies (IAPT) v1.5
- Mental Health and Learning Disabilities Data Set (MHLDDS)
- Mental Health Minimum Data Set (MHMDS)
- Mental Health Services Data Set (MHSDS)
- Civil Registrations of Death
- Patient Reported Outcome Measures (PROMs)
Objectives:
Invoice Validation
Invoice validation is part of a process by which providers of care or services get paid for the work they do.
Invoices are submitted to the Clinical Commissioning Group (CCG) so they are able to ensure that the activity claimed for each patient is their responsibility. This is done by processing and analysing Secondary User Services (SUS+) data, which is received into a secure Controlled Environment for Finance (CEfF). The SUS+ data is identifiable at the level of NHS number. The NHS number is only used to confirm the accuracy of backing-data sets and will not be used further.
Invoice Validation with be conducted by NHS Arden and Greater East Midlands Commissioning Support Unit.
The CCG are advised by NHS Arden and Greater East Midlands Commissioning Support Unit whether payment for invoices can be made or not.
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.
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)
- Diagnostic Imaging Data Set (DIDS)
- Community Services Data Set (CSDS)
- 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 NHS Midlands and Lancashire Commissioning Support Unit.
Yielded Benefits:
.
Expected Benefits:
Invoice Validation
1. Financial validation of activity
2. CCG Budget control
3. Commissioning and performance management
4. Meeting commissioning objectives without compromising patient confidentiality
5. The avoidance of misappropriation of public funds to ensure the ongoing delivery of patient care
Risk Stratification
Risk stratification promotes improved case management in primary care and will lead to the following benefits being realised:
1. Improved planning by better understanding patient flows through the healthcare system, thus allowing commissioners to design appropriate pathways to improve patient flow and allowing commissioners to identify priorities and identify plans to address these.
2. Improved quality of services through reduced emergency readmissions, especially avoidable emergency admissions. This is achieved through mapping of frequent users of emergency services 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. 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.
Outputs:
Invoice Validation
1. Addressing poor data quality issues
2. Production of reports for business intelligence
3. Budget reporting
4. Validation of invoices for non-contracted events
Risk Stratification
1. As part of the risk stratification processing activity detailed above, GPs have access to the risk stratification tool which highlights patients for whom the GP is responsible and have been classed as at risk. The only identifier available to GPs is the NHS numbers of their own patients. Any further identification of the patients will be completed by the GP on their own systems.
2. Output from the risk stratification tool will provide aggregate reporting of number and percentage of population found to be at risk.
3. Record level output will be available for commissioners (of the CCG), pseudonymised at patient level.
4. GP Practices will be able to view the risk scores for individual patients with the ability to display the underlying SUS+ data for the individual patients when it is required for direct care purposes by someone who has a legitimate relationship with the patient.
5. The CCG will be able to target specific patient groups and enable clinicians with the duty of care for the patient to offer appropriate interventions. The CCG will also be able to:
o Stratify populations based on: disease profiles; conditions currently being treated; current service use; pharmacy use and risk of future overall cost
o Plan work for commissioning services and contracts
o Set up capitated budgets
o Identify health determinants of risk of admission to hospital, or other adverse care outcomes.
Commissioning
1. Commissioner reporting:
a. Summary by provider view - plan & actuals year to date (YTD).
b. Summary by Patient Outcome Data (POD) view - plan & actuals YTD.
c. Summary by provider view - activity & finance variance by POD.
d. Planned care by provider view - activity & finance plan & actuals YTD.
e. Planned care by POD view - activity plan & actuals YTD.
f. Provider reporting.
g. Statutory returns.
h. Statutory returns - monthly activity return.
i. Statutory returns - quarterly activity return.
j. Delayed discharges.
k. Quality & performance referral to treatment reporting.
2. Readmissions analysis.
3. Production of aggregate reports for CCG Business Intelligence.
4. Production of project / programme level dashboards.
5. Monitoring of acute / community / mental health quality matrix.
6. Clinical coding reviews / audits.
7. Budget reporting down to individual GP Practice level.
8. GP Practice level dashboard reports include high flyers.
9. Comparators of CCG performance with similar CCGs as set out by a specific range of care quality and performance measures detailed activity and cost reports
10. Data Quality and Validation measures allowing data quality checks on the submitted data
11. Contract Management and Modelling
12. Patient Stratification, such as:
o Patients at highest risk of admission
o Most expensive patients (top 15%)
o Frail and elderly
o Patients that are currently in hospital
o Patients with most referrals to secondary care
o Patients with most emergency activity
o Patients with most expensive prescriptions
o Patients recently moving from one care setting to another
i. Discharged from hospital
ii. Discharged from community
Processing:
Data must only be used as stipulated within this Data Sharing Agreement.
Data Processors must only act upon specific instructions from the Data Controller.
Data can only be stored at the addresses listed under storage addresses.
Patient level data will not be shared outside of the CCG unless it is for the purpose of Direct Care, where it may be shared only with those health professionals who have a legitimate relationship with the patient and a legitimate reason to access the data.
All access to data is managed under Roles-Based Access Controls
No patient level data will be linked other than as specifically detailed within this agreement. Data will only be shared with those parties listed and will only be used for the purposes laid out in the application/agreement. The data to be released from NHS Digital will not be national data, but only that data relating to the specific locality and that data required by the applicant.
NHS Digital reminds all organisations party to this agreement of the need to comply with the Data Sharing Framework Contract requirements, including those regarding the use (and purposes of that use) by “Personnel” (as defined within the Data Sharing Framework Contract ie: employees, agents and contractors of the Data Recipient who may have access to that data)
The DSCRO (part of NHS Digital) will apply Type 2 objections before any identifiable data leaves the DSCRO.
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.
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 for the purpose of Invoice Validation is kept within the CEfF, and only used by staff properly trained and authorised for the activity. Only CEfF staff are able to access data in the CEfF and only CEfF staff operate the invoice validation process within the CEfF. Data flows directly in to the CEfF from the DSCRO and from the providers – it does not flow through any other processors.
Invoice Validation
Data Processor – NHS Arden and Greater East Midlands Commissioning Support Unit
1. Identifiable SUS+ Data is obtained from the SUS+ Repository to the Data Services for Commissioners Regional Office (DSCRO).
2. The DSCRO pushes a one-way data flow of SUS+ data into the Controlled Environment for Finance (CEfF) in the NHS Arden and Greater East Midlands Commissioning Support Unit.
3. NHS Arden and Greater East Midlands Commissioning Support Unit carry out the following processing activities within the CEfF for invoice validation purposes:
a. Validating that the Clinical Commissioning Group is responsible for payment for the care of the individual by using SUS+ and/or backing flow data.
b. Once the backing information is received, this will be checked against national NHS and local commissioning policies as well as being checked against system access and reports provided by NHS Digital to confirm the payments are:
i. In line with Payment by Results tariffs
ii. are in relation to a patient registered with a CCG GP or resident within the CCG area.
iii. The health care provided should be paid by the CCG in line with CCG guidance.
4. The CCG are notified that the invoice has been validated and can be paid. Any discrepancies or non-validated invoices are investigated and resolved between NHS Arden and Greater East Midlands Commissioning Support Unit CEfF team and the provider meaning that no identifiable data needs to be sent to the CCG. The CCG only receives notification to pay and management reporting detailing the total quantum of invoices received pending, processed etc.
Risk Stratification
Data Processor – NHS Midlands and Lancashire Commissioning Support Unit
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 NHS Midlands and Lancashire Commissioning Support Unit, who hold the SUS+ data within the secure Data Centre on N3.
3. Identifiable GP Data is securely sent from the GP system to NHS Midlands and Lancashire Commissioning Support Unit.
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 NHS Midlands and Lancashire Commissioning Support Unit has completed the processing, the CCG can access the online system via a secure connection to access the data pseudonymised at patient level.
Commissioning
Data Processor – NHS 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) , National Cancer Waiting Times Monitoring Data Set (CWT) and Diagnostic Imaging data (DIDS) only is securely transferred from the DSCRO to NHS Midlands and Lancashire Commissioning Support Unit.
2. NHS Midlands and Lancashire Commissioning Support Unitadd 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. NHS 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 NHS 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.
GDPPR COVID-19 CCG - Pseudo — DARS-NIC-402684-K6V7T
Type of data: information not disclosed for TRE projects
Opt outs honoured: No - Statutory exemption to flow confidential data without consent, Anonymised - ICO Code Compliant (Statutory exemption to flow confidential data without consent)
Legal basis: CV19: Regulation 3 (4) of the Health Service (Control of Patient Information) Regulations 2002, CV19: Regulation 3 (4) of the Health Service (Control of Patient Information) Regulations 2002; Health and Social Care Act 2012 - s261(5)(d)
Purposes: No (Clinical Commissioning Group (CCG), Sub ICB Location)
Sensitive: Sensitive
When:DSA runs 2020-09-25 — 2021-03-31 2021.01 — 2021.02.
Access method: One-Off, Frequent Adhoc Flow
Data-controller type: NHS BLACK COUNTRY AND WEST BIRMINGHAM CCG, NHS BLACK COUNTRY ICB - D2P2L
Sublicensing allowed: No
Datasets:
- GPES Data for Pandemic Planning and Research (COVID-19)
- COVID-19 General Practice Extraction Service (GPES) Data for Pandemic Planning and Research (GDPPR)
Objectives:
NHS Digital has been provided with the necessary powers to support the Secretary of State’s response to COVID-19 under the COVID-19 Public Health Directions 2020 (COVID-19 Directions) and support various COVID-19 purposes, the data shared under this agreement can be used for these specified purposes except where they would require the reidentification of individuals.
GPES data for pandemic planning and research (GDPPR COVID 19)
To support the response to the outbreak, NHS Digital has been legally directed to collect and analyse healthcare information about patients from their GP record for the duration of the COVID-19 emergency period under the COVID-19 Directions.
The data which NHS Digital has collected and is providing under this agreement includes coded health data, which is held in a patient’s GP record, such as details of:
• diagnoses and findings
• medications and other prescribed items
• investigations, tests and results
• treatments and outcomes
• vaccinations and immunisations
Details of any sensitive SNOMED codes included in the GDPPR data set can be found in the Reference Data and GDPPR COVID 19 user guides hosted on the NHS Digital website. SNOMED codes are included in GDPPR data.
There are no free text record entries in the data.
The Controller will use the pseudonymised GDPPR COVID 19 data to provide intelligence to support their local response to the COVID-19 emergency. The data is analysed so that health care provision can be planned to support the needs of the population within the CCG area for the COVID-19 purposes.
Such uses of the data include but are not limited to:
• Analysis of missed appointments - Analysis of local missed/delayed referrals due to the COVID-19 crisis to estimate the potential impact and to estimate when ‘normal’ health and care services may resume, linked to Paragraph 2.2.3 of the COVID-19 Directions.
• Patient risk stratification and predictive modelling - to highlight patients at risk of requiring hospital admission due to COVID-19, computed using algorithms executed against linked de-identified data, and identification of future service delivery models linked to Paragraph 2.2.2 of the COVID-19 Directions. As with all risk stratification, this would lead to the identification of the characteristics of a cohort that could subsequently, and separately, be used to identify individuals for intervention. However the identification of individuals will not be done as part of this data sharing agreement, and the data shared under this agreement will not be reidentified.
• Resource Allocation - In order to assess system wide impact of COVID-19, the GDPPR COVID 19 data will allow reallocation of resources to the worst hit localities using their expertise in scenario planning, clinical impact and assessment of workforce needs, linked to Paragraph 2.2.4 of the COVID-19 Directions:
The data may only be linked by the Data Controller or their respective Data Processor, to other pseudonymised datasets which it holds under a current data sharing agreement only where such data is provided for the purposes of general commissioning by NHS Digital. The Health Service Control of Patient Information Regulations (COPI) will also apply to any data linked to the GDPPR data.
The linked data may only be used for purposes stipulated within this agreement and may only be held and used whilst both data sharing agreements are live and in date. Using the linked data for any other purposes, including non-COVID-19 purposes would be considered a breach of this agreement. Reidentification of individuals is not permitted under this DSA.
LEGAL BASIS FOR PROCESSING DATA:
Legal Basis for NHS Digital to Disseminate the Data:
NHS Digital is able to disseminate data with the Recipients for the agreed purposes under a notice issued to NHS Digital by the Secretary of State for Health and Social Care under Regulation 3(4) of the Health Service Control of Patient Information Regulations (COPI) dated 17 March 2020 (the NHSD COPI Notice).
The Recipients are health organisations covered by Regulation 3(3) of COPI and the agreed purposes (paragraphs 2.2.2-2.2.4 of the COVID-19 Directions, as stated below in section 5a) for which the disseminated data is being shared are covered by Regulation 3(1) of COPI.
Under the Health and Social Care Act, NHS Digital is relying on section 261(5)(d) – necessary or expedient to share the disseminated data with the Recipients for the agreed purposes.
Legal Basis for Processing:
The Recipients are able to receive and process the disseminated data under a notice issued to the Recipients by the Secretary of State for Health and Social Care under Regulation 3(4) of COPI dated 20th March (the Recipient COPI Notice section 2).
The Secretary of State has issued notices under the Health Service Control of Patient Information Regulations 2002 requiring the following organisations to process information:
Health organisations
“Health Organisations” defined below under Regulation 3(3) of COPI includes CCGs for the reasons explained below. These are clinically led statutory NHS bodies responsible for the planning and commissioning of health care services for their local area
The Secretary of State for Health and Social Care has issued NHS Digital with a Notice under Regulation 3(4) of the National Health Service (Control of Patient Information Regulations) 2002 (COPI) to require NHS Digital to share confidential patient information with organisations permitted to process confidential information under Regulation 3(3) of COPI. These include:
• persons employed or engaged for the purposes of the health service
Under Section 26 of the Health and Social Care Act 2012, CCG’s have a duty to provide and manage health services for the population.
Regulation 7 of COPI includes certain limitations. The request has considered these limitations, considering data minimisation, access controls and technical and organisational measures.
Under GDPR, the Recipients can rely on Article 6(1)(c) – Legal Obligation to receive and process the Disclosed Data from NHS Digital for the Agreed Purposes under the Recipient COPI Notice. As this is health information and therefore special category personal data the Recipients can also rely on Article 9(2)(h) – preventative or occupational medicine and para 6 of Schedule 1 DPA – statutory purpose.
Expected Benefits:
• Manage demand and capacity
• Reallocation of resources
• Bring in additional workforce support
• Assists commissioners to make better decisions to support patients
• Identifying COVID-19 trends and risks to public health
• Enables CCGs to provide guidance and develop policies to respond to the outbreak
• Controlling and helping to prevent the spread of the virus
Outputs:
• Operational planning to predict likely demand on primary, community and acute service for vulnerable patients due to the impact of COVID-19
• Analysis of resource allocation
• Investigating and monitoring the effects of COVID-19
• Patient Stratification in relation to COVID-19, such as:
o Patients at highest risk of admission
o Frail and elderly
o Patients that are currently in hospital
o Patients with prescriptions related to COVID-19
o Patients recently Discharged from hospital
For avoidance of doubt these are pseudonymised patient cohorts, not identifiable.
Processing:
PROCESSING CONDITIONS:
Data must only be used for the purposes stipulated within this Data Sharing Agreement. Any additional disclosure / publication will require further approval from NHS Digital.
Data Processors must only act upon specific instructions from the Data Controller.
All access to data is managed under Role-Based Access Controls. Users can only access data authorised by their role and the tasks that they are required to undertake.
Patient level data will not be linked other than as specifically detailed within this Data Sharing Agreement.
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 i.e.: employees, agents and contractors of the Data Recipient who may have access to that data).
The Recipients will take all required security measures to protect the disseminated data and they will not generate copies of their cuts of the disseminated data unless this is strictly necessary. Where this is necessary, the Recipients will keep a log of all copies of the disseminated data and who is controlling them and ensure these are updated and destroyed securely.
Onward sharing of patient level data is not permitted under this agreement. Only aggregated reports with small number suppression can be shared externally.
The data disseminated will only be used for COVID-19 GDPPR purposes as described in this DSA, any other purpose is excluded.
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.
AUDIT
All access to data is auditable by NHS Digital in accordance with the Data Sharing Framework Contract and NHS Digital terms.
Under the Local Audit and Accountability Act 2014, section 35, Secretary of State has power to audit all data that has flowed, including under COPI.
DATA MINIMISATION:
Data Minimisation in relation to the data sets listed within the application are listed below:
• Patients who are normally registered and/or resident within the CCG region (including historical activity where the patient was previously registered or resident in another commissioner area).
and/or
• Patients treated by a provider where the CCG is the host/co-ordinating commissioner and/or has the primary responsibility for the provider services in the local health economy.
and/or
• Activity identified by the provider and recorded as such within national systems (such as SUS+) as for the attention of the CCG.
The Data Services for Commissioners Regional Office (DSCRO) obtains the following data sets:
- GDPPR COVID 19 Data
Pseudonymisation is completed within the DSCRO and is then disseminated as follows:
1. Pseudonymised GDPPR COVID 19 data is securely transferred from the DSCRO to the Data Controller / Processor
2. Aggregation of required data will be completed by the Controller (or the Processor as instructed by the Controller).
3. Patient level data may not be shared by the Controller (or any of its processors).
Project 4 — DARS-NIC-148002-B0M0Z
Type of data: information not disclosed for TRE projects
Opt outs honoured: Yes - patient objections upheld (Section 251)
Legal basis: National Health Service Act 2006 - s251 - 'Control of patient information'.
Purposes: ()
Sensitive: Sensitive
When:2018.10 — 2019.04.
Access method: Frequent Adhoc Flow
Data-controller type:
Sublicensing allowed:
Datasets:
- SUS for Commissioners
Objectives:
Invoice Validation
Invoice validation is part of a process by which providers of care or services get paid for the work they do.
Invoices are submitted to the Clinical Commissioning Group (CCG) so they are able to ensure that the activity claimed for each patient is their responsibility. This is done by processing and analysing Secondary User Services (SUS) data, which is received into a secure Controlled Environment for Finance (CEfF). The SUS data is identifiable at the level of NHS number. The NHS number is only used to confirm the accuracy of backing-data sets and will not be used further.
The legal basis for this to occur is under Section 251 of NHS Act 2006.
Invoice Validation with be conducted by NHS Arden and Greater East Midlands (GEM) Commissioning Support Unit (CSU)
The CCG are advised by NHS Arden and Greater East Midlands (GEM) Commissioning Support Unit (CSU)whether payment for invoices can be made or not.
Expected Benefits:
Invoice Validation
1. Financial validation of activity
2. CCG Budget control
3. Commissioning and performance management
4. Meeting commissioning objectives without compromising patient confidentiality
5. The avoidance of misappropriation of public funds to ensure the ongoing delivery of patient care
Outputs:
Invoice Validation
1. Addressing poor data quality issues
2. Production of reports for business intelligence
3. Budget reporting
4. Validation of invoices for non-contracted events
Processing:
Data must only be used as stipulated within this Data Sharing Agreement.
Data Processors must only act upon specific instructions from the Data Controller.
Data can only be stored at the addresses listed under storage addresses.
The Data Controller and any Data Processor will only have access to records of patients of residence and registration within the CCG.
Patient level data will not be shared outside of the CCG unless it is for the purpose of Direct Care, where it may be shared only with those health professionals who have a legitimate relationship with the patient and a legitimate reason to access the data.
CCGs should work with general practices within their CCG to help them fulfil data controller responsibilities regarding flow of identifiable data into risk stratification tools.
No patient level data will be linked other than as specifically detailed within this agreement. Data will only be shared with those parties listed and will only be used for the purposes laid out in the application/agreement. The data to be released from NHS Digital will not be national data, but only that data relating to the specific locality of interest of the applicant.
The DSCRO (part of NHS Digital) will apply Type 2 objections before any identifiable data leaves the DSCRO.
NHS Digital reminds all organisations party to this agreement of the need to comply with the Data Sharing Framework Contract requirements, including those regarding the use (and purposes of that use) by “Personnel” (as defined within the Data Sharing Framework Contract ie: employees, agents and contractors of the Data Recipient who may have access to that data)
Segregation
Where the Data Processor and/or the Data Controller hold both identifiable and pseudonymised data, the data will be held separately so data cannot be linked.
All access to data is audited
Invoice Validation
1. Identifiable SUS Data is obtained from the SUS Repository to the Data Services for Commissioners Regional Office (DSCRO).
The DSCRO pushes a one-way data flow of SUS data into the Controlled Environment for Finance (CEfF) in NHS Arden and Greater East Midlands (GEM) Commissioning Support Unit (CSU)
2. The CSU carry out the following processing activities within the CEfF for invoice validation purposes:
a. Checking the individual is registered to a particular Clinical Commissioning Group (CCG) and associated with an invoice from the SUS data flow to validate the corresponding record in the backing data flow
b. Once the backing information is received, this will be checked against national NHS and local commissioning policies as well as being checked against system access and reports provided by NHS Digital to confirm the payments are:
i. In line with Payment by Results tariffs
ii. are in relation to a patient registered with a CCG GP or resident within the CCG area.
iii. The health care provided should be paid by the CCG in line with CCG guidance.
The CCG are notified that the invoice has been validated and can be paid. Any discrepancies or non-validated invoices are investigated and resolved between NHS Arden and Greater East Midlands (GEM) Commissioning Support Unit (CSU)
CEfF team and the provider meaning that no identifiable data needs to be sent to the CCG. The CCG only receives notification to pay and management reporting detailing the total quantum of invoices received pending, processed etc.
Project 5 — NIC-41158-X3V7D
Type of data: information not disclosed for TRE projects
Opt outs honoured: N, Y
Legal basis: Health and Social Care Act 2012, Section 251 approval is in place for the flow of identifiable data
Purposes: ()
Sensitive: Sensitive
When:2017.09 — 2017.05.
Access method: Ongoing
Data-controller type:
Sublicensing allowed:
Datasets:
- Local Provider Data - Acute
- Local Provider Data - Demand For service
- Local Provider Data - Mental Health
- Improving Access to Psychological Therapies Data Set
- Mental Health Minimum Data Set
- SUS data (Accident & Emergency, Admitted Patient Care & Outpatient)
- SUS for Commissioners
- Public Health and Screening Services-Local Provider Flows
- Primary Care Services-Local Provider Flows
- Population Data-Local Provider Flows
- Other Not Elsewhere Classified (NEC)-Local Provider Flows
- Mental Health-Local Provider Flows
- Mental Health Services Data Set
- Mental Health and Learning Disabilities Data Set
- Maternity Services Data Set
- Experience, Quality and Outcomes-Local Provider Flows
- Emergency Care-Local Provider Flows
- Diagnostic Services-Local Provider Flows
- Diagnostic Imaging Dataset
- Demand for Service-Local Provider Flows
- Community-Local Provider Flows
- Children and Young People Health
- Ambulance-Local Provider Flows
- Acute-Local Provider Flows
- SUS (Accident & Emergency, Inpatient and Outpatient data)
- Local Provider Data - Acute, Ambulance, Community, Demand for Service, Diagnostic Services, Emergency Care, Experience Quality and Outcomes, Mental Health, Primary Care
- Children and Young People's Health Services Data Set
- Local Provider Data - Ambulance
- Local Provider Data - Community
- Local Provider Data - Diagnostic Services
- Local Provider Data - Emergency Care
- Local Provider Data - Primary Care
- SUS Accident & Emergency data
- SUS Admitted Patient Care data
- SUS Outpatient data
Objectives:
Invoice Validation
As an approved Controlled Environment for Finance (CEfF), the data processor receives SUS data identifiable at the level of NHS number to undertake invoice validation on behalf of the CCG. In order to support commissioning of patient care by validating non-contracted activity in the CCG, this data is required for the purpose of invoice validation. NHS number is only used to confirm the accuracy of backing-data sets and will not be shared outside of the CEfF.
Risk Stratification
This is an application to use SUS data identifiable at the level of NHS number for the purpose of Risk Stratification. Risk Stratification provides a forecast of future demand by identifying high risk patients. This enables commissioners to initiate proactive management plans for patients that are potentially high service users.
Pseudonymised – SUS and Local Flows
Application for the CCG to use pseudonymised data to provide intelligence to support commissioning of health services. The pseudonymised data is required to ensure that analysis of health care provision can be completed to support the needs of the health profile of the population within the CCG area based on the full analysis of multiple pseudonymised datasets.
Pseudonymised – Mental Health, Maternity, IAPT, CYPHS and DIDS
Application for the CCG to use MHSDS, MHMDS, MHLDDS, MSDS, IAPT, CYPHS and DIDs linked and pseudonymised data to provide intelligence to support commissioning of health services. The linked, pseudonymised data is required to ensure that analysis of health care provision can be completed to support the needs of the health profile of the population within the CCG area based on the full analysis of multiple pseudonymised datasets.
No record level data will be linked other than as specifically detailed within this application/agreement. Data will only be shared with those parties listed and will only be used for the purposes laid out in the application/agreement. The data to be released from the HSCIC will not be national data, but only that data relating to the specific locality of interest of the applicant.
Expected Benefits:
Invoice Validation
1) Financial validation of activity
2) CCG Budget control
3) Commissioning and performance management
4) Meeting commissioning objectives without compromising patient confidentiality
5) The avoidance of misapproproation of public funds to ensure the on-going delivery of patient care
Risk Stratification
Risk stratification promotes improved case management in primary care and will lead to the following benefits being realised:
1) Improved planning by better understanding patient flows through the healthcare system, thus allowing commissioners to design appropriate pathways to improve patient flow and allowing commissioners to identify priorities and identify plans to address these.
2) Improved quality of services through reduced emergency readmissions, especially avoidable emergency admissions. This is achieved through mapping of frequent users of emergency services and early intervention of appropriate care.
3) Improved access to services by identifying which services may be in demand but have poor access, and from this identify areas where improvement is required.
4) Potentially reduced premature mortality by more targeted intervention in primary care, which supports the commissioner to meets its requirement to reduce premature mortality in line with the CCG Outcome Framework.
5) Better understanding of the health of and the variations in health outcomes within the population to help understand local population characteristics.
All of the above lead to improved patient experience through more effective commissioning of services.
Pseudonymised – SUS and Local Flows
1) Supporting Quality Innovation Productivity and Prevention (QIPP) to review demand management and pathways.
2) 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) flows
3) Commissioning cycle support for grouping and re-costing previous activity
4) Enables monitoring of:
a) CCG outcome indicators
b) Non-financial validation of patient level data
c) Successful delivery of integrated care within the CCG
d) Checking frequent or multiple attendances to improve early intervention and avoid admissions
e) Commissioning and performance management
5) Feedback to NHS service providers on data quality at an aggregate level
Pseudonymised – Mental Health, Maternity, IAPT, CYPHS and DIDS
1) Supporting Quality Innovation Productivity and Prevention (QIPP) to review demand management and pathways.
2) Supporting Joint Strategic Needs Assessment (JSNA) for specific disease types.
3) Health economic modelling using:
(a) Analysis on provider performance.
(b) Learning from and predicting likely patient pathways for certain conditions, in order to influence early interventions and other treatments for patients.
(c) Analysis of outcome measures for differential treatments, accounting for the full patient pathway.
4) Commissioning cycle support for grouping and re-costing previous activity.
5) Enables monitoring of:
(a) CCG outcome indicators.
(b) Non-financial validation of activity.
(c) Successful delivery of integrated care within the CCG.
(d) Checking frequent or multiple attendances to improve early intervention and avoid admissions.
(e) Case management.
(f) Care service planning.
(g) Commissioning and performance management.
(h) 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.
Outputs:
Invoice Validation
1) Addressing poor data quality issues
2) Production of reports for business intelligence
3) Budget reporting
4) Validation of invoices for non-contracted events
Risk Stratification
1) As part of the risk stratification processing activity detailed above, GPs have access to the risk stratification tool which highlights patients for whom the GP is responsible and have been classed as at risk. The only identifier available to GPs is the NHS numbers of their own patients. Any further identification of the patients will be completed by the GP on their own systems.
2) Output from the risk stratification tool will provide aggregate reporting of number and percentage of population found to be at risk with no identifiers
3) Record level output will be available for commissioners in anonymised or pseudonymised format.
4) GP Practices will be able to view the risk scores for individual patients with the ability to display the underlying SUS data for the individual patients when it is required for direct care purposes by someone who has a legitimate relationship with the patient.
Pseudonymised – SUS and Local Flows
1) Commissioner reporting:
(a) Summary by provider view - plan & actuals year to date (YTD).
(b) Summary by Patient Outcome Data (POD) view - plan & actuals YTD.
(c) Summary by provider view - activity & finance variance by POD.
(d) Planned care by provider view - activity & finance plan & actuals YTD.
(e) Planned care by POD view - activity plan & actuals YTD.
(f) Provider reporting.
(g) Statutory returns.
(h) Statutory returns - monthly activity return.
(i) Statutory returns - quarterly activity return.
(j) Delayed discharges.
(k) Quality & performance referral to treatment reporting.
2) Readmissions analysis.
3) Production of aggregate reports for CCG Business Intelligence.
4) Production of project / programme level dashboards.
5) Monitoring of acute / community / mental health quality matrix.
6) Clinical coding reviews / audits.
7) Budget reporting down to individual GP Practice level.
8) GP Practice level dashboard reports include high flyers.
Pseudonymised – Mental Health, Maternity, IAPT, CYPHS and DIDS
1) Commissioner reporting:
(a) Summary by provider view - plan & actuals year to date (YTD).
(b) Summary by Patient Outcome Data (POD) view - plan & actuals YTD.
(c) Summary by provider view - activity & finance variance by POD.
(d) Planned care by provider view - activity & finance plan & actuals YTD.
(e) Planned care by POD view - activity plan & actuals YTD.
(f) Provider reporting.
(g) Statutory returns.
(h) Statutory returns - monthly activity return.
(i) Statutory returns - quarterly activity return.
(j) Delayed discharges.
(k) Quality & performance referral to treatment reporting.
2) Readmissions analysis.
3) Production of aggregate reports for CCG Business Intelligence.
4) Production of project / programme level dashboards.
5) Monitoring of acute / community / mental health quality matrix.
6) Clinical coding reviews / audits.
7) Budget reporting down to individual GP Practice level.
Processing:
Central Midlands DSCRO will apply Type 2 objections (from 1st October 2016 onwards) before any identifiable data leaves the DSCRO.
Invoice Validation
1) Central and Midlands DSCRO pushes a one-way data flow of SUS data into the Controlled Environment for Finance (CEfF) in the Arden and GEM CSU (Data Processor 2).
2) The CSU carry out the following processing activities within the CEfF for invoice validation purposes:
a) Checking the individual is registered to a particular Clinical Commissioning Group (CCG) by using the derived commissioner field in SUS and associated with an invoice from the national SUS data flow to validate the corresponding record in the backing data flow
b) Once the backing information is received, this will be checked against system access and reports provided by the HSCIC to confirm the payments are:
- In line with Payment by Results tariffs
- are in relation to a patient registered with a CCG GP or resident within the CCG area.
- The health care provided should be paid by the CCG in line with CCG guidance.
3) The CCG are notified of that the invoice has been validated and can be paid. Any discrepancies or non-validated invoices are investigated and resolved between Arden and GEM CSU and the provider meaning that no data needs to be sent to the CCG. The CCG only receives notification to pay.
Risk Stratification
1) SUS data identifiable at the level of NHS number regarding hospital admissions, A&E attendances and outpatient attendances is delivered securely from Central and Midlands Data Services for Commissioners Regional Office (DSCRO) to the data processor.
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 Midlands and Lancashire CSU (Data Processor 1), who hold the SUS data within the secure Data Centre on N3.
3) SUS data is linked to GP data in the risk stratification tool by the data processor.
4) 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.
5) Midlands and Lancashire CSU who hosts the risk stratification system that holds SUS data is limited to those administrative staff with authorised user accounts used for identification and authentication.
6) Once Midlands and Lancashire CSU has completed the processing, the CCG can dial in to the online system via N3 connection to access the data anonymised at patient level.
Pseudonymised – SUS and Local Flows
1) Central and Midlands Data Services for Commissioners Regional Office (DSCRO) receives a flow of SUS identifiable data for the CCG from the SUS Repository. Central and Midlands DSCRO also receives identifiable local provider data for the CCG directly from Providers.
2) Data quality management of data is completed by the DSCRO and the identifiable data is then passed securely to North England CSU for the addition of derived fields, linkage of data sets and analysis.
3) Midlands and Lancashire CSU then pass the processed, pseudonymised and linked data to the CCG who analyse the data to see patient journeys for pathways or service design, re-design and de-commissioning.
4) Patient level data will not be shared outside of the CCG. External aggregated reports only.
Pseudonymised – Mental Health, MSDS, IAPT, CYPHS and DIDS
1) Central and Midlands Data Services for Commissioning Regional Office (DSCRO) will receive a flow of pseudonymised patient level data for each CCG for Mental Health (MHSDS, MHMDS, MHLDDS), Improving Access to Psychological Therapies (IAPT), Maternity (MSDS), Child and Young People’s Health (CYPHS) and Diagnostic Imaging (DIDS) for commissioning purposes
2) Data quality management of data is completed by the DSCRO and the pseudonymised data is then passed securely to Midlands and Lancashire CSU for the addition of derived fields, linkage of data sets and analysis. Linkage is not with other datasets just between the data contained within the dataset itself.
3) Midlands and Lancashire CSU then pass the processed, pseudonymised and linked data to the CCG who analyse the data to see patient journeys for pathways or service design, re-design and de-commissioning.
4) The CCG analyses the data to see patient journeys for pathway or service design, re-design and de-commissioning
5) The CCG completes aggregation of required data for CCG management use – disclosing any outputs at the appropriate level.
6) Patient level data will not be shared outside of the CCG. External aggregated reports only with small numbers suppressed with HSCIC guidance.