NHS Digital Data Release Register - reformatted

NHS Liverpool CCG projects

• DSfC - NHS Liverpool CCG; RS & IV
• DSfC - CIPHA - CV19
• GDPPR COVID-19 – CCG - Pseudo
• DSfC - NHS Liverpool CCG, Comm.
• DSfC - NHS Liverpool CCG; RS, IV, Comm.
• Project 6
• Project 7

1889 data files in total were disseminated unsafely (information about files used safely is missing for TRE/"system access" projects).

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🚩 NHS Liverpool CCG was sent multiple files from the same dataset, in the same month, both with optouts respected and with optouts ignored. NHS Liverpool 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 Liverpool CCG; RS & IV — DARS-NIC-525778-F2K8G

Type of data: information not disclosed for TRE projects

Opt outs honoured: Identifiable (Section 251 NHS Act 2006)

Legal basis: 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 2021-07-15 — 2024-07-14

Access method: Frequent Adhoc Flow

Data-controller type: NHS LIVERPOOL CCG, NHS CHESHIRE AND MERSEYSIDE ICB - 99A

Sublicensing allowed: No

Datasets:

1. 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 the CCG is 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 (data from providers) and will not be used further.

The CCG are advised by the appointed CEfF whether payment for invoices can be made or not.

Invoice Validation will be conducted by NHS Liverpool CCG and Liaison Financial Services Ltd.

Liaison Financial Services Ltd conduct an independent ad-hoc review on retrospective payments made. Investing resource, skills and experience into deeper reconciliation, this identifies overcharges already paid and recovers savings for the CCG that would otherwise be lost.

RISK STRATIFICATION
Risk stratification is a tool for identifying and predicting which patients are at high risk (of health deterioration and using multiple services) 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 both individual and groups of vulnerable patients. Commissioners can then prepare plans for patients who may require high levels of care. Risk Stratification also enables General Practitioners (GPs) to better target intervention in Primary Care.

Risk Stratification will be conducted by
- Arden & GEM Commissioning Support Unit who apply risk stratification algorithms to data to produce outputs to present data to the CCG.
- Midlands & Lancashire Commissioning Support Unit who apply business intelligence methods to outputs and present data to the CCG.
- Cloud 2 Limited. A company procured via the G Cloud 11 framework in order to build reporting capability for Microsoft Power BI. Cloud 2 will receive pseudonymised data only in order to create Power BI reports for the CCG and GPs.

Arden GEM CSU receive identifiable SUS and GP data as a Risk Stratification Supplier, link the datasets together at patient level and run the data through a Risk Stratification algorithm, that produces a Risk Stratification output. At a minimum this is the patient identifier and a risk score, though typically includes some further details such as additional flags to help segment/risk stratify the data. AGEM provide this output to the CCG in pseudonymised form. This is provided as part of a data management service, so providing the data in a format that is ready for input into databases/data warehouses suitable for further analysis. AGEM can also supply the data to the GP practices (for their own patients only).

Arden GEM CSU pass the Risk Stratification outputs to Midlands and Lancashire CSU, who receive the data in identifiable format (also as a Risk Stratification supplier). They load this information into their BI tool which is made available to the GP practices. The BI tool allows the GPs to view their own data only, in different formats, and allows easy mechanisms for analysing and displaying the data. GPs can re-identify/view identifiable data for their own patients, for direct care purpose. The CCG can also access the RS outputs through the BI tool, but in pseudonymised form.

Cloud 2 Limited are a digital workplace specialist and a Microsoft Gold Partner, specialising in analytics and data, with a focus on Power BI. Such expertise will facilitate reporting that will enhance the intelligence used to inform business decisions, ensure financial stability, and better outcomes for the population. In order to produce these reports, Cloud 2 Limited are required to access the data held under this agreement. They will access the data only for this purpose and using the data held under this agreement for any other purpose would be considered a breach of the agreement.

Midlands and Lancashire Commissioning Support Unit provide the current BI tool, but the CCG requires a more agile system. In order to do this, they have commissioned Cloud2 to create this report. Although the 2 BI tools will offer similar information, the tool developed by Cloud2 will be more tailed to the CCGs needs. The intention is that once the report is fully created, the BI tool provided by Midlands and Lancashire Commissioning Support Unit will no longer be used and would be removed from the DSA. Ultimately, one BI tool will be sufficient, but the CCG requires a period of dual running of the reports until they have fully developed and integrated the Cloud2 report.

Yielded Benefits:

Expected Benefits:

INVOICE VALIDATION
The invoice validation process supports the ongoing delivery of patient care across the NHS and the CCG region by:
1. Ensuring that activity is fully financially validated.
2. Ensuring that service providers are accurately paid for the patients treatment.
3. Enabling services to be planned, commissioned, managed, and subjected to financial control.
4. Enabling commissioners to confirm that they are paying appropriately for treatment of patients for whom they are responsible.
5. Fulfilling commissioners duties to fiscal probity and scrutiny.
6. Ensuring full financial accountability for relevant organisations.
7. Ensuring robust commissioning and performance management.
8. Ensuring commissioning objectives do not compromise patient confidentiality.
9. Ensuring the avoidance of misappropriation of public funds.

INVOICE VALIDATION – Liaison Financial Services Ltd
1. Financial validation of activity
2. CCG Budget control
3. Assurances over the robustness of internal control mechanisms relating to the payment of invoices and/or suggested improvements
4. Identification and recovery of monies which would otherwise be lost
5. Meeting commissioning objectives without compromising patient confidentiality
6. The avoidance of misappropriation of public funds to ensure the ongoing delivery of patient care
7. Benefit delivered 3-9 months from receiving data, depending on number of claims to investigate and resolve

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 and health outcomes through more effective commissioning of services.

Outputs:

INVOICE VALIDATION
1. The Controlled Environment for Finance (CEfF) will enable the CCG to challenge invoices and raise discrepancies and disputes.
2. Outputs from the CEfF will enable accurate production of budget reports, which will:
a. Assist in addressing poor quality data issues
b. Assist in business intelligence
3. Validation of invoices for non-contracted events where a service delivered to a patient by a provider that does not have a written contract with the patient’s responsible commissioner, but does have a written contract with another NHS commissioner/s.
4. Budget control of the CCG.

INVOICE VALIDATION – Liaison Financial Services Ltd
1. Validation of Continuing Healthcare related invoices and payments
2. Independent Identification of potential overpayments made by the CCG through invoice validation
3. Liaising with providers with a view to recouping these monies
4. Review is completed for the retrospective period from date of contract with Liaison Financial Services back to 01/04/2013.
5. Reviews take 3-9 months depending on number of claims to investigate and resolve
6. Liaison Financial Services would repeat the exercise 2-3 years later
7. CCGs could request reviews to be done more frequently
8. SUS+ would only be requested each time a review was completed, and could be requested at different times as independent reviews

RISK STRATIFICATION
1. As part of the risk stratification processing activity detailed above, GPs have access to the risk stratification tool which highlights patients for whom the GP is responsible and have been classed as at risk. The only identifier available to GPs is the NHS numbers of their own patients. Any further identification of the patients will be completed by the GP on their own systems.
2. GP Practices will be able to view the risk scores for individual patients with the ability to display the underlying SUS+ data for the individual patients when it is required for direct care purposes by someone who has a legitimate relationship with the patient.

CCGs will be able to:
3. Target specific vulnerable patient groups and enable clinicians with the duty of care for the patient to offer appropriate interventions.
4. Reduce hospital readmissions and targeting clinical interventions to high risk patients.
5. Identify patients at risk of deterioration and providing effective care.
6. Reduce in the difference in the quality of care between those with the best and worst outcomes.
7. Re-design care to reduce admissions.
8. Set up capitated budgets – budgets based on care provided to the specific population.
9. Identify health determinants of risk of admission to hospital, or other adverse care outcomes.
10. Monitor vulnerable groups of patients including but not limited to frailty, COPD, Diabetes, elderly.
11. Health needs assessments – identifying numbers of patients with specific health conditions or combination of conditions.
12. Classify vulnerable groups based on: disease profiles; conditions currently being treated; current service use; pharmacy use and risk of future overall cost.
13. Production of Theographs – a visual timeline of a patients encounters with hospital providers.
14. Analyse based on specific diseases
In addition:
- The risk stratification tool will provide aggregate reporting of number and percentage of population found to be at risk.
- Record level output (pseudonymised) will be available for commissioners (of the CCG), pseudonymised at patient level. Onward sharing of this data is not permitted.

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DSfC - CIPHA - CV19 — DARS-NIC-396095-H1P1D

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

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

Sensitive: Sensitive

When:DSA runs 2020-10-13 — 2021-03-31 2021.01 — 2021.05.

Access method: One-Off, Frequent Adhoc Flow

Data-controller type: CHESHIRE EAST COUNCIL, CHESHIRE WEST AND CHESTER COUNCIL, HALTON BOROUGH COUNCIL, KNOWSLEY METROPOLITAN BOROUGH COUNCIL, LIVERPOOL CITY COUNCIL, NHS CHESHIRE CCG, NHS HALTON CCG, NHS KNOWSLEY CCG, NHS LIVERPOOL CCG, NHS SOUTH SEFTON CCG, NHS SOUTHPORT AND FORMBY CCG, NHS ST HELENS CCG, NHS WARRINGTON CCG, NHS WIRRAL CCG, SEFTON METROPOLITAN BOROUGH COUNCIL, ST HELENS COUNCIL, WARRINGTON BOROUGH COUNCIL, WIRRAL METROPOLITAN BOROUGH COUNCIL - PUBLIC HEALTH, CHESHIRE EAST COUNCIL, CHESHIRE WEST AND CHESTER COUNCIL, HALTON BOROUGH COUNCIL, KNOWSLEY METROPOLITAN BOROUGH COUNCIL, LIVERPOOL CITY COUNCIL, NHS CHESHIRE AND MERSEYSIDE ICB - 01F, NHS CHESHIRE AND MERSEYSIDE ICB - 01J, NHS CHESHIRE AND MERSEYSIDE ICB - 01T, NHS CHESHIRE AND MERSEYSIDE ICB - 01V, NHS CHESHIRE AND MERSEYSIDE ICB - 01X, NHS CHESHIRE AND MERSEYSIDE ICB - 02E, NHS CHESHIRE AND MERSEYSIDE ICB - 12F, NHS CHESHIRE AND MERSEYSIDE ICB - 27D, NHS CHESHIRE AND MERSEYSIDE ICB - 99A, SEFTON METROPOLITAN BOROUGH COUNCIL, ST HELENS COUNCIL, WARRINGTON BOROUGH COUNCIL, WIRRAL METROPOLITAN BOROUGH COUNCIL - PUBLIC HEALTH, CHESHIRE EAST COUNCIL, CHESHIRE WEST AND CHESTER COUNCIL, HALTON BOROUGH COUNCIL, KNOWSLEY METROPOLITAN BOROUGH COUNCIL, LIVERPOOL CITY COUNCIL, NHS CHESHIRE AND MERSEYSIDE ICB - 01F, NHS CHESHIRE AND MERSEYSIDE ICB - 01J, NHS CHESHIRE AND MERSEYSIDE ICB - 01T, NHS CHESHIRE AND MERSEYSIDE ICB - 01V, NHS CHESHIRE AND MERSEYSIDE ICB - 01X, NHS CHESHIRE AND MERSEYSIDE ICB - 02E, NHS CHESHIRE AND MERSEYSIDE ICB - 12F, NHS CHESHIRE AND MERSEYSIDE ICB - 27D, NHS CHESHIRE AND MERSEYSIDE ICB - 99A, SEFTON METROPOLITAN BOROUGH COUNCIL, ST HELENS COUNCIL, WARRINGTON BOROUGH COUNCIL, WIRRAL BOROUGH COUNCIL

Sublicensing allowed: No

Datasets:

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

Objectives:

The overarching purpose for this agreement is to support a set of COVID related population health analytics designed to inform both population level planning for COVID recovery and to support the targeting of direct care to vulnerable populations across the Cheshire & Merseyside Sustainable Transformation Partnership (C&MSTP).

Data released will only be shared with those parties listed and will only be used for COVID-19 purposes as laid out in this agreement. Any non-COVID-19 purpose and any other COVID-19 purpose, except as set out in this agreement, is excluded.

Although CMSTP is made up of over 450 organisations (including more than 350 GP practices) for the purposes of this Data Sharing Agreements it has been determined that the 8 Clinical Commissioning Groups and 8 Local Authorities are the Joint Data Controllers. These organisations form the membership of the Data Access and Asset Group within the STP and so for the purpose of this agreement are deemed to be Joint Data Controllers.

The COVID related population health analytics will be achieved via analysis of the data requested in this agreement and the data sets listed in the Processing Activities section producing pseudonymised data for place-based local intelligence services. The proposal is to make a set of person level pseudonymised data available to the local placed based intelligence teams that being the CCG and Local Authorities. This will enable them to support the local system including the COVID recovery cells, public health teams, Hospital and Out of Hospital cells across the Cheshire and Merseyside patch as well as local with COVID planning, which includes support to General Practice and PCN’s in intelligence required.

A further example of analysis is a set of automated dashboards in the areas of COVID sit rep reporting, Capacity and Demand, Epidemiology, and Population Stratification.

These dashboards are required for:

Dashboard 1: COVID sit rep reporting: this suite of reports will show the daily situation of COVID in Cheshire and Mersey to aid the monitoring and management of outbreaks and incidents including cases, mortality, hospital admissions, testing and outbreaks. It’s audience is system wide including local Recovery Cells and Hospital and Out of Hospital cells, all designated by government to locally manage the pandemic. It will enable them to have the same up to date information on the spread and challenges in the pandemic so they can make informed population management decisions.

Dashboard 2 Capacity and Demand: This dashboard will assist in monitoring and managing demand across acute, community, mental health and local authority services in as near real time as possible, including the ability to understand if there are possible surges or subsequent ‘waves’ emerging by recognising changing trends. It will enable understanding of whether there is enough capacity to meet that demand and allow the Cells and STO described above to take informed actions to control and prevent the spread of COVID. This dashboard is targeted at those user groups that are responsible for planning system capacity including Cheshire and Merseyside region, sub regional teams i.e. North Mersey, individual CCG’s and Primary Care Networks.

Dashboard 3 Epidemiology: This dashboard will enable monitoring and recognition of trends in mortality and incidence over time at differing levels of geography. It will also provide insight in terms of demographic and health characteristics of individuals most affected by COVID. It will enable identification of geographical outbreaks or ‘hot spots’ of emerging infection. This dashboard is aimed at Public Health departments; The Out of Hospital and Acute Recovery Cells at Cheshire and Mersey Region that are responsible for planning and those responsible for planning a COVID response with PCN’s.

Dashboard 4 Population Stratification: This Dashboard enables GP practices to monitor and manage outbreaks and assist in controlling and preventing the spread of COVID by identifying individuals with certain characteristics that will be vulnerable to adverse outcomes as a result of COVID and target services/interventions appropriately to control and prevent the spread of COVID. (This is not the same as risk stratification in general practice that is most commonly used for stratifying the risk of unplanned admission to acute care)

The outputs of Dashboard 4 may also be used for Direct Care Purposes.


The 9 CCGs involved in this agreement also have a an existing agreement for commissioning purposes (NIC-140059)

Expected Benefits:

Benefits at Cheshire and Mersey Region, CCG and Local Authority level

The Cheshire and Merseyside Health Care partnership (C&M HCP) is made up of nine local authority areas (or “places”). The C&M HCP is responsible for planning enough system capacity to respond to any surges in demand due the COVID pandemic, whilst also reintroducing planned care capacity cross both acute, community and mental health providers. Demand and capacity reports will enable the C&M HCP and the nine places (eg Cheshire East, Liverpool, Warrington etc) to be sighted on system demand and respond with capacity accordingly.

• Implementation of the system will allow the production of bespoke real-time dashboards to present data in easy to understand visual displays that can then prompt further detailed analysis depending on issues highlighted as being of concern across Cheshire & Merseyside.
Available once data flows.

• C&M Covid dashboards including cases rates, testing rates and system metrics such as hospital admissions and local mortality rates will enable local health care systems within the C&M footprint to understand the current Covid situation and respond with appropriate policy.
Available within 3-6 months.

• An epidemiology dashboard will allow place-based decision makers to identify the population characteristics of people presenting for tests, cases and mortality to better understand the nature of the pandemic and identify if there are particular cohorts that are consuming testing at a greater rate than others to help manage testing capacity.
Available within 3-6 months.

• A specific testing dashboard will help place-based decision makers to identify geographical Covid hot spots and drill down to identify patterns and/or vulnerable cohorts for management. This may allow more targeted intervention at place-based or more local geographies.
Available within 3-6 months.

• Real-time detailed capacity and demand reporting across all sectors including hospital and out of hospital services will enable accurate reporting as stipulated in NHS England (NHSE) phase three planning guidance and it is hoped allow place-based decision makers to better manage demand and capacity.
Available within 3-6 months.

• The system will provide the data to enable the C&M HCP and place-based NHS organisations to follow the NHSE planning guidance which includes the following:

“Restore NHS services inclusively, so that they are used by those in greatest need. This will be guided by new, core performance monitoring of service use and outcomes among those from the most deprived neighbourhoods and from Black and Asian communities, by 31 October 2020.

Ensure datasets are complete and timely, to underpin an understanding of and response to inequalities. All NHS organisations should proactively review and ensure the completeness of patient ethnicity data by no later than 31 December 2020, with general practice prioritising those groups at significant risk of COVID-19 from 1 September 2020.

Collaborate locally in planning and delivering action to address health inequalities, including incorporating in plans for restoring critical services by 21 September; better listening to communities and strengthening local accountability; deepening partnerships with local authorities and the voluntary and community sector; and maintaining a continual focus on implementation of these actions, resources and impact, including a full report by 31 March 2021.”

https://www.england.nhs.uk/publication/implementing-phase-3-of-the-nhs-response-to-the-covid-19-pandemic/


What are the benefits for GP Practices and Primary Care Networks (PCNs)?


• Identification of vulnerable groups will give Practices and PCNs a better understanding of vulnerable populations and their needs. It is hoped that services can then be planned and targeted more appropriately.
Available within 3-6 months.

• Analysis of the characteristics of people affected by COVID in their populations and identification of local hotspots may allow targeted interventions based on specific risk factors, for example local shielding advice, patient remote monitoring and specific treatment interventions (such as early use of steroids in selected patients in community). This may lead to reduced morbidity and mortality and so better patient outcomes.
Available within 3-6 months.


What are the benefits for patients?

• It is hoped that the detailed data analysis will enable more effective targeted support and/or interventions from services that are available, to at risk individuals.
Available within 3-6 months.

• Local commissioners may identify service delivery gaps that could be addressed locally by re-organising current service provision or by commissioning additional service provision.
Available within 3-6 months.

• It is hoped the above will result in improved patient well-being and a reduction in morbidity and mortality associated with Covid infection.
Available over the course of the pandemic, may be seen within 3-6 months.

Outputs:

COVID National & Sitrep
This report shows COVID cases, mortality and admissions. It compares trends
over time and compares rates by different geographies and providers.


COVID Out of Hospital Capacity & Demand
This report shows capacity and demand for hospital, ready for discharge,
care homes, domiciliary care, mental health and community providers.


COVID Testing Report
This report shows numbers, rates, positivity rates of those testing positive.
Also homes, schools and organisations that have had an outbreak against
The national definitions.


COVID in Hospital Demand Prediction Tool (Manchester Model)
This report provides predicting short term, in hospital COVID bed occupancy
split by core and ICU beds.


Enhanced case Finder
This report enables the ability to identify vulnerable cohorts of the
population including those advised to shield, with the functionality to drill
down to identify patients for targeting of direct care.


COVID Epidemiology
This report shows stratified cases, suspected cases, mortality and people
receiving tests by characteristics including age, condition, deprivation and BAME,
and over time.

Processing:

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

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

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

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

Patient level data will not be linked other than as specifically detailed within this Data Sharing Agreement.

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 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:
Patient level data will not be shared outside of the Data Controllers / Data Processors unless it is for the purpose of Direct Care, where it may be shared only with those health professionals who have a legitimate relationship with the patient and a legitimate reason to access the data.

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

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

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. This also includes the purpose on which they would be applied -

For the purpose of Commissioning:
• Patients who are normally registered and/or resident within the CCG regions (including historical activity where the patient was previously registered or resident in another commissioner).
and/or
• Patients treated by a provider where the CCG are 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 CCG - this is only for commissioning and relates to both national and local flows.

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

Data quality management and pseudonymisation is completed within the DSCRO and is then disseminated as follows:

Data Processor 1 – Graphnet Health Ltd
1. SUS+, Local Provider data, Mental Health data (MHSDS, MHMDS, MHLDDS), Maternity data (MSDS), Improving Access to Psychological Therapies data (IAPT), Child and Young People’s Health data (CYPHS), Community Services Data Set (CSDS), Diagnostic Imaging data (DIDS), National Cancer Waiting Times Monitoring Data Set (CWT), Civil Registries Data (CRD) (Births and Deaths), National Diabetes Audit (NDA) Patient Reported Outcome Measures (PROMs) and Shielded Patient List (SPL) data only is pseudonymised using the Nottingham Open Pseudonymiser tool in the DSCRO with a specific SALT key for this project. The pseudonymised data is then securely transferred to Arden and GEM Commissioning Support Unit.
2. Arden and GEM Commissioning Support unit add derived fields by using existing data and then transfer the data to Graphnet Health Ltd
3. Graphnet Health Ltd also receive data directly from providers (see points i - v for details)
4. Graphnet Health Ltd link data and provide analysis
5. Allowed linkage is between the data sets contained within point 1 and 3.
6. Graphnet Health Ltd then pass the processed, pseudonymised and linked data to the Data Controllers.
7. Patient level data will not be shared outside of the Data Controllers / Data Processors unless it is for the purpose of direct care and will only be shared within 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.

Data From Providers
i. Graphnet Health Ltd receives the following identifiable datasets directly from providers:
Acute HL7
Mental Health
Community
Primary Care
Ambulance 111
Ambulance 999
SDRA Spine
Social Care
Pathology
NPEX
Testing Data
ii. The identifiable data is landed in a segregated section of Graphnet Health Ltd with strict access controls
iii. The data is pseudonymised using the Nottingham Open Pseudonymiser tool and SALT key specific for this project that has been shared via the DSCRO
iv. The pseudonymised data is then sent to Graphnet Healthcare Ltd main system for linkage to other datasets
v. The identifiable data is then deleted from the segregated area

For clarity, the reidentification of individuals for GP Direct Care purposes is carried out by the DSCRO.

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GDPPR COVID-19 – CCG - Pseudo — DARS-NIC-388922-L1S0K

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 2020-09-01 — 2021-03-31 2021.01 — 2021.05.

Access method: One-Off, Frequent Adhoc Flow

Data-controller type: NHS CHESHIRE CCG, NHS HALTON CCG, NHS KNOWSLEY CCG, NHS LIVERPOOL CCG, NHS SOUTH SEFTON CCG, NHS SOUTHPORT AND FORMBY CCG, NHS ST HELENS CCG, NHS WARRINGTON CCG, NHS WIRRAL CCG, NHS CHESHIRE AND MERSEYSIDE ICB - 01F, NHS CHESHIRE AND MERSEYSIDE ICB - 01J, NHS CHESHIRE AND MERSEYSIDE ICB - 01T, NHS CHESHIRE AND MERSEYSIDE ICB - 01V, NHS CHESHIRE AND MERSEYSIDE ICB - 01X, NHS CHESHIRE AND MERSEYSIDE ICB - 02E, NHS CHESHIRE AND MERSEYSIDE ICB - 12F, NHS CHESHIRE AND MERSEYSIDE ICB - 27D, NHS CHESHIRE AND MERSEYSIDE ICB - 99A

Sublicensing allowed: No

Datasets:

1. GPES Data for Pandemic Planning and Research (COVID-19)
2. 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).

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DSfC - NHS Liverpool CCG, Comm. — DARS-NIC-113980-R8Z8K

Type of data: information not disclosed for TRE projects

Opt outs honoured: No - data flow is not identifiable, Anonymised - ICO Code Compliant (Section 251, Does not include the flow of confidential data)

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

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

Sensitive: Sensitive

When:DSA runs 2018-12-07 — 2021-12-06 2018.06 — 2021.05.

Access method: Frequent adhoc flow, Frequent Adhoc Flow, One-Off

Data-controller type: NHS LIVERPOOL CCG, NHS CHESHIRE AND MERSEYSIDE ICB - 99A

Sublicensing allowed: No

Datasets:

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

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 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)
§ Adult Social Care
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
§ Monitoring, at a population level, particular cohorts of service users and designing analytical models which support more effective interventions in health and Adult Social Care
§ Monitoring service and integrated care outcomes across a pathway or care setting involving Adult Social Care
§ Developing, through evaluation, more effective interventions across a pathway or care setting involving Adult Social Care
§ Designing and implementing new payment models across health and Adult Social Care
§ Understanding current and future population needs and resource utilisation for local strategic planning purposes.
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.

Yielded Benefits:

1. Monitoring In year projects 2. Learning from and predicting likely patient pathways for certain conditions, in order to influence early interventions and other treatments for patients 3. Successful delivery of integrated care within the CCG. 4. Better understanding of the health of and the variations in health outcomes within the population to help understand local population characteristics. 5. 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 6. Insights into patient outcomes, and identification of the possible efficacy of outcomes-based contracting opportunities.

Expected Benefits:

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

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
13. Production of aggregate reports for CCG Business Intelligence including:-
a. Understanding current and future population needs and resource utilisation
b. Tracking outcomes across pathways, and meeting outcome targets
c. Evaluation of interventions across pathways
d. Designing and implementing new payment models
e. More sophisticated risk stratification and predictive analytics
f. Demand management
14. Production of project / programme level dashboards

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.

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.
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

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)
10. Adult Social Care
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), Diagnostic Imaging data (DIDS) and Adult Social Care 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 derived fields, link data and provide analysis to:
o See patient journeys for pathways or service design, re-design and de-commissioning.
o Check recorded activity against contracts or invoices and facilitate discussions with providers.
o Undertake population health management
o Undertake data quality and validation checks
o Thoroughly investigate the needs of the population
o Understand cohorts of residents who are at risk
o Conduct Health Needs Assessments
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.
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.

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DSfC - NHS Liverpool CCG; RS, IV, Comm. — DARS-NIC-47191-D9X6J

Type of data: information not disclosed for TRE projects

Opt outs honoured: N, Y, No - data flow is not identifiable, Yes - patient objections upheld, Anonymised - ICO Code Compliant, Identifiable (Does not include the flow of confidential data, Section 251, Mixed, Mixture of confidential data flow(s) with consent and flow(s) with support under 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), Section 251 approval is in place for the flow of identifiable data, National Health Service Act 2006 - s251 - 'Control of patient information'. , 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(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 2019-01-01 — 2021-12-31 2018.06 — 2021.05.

Access method: Frequent adhoc flow, Frequent Adhoc Flow, One-Off

Data-controller type: NHS LIVERPOOL CCG, NHS CHESHIRE AND MERSEYSIDE ICB - 99A, LIVERPOOL CITY COUNCIL, NHS CHESHIRE AND MERSEYSIDE ICB - 99A

Sublicensing allowed: No

Datasets:

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

Objectives:

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 Liverpool CCG

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 a forecast of future demand by identifying high risk 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.
The legal basis for this to occur is under Section 251 of NHS Act 2006 (CAG 7-04(a)).
Risk Stratification will be conducted by Arden & GEM CSU & Midlands & Lancashire CSU

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)
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

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 Arden & GEM CSU
- NHS Midlands & Lancashire CSU
- Salford Royal Trust (Hosting AQuA and AHSN)
- Hartree Foundation
- University of Liverpool

Yielded Benefits:

N/A

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.

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)
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 Liverpool CCG

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


Risk Stratification - Data Processor – Arden & GEM CSU

1. Identifiable SUS+ data is obtained from the SUS Repository to the Data Services for Commissioners Regional Office (DSCRO).
2. Data quality management and standardisation of data is completed by the DSCRO and the data identifiable at the level of NHS number is transferred securely to Arden & GEM CSU, who hold the SUS+ data within the secure Data Centre on N3.
3. Identifiable GP Data is securely sent from the GP system to Arden & GEM CSU.
4. SUS+ data is linked to GP data in the risk stratification tool by the data processor.
5. As part of the risk stratification processing activity, GPs have access to the risk stratification tool within the data processor, which highlights patients with whom the GP has a legitimate relationship and have been classed as at risk. The only identifier available to GPs is the NHS numbers of their own patients. Any further identification of the patients will be completed by the GP on their own systems.
6. Once Arden & GEM CSU has completed the processing, the CCG can access the online system via a secure connection to access the data aggregate with small number suppression.

Risk Stratification - Data Processor – Midlands & Lancashire CSU

1. Identifiable SUS+ data is obtained from the SUS Repository to the Data Services for Commissioners Regional Office (DSCRO).
2. Data quality management and standardisation of data is completed by the DSCRO and the data identifiable at the level of NHS number is transferred securely to Arden & GEM CSU, who hold the SUS+ data within the secure Data Centre on N3.
3. The data is transferred from Arden & GEM CSU securely to Midlands & Lancashire CSU who apply the data to the BI tool to analyse and produce reports.
4. Once Midlands & Lancashire CSU has completed the processing, the CCG can access the online system via a secure N3 connection to access the data pseudonymised at patient level and aggregated reports.
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.


Commissioning

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

Data Processor - Arden & GEM CSU

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

Data Processor – Hartree Centre: Science and Technology Research

1) Pseudonymised SUS+, only is securely transferred from the DSCRO to Hartree Centre: Science and Technology Research.
2) Hartree Centre: Science and Technology Research add derived fields and provide analysis.
3) Hartree Centre: Science and Technology Research then pass the processed, pseudonymised and linked data to the CCG.
4) Aggregation of required data for CCG management use will be completed by Hartree Centre: Science and Technology Research or the CCG as instructed by the CCG.
5) Patient level data will not be shared outside of the CCG and will only be shared within the CCG on a need to know basis, as per the purposes stipulated within the Data Sharing Agreement. External aggregated reports only with small number suppression can be shared.

Data Processor – University of Liverpool

1) Pseudonymised SUS+, only is securely transferred from the DSCRO to University of Liverpool.
2) University of Liverpool add derived fields and provide analysis.
3) University of Liverpool then pass the processed, pseudonymised and linked data to the CCG.
4) Aggregation of required data for CCG management use will be completed by University of Liverpool or the CCG as instructed by the CCG.
5) Patient level data will not be shared outside of the CCG and will only be shared within the CCG on a need to know basis, as per the purposes stipulated within the Data Sharing Agreement. External aggregated reports only with small number suppression can be shared.

Data Processor – Salford Royal Foundation Trust: Hosting Advancing Quality Alliance (AQuA)

1) Pseudonymised SUS+, Local Provider data and Mental Health data (MHSDS, MHMDS, MHLDDS) only is securely transferred from the DSCRO to Advancing Quality Alliance
2) Advancing Quality Alliance add derived fields and provide analysis.
3) Allowed linkage is between the data sets contained within point 1.
4) Advancing Quality Alliance then pass the processed, pseudonymised and linked data to the CCG.
5) Aggregation of required data for CCG management use will be completed by Advancing Quality Alliance 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 – Salford Royal Foundation Trust: Hosting Academic Health Sciences Network

1) Pseudonymised SUS+ and Local Provider data only is securely transferred from the DSCRO to Academic Health Sciences Network
2) Academic Health Sciences Network add derived fields and provide analysis.
3) Allowed linkage is between the data sets contained within point 1.
4) Academic Health Sciences Network then pass the processed, pseudonymised and linked data to the CCG.
5) Aggregation of required data for CCG management use will be completed by Academic Health Sciences Network 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.

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Project 6 — NIC-113980-R8Z8K

Type of data: information not disclosed for TRE projects

Opt outs honoured: N

Legal basis: Health and Social Care Act 2012

Purposes: ()

Sensitive: Sensitive

When:2017.12 — 2018.02.

Access method: Ongoing

Data-controller type:

Sublicensing allowed:

Datasets:

1. SUS data (Accident & Emergency, Admitted Patient Care & Outpatient)
2. Improving Access to Psychological Therapies Data Set
3. Mental Health Minimum Data Set
4. Mental Health and Learning Disabilities Data Set
5. Children and Young People's Health Services Data Set
6. Maternity Services Dataset
7. Mental Health Services Data Set
8. Local Provider Data - Acute
9. Local Provider Data - Ambulance
10. Local Provider Data - Community
11. Local Provider Data - Demand for Service
12. Local Provider Data - Diagnostic Services
13. Local Provider Data - Emergency Care
14. Local Provider Data - Experience Quality and Outcomes
15. Local Provider Data - Mental Health
16. Local Provider Data - Other not elsewhere classified
17. Local Provider Data - Population Data

Objectives:

Objective for processing:
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)
 Adult Social Care
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
 Monitoring, at a population level, particular cohorts of service users and designing analytical models which support more effective interventions in health and Adult Social Care
 Monitoring service and integrated care outcomes across a pathway or care setting involving Adult Social Care
 Developing, through evaluation, more effective interventions across a pathway or care setting involving Adult Social Care
 Designing and implementing new payment models across health and Adult Social Care
 Understanding current and future population needs and resource utilisation for local strategic planning purposes.
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.

Expected Benefits:

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

Specific outputs expected, including target date:
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
13. Production of aggregate reports for CCG Business Intelligence including:-
a. Understanding current and future population needs and resource utilisation
b. Tracking outcomes across pathways, and meeting outcome targets
c. Evaluation of interventions across pathways
d. Designing and implementing new payment models
e. More sophisticated risk stratification and predictive analytics
f. Demand management
14. Production of project / programme level dashboards

Processing:

Processing activities:
Data must only be used as stipulated within this Data Sharing Agreement.

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

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

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.

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.
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

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)
10. Adult Social Care
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), Diagnostic Imaging data (DIDS) and Adult Social Care 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 derived fields, link data and provide analysis to:
o See patient journeys for pathways or service design, re-design and de-commissioning.
o Check recorded activity against contracts or invoices and facilitate discussions with providers.
o Undertake population health management
o Undertake data quality and validation checks
o Thoroughly investigate the needs of the population
o Understand cohorts of residents who are at risk
o Conduct Health Needs Assessments
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.
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.

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Project 7 — NIC-47191-D9X6J

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:

1. Children and Young People's Health Services Data Set
2. Improving Access to Psychological Therapies Data Set
3. Local Provider Data - Acute
4. Local Provider Data - Ambulance
5. Local Provider Data - Community
6. Local Provider Data - Demand for Service
7. Local Provider Data - Diagnostic Services
8. Local Provider Data - Emergency Care
9. Local Provider Data - Experience Quality and Outcomes
10. Local Provider Data - Public Health & Screening services
11. Local Provider Data - Mental Health
12. Local Provider Data - Other not elsewhere classified
13. Local Provider Data - Population Data
14. Mental Health and Learning Disabilities Data Set
15. Mental Health Minimum Data Set
16. Mental Health Services Data Set
17. SUS Accident & Emergency data
18. SUS Admitted Patient Care data
19. SUS Outpatient data
20. Maternity Services Dataset
21. SUS data (Accident & Emergency, Admitted Patient Care & Outpatient)
22. SUS (Accident & Emergency, Inpatient and Outpatient data)
23. 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, Public Health & Screening services

Objectives:

Invoice Validation
As an approved Controlled Environment for Finance (CEfF), the CCG receives SUS data identifiable at the level of NHS number according to S.251 CAG 7-07(a) and (b)/2013. The data is required for the purpose of invoice validation. The NHS number is only used to confirm the accuracy of backing-data sets and will not be shared outside of the CEfF.

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 – 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:

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 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:

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 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 aggregated reports
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 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.
Invoice Validation
1. SUS Data is sent from the SUS Repository to North West DSCRO
2. North West 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. Checking the individual is registered to the 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 national NHS and local commissioning policies as well as being checked against system access and reports provided by the HSCIC to confirm the payments are:
i. In line with Payment by Results tariffs
ii. Are 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 that the invoice has been validated and can be paid. Any discrepancies or non-validated invoices are investigated and resolved

Risk Stratification
Data Processor 1 – Arden & GEM CSU:
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. 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 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.
7. 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.
8. 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.

Data Processor 2 – Midlands & Lancashire CSU:
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. 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 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.
7. 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.
8. Data identifiable at the level of NHS number is transferred securely to Midlands & Lancashire CSU who apply the data to the BI tool to analyse and produce reports.
9. Once Midlands & Lancashire CSU has completed the processing, the CCG can access the online system via a secure N3 connection to access the data pseudonymised at patient level and aggregated reports.
10. 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.

Pseudonymised – SUS and Local Flows
Data Processor 1 – Arden & GEM CSU:
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 the 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 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 GMSS then pass the processed 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 in line with the HES analysis guide.
Data Processor 2 – Midlands & Lancashire CSU:
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. The identifiable SUS data and identifiable local provider flow data is securely transferred from North West DSCRO to Central Midlands DSCRO.
3. Data quality management and pseudonymisation of data is completed by Central Midlands DSCRO and the pseudonymised data is then passed securely to Midland & Lancashire CSU for the addition of derived fields, linkage of data sets and analysis.
4. Midland & 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.
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 3 – Hartree Centre: Science and Technology Research (SUS Only):
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 sets and analysis.
3. Arden & GEM CSU then passes the pseudonymised data securely to Hartree Centre: Science and Technology Research.
4. Hartree Centre: Science and Technology Research analyse the data to see patient journeys for pathway or service design, re-design and de-commissioning.
5. Hartree Centre: Science and Technology Research then pass the processed pseudonymised data to the CCG
Data Processor 4 – University of Liverpool (SUS Only):
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 sets and analysis.
3. Arden & GEM CSU then passes the pseudonymised data securely to University of Liverpool.
4. University of Liverpool analyse the data to see patient journeys for pathway or service design, re-design and de-commissioning.
5. University of Liverpool then pass the processed pseudonymised data to the CCG
Data Processor 5 - AQuA
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. Only substantive employees of the Trust have access to the data.
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 6 – Academic Health Sciences Network (Utilisation Management Team)
5. 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.
6. 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.
7. Arden & GEM CSU then passes the pseudonymised data securely to the Academic Health Service (Utilisation Management Team) (AHSN UMT)
8. 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. Only substantive employees of the Trust have access to the data.
8.
9. 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.

Pseudonymised – Mental Health, MSDS, IAPT, CYPHS and DIDS
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 Maternity (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
2. The pseudonymised data is securely transferred from North West DSCRO to Central Midlands DSCRO.
3. Data quality management and pseudonymisation of data is completed by Central Midlands 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.
4. 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.
5. The CCG analyses the data to see patient journeys for pathway or service design, re-design and de-commissioning
6. Aggregation of required data for CCG management use can be completed by the CSU or 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 in line with the HES analysis guide.
Data Processor 2 – Midlands & Lancashire 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 Maternity (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
2. The identifiable SUS data and identifiable local provider flow data is securely transferred from North West DSCRO to Central Midlands DSCRO.
3. Data quality management and pseudonymisation of data is completed by Central Midlands DSCRO and the pseudonymised data is then passed securely to Midland & Lancashire CSU for the addition of derived fields, linkage of data sets and analysis.
4. Midland & 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.
5. The CCG analyses the data to see patient journeys for pathway or service design, re-design and de-commissioning
6. Aggregation of required data for CCG management use can be completed by the CSU or 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 in line with the HES analysis guide.
Data Processor 5 - Advancing Quality Alliance (AQuA)
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.

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