NHS Digital Data Release Register - reformatted

NHS Tameside And Glossop CCG projects

DSfC - NHS Tameside & Glossop CCG, RS, Comm.
DSfC - NHS Tameside and Glossop CCG - IV
Project 3
Project 4

1004 data files in total were disseminated unsafely (information about files used safely is missing for TRE/"system access" projects).

🚩 NHS Tameside And Glossop CCG was sent multiple files from the same dataset, in the same month, both with optouts respected and with optouts ignored. NHS Tameside And Glossop 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 Tameside & Glossop CCG, RS, Comm. — DARS-NIC-47189-W3X0L

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 (Does not include the flow of confidential data, 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), Health and Social Care Act 2012 - s261 - 'Other dissemination of information', 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 2019-05-01 — 2022-04-30 2018.10 — 2021.05.

Access method: Frequent Adhoc Flow, One-Off

Data-controller type: NHS TAMESIDE AND GLOSSOP CCG, NHS GREATER MANCHESTER ICB - 01Y

Sublicensing allowed: No

Datasets:

Acute-Local Provider Flows
Ambulance-Local Provider Flows
Children and Young People Health
Community Services Data Set
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
SUS for Commissioners
Civil Registration - Births
Civil Registration - Deaths
e-Referral Service for Commissioning
Medicines dispensed in Primary Care (NHSBSA data)
National Diabetes Audit
Patient Reported Outcome Measures
Personal Demographic Service
Summary Hospital-level Mortality Indicator
National Cancer Waiting Times Monitoring DataSet (NCWTMDS)
Improving Access to Psychological Therapies Data Set_v1.5
Civil Registrations of Death
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)
Patient Reported Outcome Measures (PROMs)
Summary Hospital-level Mortality Indicator (SHMI)

Objectives:

Risk Stratification
To use SUS data identifiable at the level of NHS number according to S.251 CAG 7-04(a) (and Primary Care Data) for the purpose of Risk Stratification. Risk Stratification provides a forecast of future demand by identifying high risk patients. This enables commissioners to initiate proactive management plans for patients that are potentially high service users. Risk Stratification enables General Practitioners (GPs) to better target intervention in Primary Care.
Risk Stratification will be conducted by Arden and Greater East Midlands Commissioning Support Unit.

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.
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)
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
- Greater Manchester Shared Services (GMSS)
- Advancing Quality Alliance (AQuA)
- The Academic Health Sciences Network

Yielded Benefits:

N/A

Expected 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 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.
6. 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. 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.

Outputs:

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.

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. Access is limited to those substantive employees with authorised user accounts used for identification and authentication.

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 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 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.
Risk Stratification
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 and Greater East Midlands 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 Arden and Greater East Midlands 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. Access to the Risk Stratification system that Arden and Greater East Midlands Commissioning Support Unit hosts is limited to those substantive employees with authorised user accounts used for identification and authentication.
Once Arden and Greater East Midlands Commissioning Support Unit has completed the processing, the CCG can access the online system via a secure N3 connection to access the data pseudonymised at patient level.

Commissioning
The Data Services for Commissioners Regional Office (DSCRO) obtains the following data sets:
1. SUS
2. Local Provider Flows (received directly from providers)
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
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. Diagnostic Imaging Data Set (DIDS)
Data quality management and pseudonymisation is completed within the DSCRO and is then disseminated as follows:
Data Processor 1 – Arden and Greater East Midlands 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) and Diagnostic Imaging data (DIDS) only is securely transferred from the DSCRO to Arden and Greater East Midlands Commissioning Support Unit.
2) Arden and Greater East Midlands Commissioning Support Unit 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 Unit then pass the processed, pseudonymised and linked data to the CCG. The CCG analyse the data to see patient journeys for pathways or service design, re-design and de-commissioning.
5) Aggregation of required data for CCG management use will be completed by Arden and Greater East Midlands 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.

Data Processor 2 – Greater Manchester Shared Services (GMSS)
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 Greater Manchester Shared Services (GMSS)
5) Greater Manchester Shared Services (GMSS) analyse and conduct the BI function and then send the Pseudonymised data to the CCG.
6) Aggregation of required data for CCG management use will be completed by Greater Manchester Shared Services (GMSS) or the CCG as instructed by the CCG.
7) Patient level data will not be shared outside of the CCG and will only be shared within the CCG on a need to know basis, as per the purposes stipulated within the Data Sharing Agreement. External aggregated reports only with small number suppression can be shared

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.
1. 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.
4) Aggregation of required data for CCG management use will be completed by Advancing Quality Alliance (AQuA).
5) Patient level data will not be shared outside of the CCG and will only be shared within the CCG on a need to know basis, as per the purposes stipulated within the Data Sharing Agreement. External aggregated reports only with small number suppression can be shared

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

DSfC - NHS Tameside and Glossop CCG - IV — DARS-NIC-180025-W6S0Z

Type of data: information not disclosed for TRE projects

Opt outs honoured: Yes - patient objections upheld, No - DSfC for Invoice Validation Purposes, Identifiable (Section 251, Section 251 NHS Act 2006)

Legal basis: National Health Service Act 2006 - s251 - 'Control of patient information'. , Health and Social Care Act 2012 s261(7); National Health Service Act 2006 - s251 - 'Control of patient information'.

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

Sensitive: Sensitive

When:DSA runs 2019-02-06 — 2022-02-05 2018.10 — 2021.05.

Access method: Frequent Adhoc Flow, One-Off

Data-controller type: NHS TAMESIDE AND GLOSSOP CCG, NHS GREATER MANCHESTER ICB - 01Y

Sublicensing allowed: No

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.

Invoice Validation with be conducted by NHS Tameside and Glossop CCG
The CCG are advised by their inhouse team whether payment for invoices can be made or not

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
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.
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 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 CCG and from the providers – it does not flow through any other processors.

Invoice Validation

1. Identifiable SUS+ Data is obtained from the SUS+ Repository by 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) located in the CCG.
3. The CEfF conduct the following processing activities 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. In relation to a patient registered with the CCG GP or resident within the CCG area.
iii. The health care provided should be paid by the CCG in line with CCG guidance.
4. The CCG are notified by the CEfF that the invoice has been validated and can be paid. Any discrepancies or non-validated invoices are investigated and resolved

Project 3 — DARS-NIC-193396-Z2Q3C

Type of data: information not disclosed for TRE projects

Opt outs honoured: No - data flow is not identifiable, Yes - patient objections upheld

Legal basis: Health and Social Care Act 2012 – s261(1) and s261(2)(b)(ii), Health and Social Care Act 2012 – s261(7)

Purposes: ()

Sensitive: Sensitive

When:2018.10 — 2019.04.

Access method: Frequent Adhoc Flow

Data-controller type:

Sublicensing allowed:

Datasets:

Acute-Local Provider Flows
Ambulance-Local Provider Flows
Children and Young People Health
Community-Local Provider Flows
Demand for Service-Local Provider Flows
Diagnostic Imaging Dataset
Diagnostic Services-Local Provider Flows
Emergency Care-Local Provider Flows
Experience, Quality and Outcomes-Local Provider Flows
Improving Access to Psychological Therapies Data Set
Maternity Services Data Set
Mental Health and Learning Disabilities Data Set
Mental Health Minimum Data Set
Mental Health Services Data Set
Mental Health-Local Provider Flows
Other Not Elsewhere Classified (NEC)-Local Provider Flows
Population Data-Local Provider Flows
Primary Care Services-Local Provider Flows
Public Health and Screening Services-Local Provider Flows
SUS for Commissioners

Objectives:

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

Processing for commissioning will be conducted by Arden and GEM Commissioning Support Unit
Salford Royal NHS Foundation Trust in their capacity as Data Processor will, in addition, utilise internal teams as follows:
Advancing Quality Alliance (AQuA) provide support for a range of quality improvement programmes across regions of CCGs, (listed within the Data Sharing Agreement), undertaking analyses and producing aggregate reports for the CCGs. AQuA includes the NW Advancing Quality (AQ) Programme, which was set up to help drive quality improvements across the region. The AQ programme focusses on several clinical focus areas which affect many patients in the region. These evidence based clinical focus areas fall into categories such as cardiac conditions, orthopaedics (for example Hip and Knee replacement surgery) and respiratory conditions.
The overarching aim of the AQ programme is to identify if specific treatment pathways commissioned by CCGs and delivered by the trusts are meeting recommended guidelines for quality, and through working with the trusts and CCGs, improving the performance of the pathways and ensuring patients get the most appropriate treatment for their condition regardless of which hospital they are treated in.
To enable them to undertake this work, AQuA requires pseudonymised SUS data and local provider flows which have been specified for the AQ Programme.
- Secondary Uses Service (SUS)
- Local Provider Flows
o Acute

Expected Benefits:

The following measurable benefits are expected through each team
AQuA
The AQ Programme is a Quality Improvement and Audit programme that identifies a set of robust, evidence based clinical quality measures for given focus areas. The measures represent a standard clinical practice that providers agree patients in the relevant cohort should receive. AQ are currently working with 8 clinical focus areas and each focus area has between 5 and 10 clinical measures.
Each measure would have a beneficial outcome. An evaluation of the early AQ programme evidenced that the pneumonia measures reduced mortality within the Northwest region (N Engl J Med 2012; 367:1821-8). Not all measures have such dramatic outcomes; some measures may improve diagnostic speed or improve patient education. All the measures are directed at ensuring consistency of care, improving implementation of care year on year, and reducing inequality of care from trust to trust.

The detailed information collected can be used to identify areas where care may fall short within a pathway or amongst trusts and be used as the basis for quality improvement. For example, it was identified through analysis of the data that one NW trust was consistently missing the delivery of antibiotics within 4 hours. The ‘CFA audit data’ was used as the basis to review cases and map the processes, and identify the gaps. The trust identified that prescriptions were being written in A&E, but the dosage was not being delivered in A&E. The process was then updated to ensure that the dosage would be delivered before the patients left the A&E for the ward.
Once care has been improved across the region within a clinical focus area and new processes are established, AQ can replace a CFA and work on establishing improvements in new areas.

Ongoing benefits for the CCGs therefore include ensuring equitable standards of care for their patients. In addition, the AQ programme delivers impartial monitoring of standard quality measures that are consistently delivered across annual periods showing year on year improvement and adherence to robust clinical standards. AQ continues to build on current progress with its underpinning values of detailed and evidence-based pathways, strong clinical guidance, peer level networking and support underpinned by excellent data collection with regular robust reporting.

Outputs:

The following outputs are expected through each team:
AQuA
AQuA will use the data to produce a range of reports that will be made available to both commissioners and providers, with specific attention on the clinical focus areas (CFA). All reports will be at an aggregate level and examples include;
1) Monthly coding quality reports to evaluate the completeness of diagnostic coding in the SUS data. The purpose of this report is to ensure that the source data is fit-for-purpose to create the AQ Clinical Focus Area (CFA) populations accurately.
2) Monthly benchmarking reports reporting on the data collection quality of the AQ data. The purpose of this report is to ensure that provider trusts are collecting suitable information in their local data for the identified AQ populations.
3) Monthly benchmarking reports using the collected local CFA data to evaluate the delivery of the AQ CFA measures. The purpose of this report is to allow the provider trusts and CCGs to see the percentage of the AQ population receiving each AQ measure within each trust and compare the performance to other participating trusts.
4) Bi-annual public reports/summary benchmarking reports will be published on the Advancing Quality Alliance website.
•

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 CCGs as follows:

NHS Bolton CCG
NHS Bury CCG
NHS Heywood, Middleton and Rochdale CCG
NHS Manchester CCG
NHS Oldham CCG
NHS Salford CCG
NHS Stockport CCG
NHS Tameside & Glossop CCG
NHS Trafford CCG
NHS Wigan CCG
NHS Eastern Cheshire CCG
NHS Halton CCG
NHS Knowsley CCG
NHS Liverpool CCG
NHS South Cheshire CCG
NHS South Sefton CCG
NHS Southport & Formby CCG
NHS St Helens CCG
NHS Vale Royal CCG
NHS Warrington CCG
NHS West Cheshire CCG
NHS Wirral CCG
NHS Blackburn & Darwin CCG
NHS Blackpool CCG
NHS Chorley & South Ribble CCG
NHS East Lancashire CCG
NHS Fylde & Wyre CCG
NHS Greater Preston CCG
NHS Morecambe Bay CCG
NHS West Lancashire CCG
NHS Cumbria CCG

Patient level data will not be shared outside of the Data Controller.
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 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 i.e: 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. The applicant will not link the data further and the only data linkages are those permitted under this application / Data Sharing Agreement. Data will not be used for reidentification purposes.
All access to data is audited
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
Data quality management and pseudonymisation is completed within the DSCRO and is then disseminated as follows:
1. Pseudonymised SUS+ and Local Provider data only is securely transferred from the DSCRO to Arden and Greater East Midlands Commissioning Support Unit.
2. Arden and Greater East Midlands Commissioning Support Unit add further derived fields and perform additional checks for data quality issues such as local duplication of records, or adjustments for known data recording issues, and prepare the data for further use.
3. Allowed linkage is between the data sets contained within point 1.
4. Arden and Greater East Midlands Commissioning Support Unit then pass the processed, pseudonymised and linked data to Salford Royal NHS Foundation Trust.
5. Salford Royal NHS Foundation Trust analyse the data 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
6. Data is accessed by two teams within Salford Royal NHS Foundation Trust:
- Advancing Quality Alliance (AQuA)
7. Access is via team specific role-based access only and is specific to each team as:
- Advancing Quality Alliance (AQuA) team members have access to SUS+ and local provider flow data only.
8. Aggregation of the data will be completed by Arden and Greater East Midlands Commissioning Support Unit.or (Advancing Quality Alliance (AQuA) within Salford Royal NHS Foundation Trust.
9. Patient level data will not be shared outside of (Advancing Quality Alliance (AQuA) within Salford Royal NHS Foundation Trust and will only be shared within the individual teams on a need to know basis, as per the purposes stipulated within the Data Sharing Agreement. External aggregated reports only with small number suppression will be shared with the 31 CCGs listed in line with NHS Digital guidance applicable to each data set.

Project 4 — NIC-47189-W3X0L

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:2017.06 — 2017.05.

Access method: Ongoing

Data-controller type:

Sublicensing allowed:

Datasets:

Children and Young People's Health Services Data Set
Improving Access to Psychological Therapies Data Set
Local Provider Data - Acute
Local Provider Data - Ambulance
Local Provider Data - Community
Local Provider Data - Demand for Service
Local Provider Data - Diagnostic Services
Local Provider Data - Emergency Care
Local Provider Data - Experience Quality and Outcomes
Local Provider Data - Mental Health
Local Provider Data - Other not elsewhere classified
Local Provider Data - Population Data
Local Provider Data - Public Health & Screening services
Mental Health and Learning Disabilities Data Set
Mental Health Minimum Data Set
Mental Health Services Data Set
SUS Accident & Emergency data
SUS Admitted Patient Care data
SUS Outpatient data
Maternity Services Dataset
SUS data (Accident & Emergency, Admitted Patient Care & Outpatient)
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
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, Other not elsewhere classified, Population Data, Primary Care

Objectives:

Risk Stratification
To use SUS data identifiable at the level of NHS number according to S.251 CAG 7-04(a) (and Primary Care Data) for the purpose of Risk Stratification. Risk Stratification provides a forecast of future demand by identifying high risk patients. This enables commissioners to initiate proactive management plans for patients that are potentially high service users. Risk Stratification enables GPs to better target intervention in Primary Care

Pseudonymised – SUS and Local Flows
To use pseudonymised data to provide intelligence to support commissioning of health services. The pseudonymised data is required to ensure that analysis of health care provision can be completed to support the needs of the health profile of the population within the CCG area based on the full analysis of multiple pseudonymised datasets.
The 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 – Mental Health, Maternity, IAPT, CYPHS and DIDS
To use pseudonymised data for the following datasets to provide intelligence to support commissioning of health services :
- Mental Health Minimum Data Set (MHMDS)
- Mental Health Learning Disability Data Set (MHLDDS)
- Mental Health Services Data Set (MHSDS)
- Maternity Services Data Set (MSDS)
- Improving Access to Psychological Therapy (IAPT)
- Child and Young People Health Service (CYPHS)
- Diagnostic Imaging Data Set (DIDS)
The pseudonymised data is required to ensure that analysis of health care provision can be completed to support the needs of the health profile of the population within the CCG area based on the full analysis of multiple pseudonymised datasets.

No record level data will be linked other than as specifically detailed within this application/agreement. Data will only be shared with those parties listed and will only be used for the purposes laid out in the application/agreement. The data to be released from the HSCIC will not be national data, but only that data relating to the specific locality of interest of the applicant.

Expected 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 and early intervention of appropriate care.
3. Improved access to services by identifying which services may be in demand but have poor access, and from this identify areas where improvement is required.
4. Potentially reduced premature mortality by more targeted intervention in primary care, which supports the commissioner to meets its requirement to reduce premature mortality in line with the CCG Outcome Framework.
5. Better understanding of the health of and the variations in health outcomes within the population to help understand local population characteristics.
All of the above lead to improved patient experience through more effective commissioning of services.

Pseudonymised – SUS and Local Flows
1. Supporting Quality Innovation Productivity and Prevention (QIPP) to review demand management, integrated care and pathways.
a. Analysis to support full business cases.
b. Develop business models.
c. Monitor In year projects.
2. Supporting Joint Strategic Needs Assessment (JSNA) for specific disease types.
3. Health economic modelling using:
a. Analysis on provider performance against 18 weeks wait targets.
b. Learning from and predicting likely patient pathways for certain conditions, in order to influence early interventions and other treatments for patients.
c. Analysis of outcome measures for differential treatments, accounting for the full patient pathway.
d. Analysis to understand emergency care and linking A&E and Emergency Urgent Care Flows (EUCC).
4. Commissioning cycle support for grouping and re-costing previous activity.
5. Enables monitoring of:
a. CCG outcome indicators.
b. Non-financial validation of activity.
c. Successful delivery of integrated care within the CCG.
d. Checking frequent or multiple attendances to improve early intervention and avoid admissions.
e. Case management.
f. Care service planning.
g. Commissioning and performance management.
h. 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.

Pseudonymised – Mental Health, Maternity, IAPT, CYPHS and DIDS
1. Supporting Quality Innovation Productivity and Prevention (QIPP) to review demand management, Integrated care and pathways.
a. Analysis to support full business cases.
b. Develop business models.
c. Monitor In year projects.
2. Supporting Joint Strategic Needs Assessment (JSNA) for specific disease types.
3. Health economic modelling using:
a. Analysis on provider performance against 18 weeks wait targets.
b. Learning from and predicting likely patient pathways for certain conditions, in order to influence early interventions and other treatments for patients.
c. Analysis of outcome measures for differential treatments, accounting for the full patient pathway.
d. Analysis to understand emergency care and linking A&E and Emergency Urgent Care Flows (EUCC).
4. Commissioning cycle support for grouping and re-costing previous activity.
5. Enables monitoring of:
a. CCG outcome indicators.
b. Non-financial validation of activity.
c. Successful delivery of integrated care within the CCG.
d. Checking frequent or multiple attendances to improve early intervention and avoid admissions.
e. Case management.
f. Care service planning.
g. Commissioning and performance management.
h. 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.

Outputs:

Risk Stratification
1. 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 risk stratification presents pseudonymised data to the GPs. GPs are able to re-identify information only for their own patients for the purpose of direct care.
2. Output from the risk stratification tool will provide aggregate reporting of number and percentage of population found to be at risk.
3. Record level output will be available for commissioners pseudonymised at patient level and aggregated reports.
Pseudonymised – SUS and Local Flows
1. Commissioner reporting:
a. Summary by provider view - plan & actuals year to date (YTD).
b. Summary by Patient Outcome Data (POD) view - plan & actuals YTD.
c. Summary by provider view - activity & finance variance by POD.
d. Planned care by provider view - activity & finance plan & actuals YTD.
e. Planned care by POD view - activity plan & actuals YTD.
f. Provider reporting.
g. Statutory returns.
h. Statutory returns - monthly activity return.
i. Statutory returns - quarterly activity return.
j. Delayed discharges.
k. Quality & performance referral to treatment reporting.
2. Readmissions analysis.
3. Production of aggregate reports for CCG Business Intelligence.
4. Production of project / programme level dashboards.
5. Monitoring of acute / community / mental health quality matrix.
6. Clinical coding reviews / audits.
7. Budget reporting down to individual GP Practice level.
8. GP Practice level dashboard reports include high flyers.

Pseudonymised – Mental Health, Maternity, IAPT, CYPHS and DIDS
1. Commissioner reporting:
a. Summary by provider view - plan & actuals year to date (YTD).
b. Summary by Patient Outcome Data (POD) view - plan & actuals YTD.
c. Summary by provider view - activity & finance variance by POD.
d. Planned care by provider view - activity & finance plan & actuals YTD.
e. Planned care by POD view - activity plan & actuals YTD.
f. Provider reporting.
g. Statutory returns.
h. Statutory returns - monthly activity return.
i. Statutory returns - quarterly activity return.
j. Delayed discharges.
k. Quality & performance referral to treatment reporting.
2. Readmissions analysis.
3. Production of aggregate reports for CCG Business Intelligence.
4. Production of project / programme level dashboards.
5. Monitoring of mental health quality matrix.
6. Clinical coding reviews / audits.
7. Budget reporting down to individual GP Practice level.
8. GP Practice level dashboard reports include high flyers.

Processing:

Prior to the release of identifiable data by North West DSCRO, Type 2 objections will be applied and the relevant patient’s data redacted.

Risk Stratification
1. SUS Data is sent from the SUS Repository to North West Data Services for Commissioners Regional Office (DSCRO) to the data processor.
2. SUS data identifiable at the level of NHS number regarding hospital admissions, A&E attendances and outpatient attendances is delivered securely from North West DSCRO to the data processor.
3. Data quality management and standardisation of data is completed by North West 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.
4. Identifiable GP Data is securely sent from the GP system to Arden & GEM CSU.
5. SUS data is linked to GP data in the risk stratification tool by the data processor.
6. Arden & GEM 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.
7. Once Arden & GEM CSU has completed the processing, the data is passed to the CCG in pseudonymised form at patient level and as aggregated reports.

Pseudonymised – SUS and Local Flows
Data Processor 2 – GMSS (via DP1):
1. North West Data Services for Commissioners Regional Office (DSCRO) receives a flow of SUS identifiable data for the CCG from the SUS Repository. North West DSCRO also receives identifiable local provider data for the CCG directly from Providers.
2. Data quality management and pseudonymisation of data is completed by North West DSCRO and the pseudonymised data is then passed securely to Arden & GEM CSU for the addition of derived fields, linkage of data sets and analysis.
3. Arden & GEM CSU then passes the pseudonymised data securely to the Greater Manchester Shared Services (GMSS).
4. GMSS analyse the data to see patient journeys for pathway or service design, re-design and de-commissioning.
5. GMSS then pass the processed pseudonymised data to 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 in line with the HES analysis guide.
Data Processor 4 – AQuA (via DP1):
1. North West Data Services for Commissioners Regional Office (DSCRO) receives a flow of SUS identifiable data for the CCG from the SUS Repository. North West DSCRO also receives identifiable local provider data for the CCG directly from Providers.
2. Data quality management and pseudonymisation of data is completed by North West DSCRO and the pseudonymised data is then passed securely to Arden & GEM CSU for the addition of derived fields, linkage of data sets and analysis.
3. Arden & GEM CSU then passes the pseudonymised data securely to AQuA to provide support for a range of quality improvement programmes including the NW Advancing Quality Programme. AQuA identifies cohorts of patients within specific disease groups for further analysis to help drive quality improvements across the region.
4. AQuA produces aggregate reports only with small number suppression in line with the HES analysis guide. Only aggregate reports are sent to the CCG.

Data Processor 5 – Academic Health Sciences Network (Utilisation Management Team) (SUS Only) (via DP1)::
1. North West Data Services for Commissioners Regional Office (DSCRO) receives a flow of SUS identifiable data for the CCG from the SUS Repository.
2. Data quality management and pseudonymisation of data is completed by North West DSCRO and the pseudonymised data is then passed securely to Arden & GEM CSU for the addition of derived fields, linkage of data and analysis.
3. Arden & GEM CSU then passes the pseudonymised data securely to the Academic Health Service (Utilisation Management Team) (AHSN UMT)
4. The AHSN UMT receive pseudonymised SUS data for Greater Manchester patients. They 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. AHSN UMT produces aggregate reports only with small number suppression in line with the HES analysis guide. Only aggregate reports are sent to the CCG.
NHS Bury CCG, NHS Heywood, Middleton and Rochdale CCG, NHS North Manchester CCG and NHS Oldham CCG have a collaborative information sharing agreement in place to share pseudonymised SLAM and SLAM Backup data between these CCGs only. SLAM data is included under Local Flows and is available under the Health and Social Care Act 2012.
Pseudonymised – Mental Health and IAPT
Data Processor 1 – Arden & GEM CSU
1. North West Data Services for Commissioners Regional Office (DSCRO) receives a flow of data identifiable at the level of NHS number for Mental Health (MHSDS, MHMDS, MHLDDS) and MSDS. North West DSCRO also receive a flow of pseudonymised patient level data for each CCG for Improving Access to Psychological Therapies (IAPT), Child and Young People’s Health (CYPHS) and Diagnostic Imaging (DIDS) for commissioning purposes
1. Data quality management and pseudonymisation of data is completed by North West DSCRO and the pseudonymised data is then passed securely to Arden & GEM CSU for the addition of derived fields, linkage of data sets and analysis.
2. Arden & GEM 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.
3. The CCG analyses the data to see patient journeys for pathway or service design, re-design and de-commissioning
4. Aggregation of required data for CCG management use can be completed by the CSU or the CCG
5. Patient level data will not be shared outside of the CCG and will only be shared within the CCG on a need to know basis, as per the purposes stipulated within the Data Sharing Agreement. External aggregated reports only with small number suppression in line with the HES analysis guide.
Data Processor 2 – GMSS (via DP1):
Greater Manchester Shared Services (GMSS) have taken BI services in house and are now hosted by Oldham CCG. AGEM CSU flow data to a small team within GMSS. Access to the data is restricted to this team who access and manage the data. These BI services were previously provided by North West CSU.

GMSS deliver a range of services including;
• effective use of resources;
• data quality;
• information governance;
• market management;
• provider contract & performance management;

To enable GMSS to support these services a team within the GMSS have controlled access to SUS data at a pseudonymised level. Access to the data is controlled by AGEM CSU using users’ roles to ensure only appropriate users gain access to pseudonymised data. Data can then be used for reporting to support the range of services being offered to CCGs, and CCGs receive aggregate level reports from GMSS. GMSS staff are separate from Oldham CCG staff and accordingly have separate functions and roles.
1. North West Data Services for Commissioners Regional Office (DSCRO) receives a flow of data identifiable at the level of NHS number for Mental Health (MHSDS, MHMDS, MHLDDS) North West DSCRO also receive a flow of pseudonymised patient level data for each CCG for Improving Access to Psychological Therapies (IAPT) for commissioning purposes
2. The pseudonymised data is securely transferred from North West DSCRO to Arden & GEM CSU for the addition of derived fields, linkage of data sets and analysis.
3. Arden & GEM CSU then pass the processed, pseudonymised and linked data to the Greater Manchester Shared Services (GMSS)
4. GMSS analyse and conduct the BI function and then send the Pseudonymised data to the CCG.
5. Patient level data will not be shared outside of the CCG and will only be shared within the CCG on a need to know basis, as per the purposes stipulated within the Data Sharing Agreement. External aggregated reports only with small number suppression.
Data Processor 4 - Advancing Quality Alliance (AQuA) (via DP1):
1. North West Data Services for Commissioners Regional Office (DSCRO) receives a flow of data identifiable at the level of NHS number for Mental Health (MHSDS, MHMDS, MHLDDS).
2. Data quality management and pseudonymisation of data is completed by North West DSCRO and the pseudonymised data is then passed securely to Arden & GEM CSU for the addition of derived fields, linkage of data sets and analysis.
3. Arden & GEM CSU then passes the pseudonymised data securely to Advancing Quality Alliance (AQuA).
4. AQuA receives pseudonymised SUS data for Greater Manchester patients. They 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. AQuA produces aggregate reports only with small number suppression in line with the HES analysis guide. Only aggregate reports are sent to the CCG.