NHS Digital Data Release Register - reformatted

NHS South Yorkshire Icb - 03n projects

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


DSfC - NHS Sheffield CCG - COMM, IV, RS — DARS-NIC-89613-L9D8C

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 (Section 251, Section 251 NHS Act 2006)

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(7), Health and Social Care Act 2012 – s261(1) and s261(2)(b)(ii), Health and Social Care Act 2012 – s261(7), Health and Social Care Act 2012 – s261(7); National Health Service Act 2006 - s251 - 'Control of patient information'., Health and Social Care Act 2012 - s261 - 'Other dissemination of information', Health and Social Care Act 2012 - s261 - 'Other dissemination of information'; National Health Service Act 2006 - s251 - 'Control of patient information'., Health and Social Care Act 2012 – s261(2)(b)(ii)

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

Sensitive: Sensitive

When:DSA runs 2019-08-01 — 2020-07-31 2018.06 — 2021.05.

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

Data-controller type: NHS SHEFFIELD CCG, NHS SOUTH YORKSHIRE ICB - 03N

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. Community Services Data Set
  22. National Cancer Waiting Times Monitoring DataSet (CWT)
  23. Civil Registration - Births
  24. Civil Registration - Deaths
  25. National Diabetes Audit
  26. Patient Reported Outcome Measures
  27. National Cancer Waiting Times Monitoring DataSet (NCWTMDS)
  28. Improving Access to Psychological Therapies Data Set_v1.5
  29. Civil Registrations of Death
  30. Community Services Data Set (CSDS)
  31. Diagnostic Imaging Data Set (DID)
  32. Improving Access to Psychological Therapies (IAPT) v1.5
  33. Mental Health and Learning Disabilities Data Set (MHLDDS)
  34. Mental Health Minimum Data Set (MHMDS)
  35. Mental Health Services Data Set (MHSDS)
  36. Patient Reported Outcome Measures (PROMs)

Objectives:

Invoice Validation
Invoice validation is part of a process by which providers of care or services get paid for the work they do.
Invoices are submitted to the Clinical Commissioning Group (CCG) so they are able to ensure that the activity claimed for each patient is their responsibility. This is done by processing and analysing Secondary User Services (SUS+) data, which is received into a secure Controlled Environment for Finance (CEfF). The SUS+ data is identifiable at the level of NHS number. The NHS number is only used to confirm the accuracy of backing-data sets and will not be used further.
The legal basis for this to occur is under Section 251 of NHS Act 2006.
Invoice Validation with be conducted by Rotherham CCG
The CCG are advised by Rotheram CCG whether payment for invoices can be made or not.

Risk Stratification
Risk stratification is a tool for identifying and predicting which patients are at high risk or are likely to be at high risk and prioritising the management of their care in order to prevent worse outcomes.
To conduct risk stratification Secondary User Services (SUS+) data, identifiable at the level of NHS number is linked with Primary Care data (from GPs) and an algorithm is applied to produce risk scores. Risk Stratification provides focus for future demands by enabling commissioners to prepare plans for patients. Commissioners can then prepare plans for patients who may require high levels of care. Risk Stratification also enables General Practitioners (GPs) to better target intervention in Primary Care.
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 North of England Commissioning Support Unit (NECS)

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
§ 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 North of England Commissioning Support Unit (NECS), Medeanalytics, Sheffield Hallam University, University of Sheffield & Attain Health Management Services Ltd

Yielded Benefits:

Key benefits: CCG achieved financial balance Service pressures known and managed

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


Specific outputs related to the work of the following Data Processors:
Data Processor 2 – Medeanalytics
1) The Medeanalytics processing will generate an output of predictive risk scores at the person level calculated from predictor parameters obtained from SUS, primary care and local flows.
2) The aim is to calculate a range of different predictive scores that would be useful to inform patients’ direct care, where validated algorithms are available. Such predictive scores would include the risk of hospital admission, electronic frailty index and the risk of admission to long term residential care.
3) The risk scores would be augmented with other contextual information relevant to the care process in the format of a care dashboard, such as diagnoses of long term conditions and relevant service activity.
Data Processor 3 – Sheffield Hallam University
1) The university undertakes commissioned projects on behalf of the CCG to evaluate pilots and similar schemes to inform commissioning / investment decisions – generally where the CCG does not have in-house expertise – and this will involve data processing and analytics. Generally, the CCG does not solely work with a single university data processor, because of a) Fair Trading considerations, and b) each university offers quite different specialisations and expertise, the specifics of which in relation to this application are set out below.
2) The general outputs from data processing by the university includes aggregated descriptive and interferential statistics to ascertain outcome and impact in such evaluations, as well as health economic descriptors to understand cost-utility or cost-effectiveness.
3) The CCG works with Sheffield Hallam University principally in relation to projects involving the healthcare workforce (as a training institution for nurses and therapy professions) and those involving community healthcare services.
4) Specific outputs will focus on the effectiveness and cost-effectiveness of options involving the healthcare professions and teams (most often the nursing and therapy workforce), multidisciplinary working, and community healthcare services.

Data Processor 4 – University of Sheffield
1) The university undertakes commissioned projects on behalf of the CCG to evaluate pilots and similar schemes to inform commissioning / investment decisions – generally where the CCG does not have in-house expertise – and this will involve data processing and analytics. Generally, the CCG does not solely work with a single university data processor, because of a) Fair Trading considerations, and b) each university offers quite different specialisations and expertise, the specifics of which in relation to this application are set out below.
2) The general outputs from data processing by the university includes aggregated descriptive and interferential statistics to ascertain outcome and impact in such evaluations, as well as health economic descriptors to understand cost-utility or cost-effectiveness.
3) The University of Sheffield, School of Health & Related Research (ScHARR) are partners in the Yorkshire & The Humber Collaboration for Leadership in Applied Health Research and Care (CLAHRC). This includes the evaluation on behalf of the CCG of several current and forthcoming national pilots taking place in the city.
4) The CCG works with the University of Sheffield principally in relation to mental health & wellbeing, secondary care services, urgent & emergency care, primary care services and health technologies.
5) Specific outputs will focus on the effectiveness and cost-effectiveness of options involving the services set out in item 2.

Data Processor 5 – Attain Health Management Services Ltd
1) Attain has been appointed to assist the CCG with its planning for Sustainability & Transformation Plans; work that is focused on Sheffield-based tertiary services having larger geographic catchments, principally acute stroke and children’s secondary care services.
2) Outputs from data processing will comprise consolidated activity and commissioner expenditure in relation to these services.

Processing:

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

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

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

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

1. Identifiable SUS+ Data is obtained from the SUS+ Repository to the Data Services for Commissioners Regional Office (DSCRO).
2. The DSCRO pushes a one-way data flow of SUS+ data into the Controlled Environment for Finance (CEfF) in the Rotherham CCG
3. The CSU carry out the following processing activities within the CEfF for invoice validation purposes:
a. Validating that the Clinical Commissioning Group is responsible for payment for the care of the individual by using SUS+ and/or backing flow data.
b. Once the backing information is received, this will be checked against national NHS and local commissioning policies as well as being checked against system access and reports provided by NHS Digital to confirm the payments are:
i. In line with Payment by Results tariffs
ii. are in relation to a patient registered with a CCG GP or resident within the CCG area.
iii. The health care provided should be paid by the CCG in line with CCG guidance. 
4. The CCG are notified that the invoice has been validated and can be paid. Any discrepancies or non-validated invoices are investigated and resolved between Rotherham CCG CEfF team and the provider meaning that no identifiable data needs to be sent to the CCG. The CCG only receives notification to pay and management reporting detailing the total quantum of invoices received pending, processed etc.

Risk Stratification

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 North of England Commissioning Support Unit (NECS), who hold the SUS+ data within the secure Data Centre on N3.
3. Identifiable GP Data is securely sent from the GP system to North of England Commissioning Support Unit (NECS)
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 North of England Commissioning Support Unit (NECS) has completed the processing, the CCG can access the online system via a secure connection to access the data pseudonymised at patient level.

Commissioning
The Data Services for Commissioners Regional Office (DSCRO) obtains the following data sets:
1. SUS+
2. Local Provider Flows (received directly from providers)
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 1 - North of England Commissioning Support Unit (NECS)
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) is securely transferred from the DSCRO to North of England Commissioning Support Unit (NECS).
2. North of England Commissioning Support Unit (NECS) 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. North of England Commissioning Support Unit (NECS) then pass the processed, pseudonymised and linked data to the CCG.
5. Aggregation of required data for CCG management use will be completed by North of England Commissioning Support Unit (NECS) or the CCG as instructed by the CCG.
6. Patient level data will not be shared outside of the CCG and will only be shared within the CCG on a need to know basis, as per the purposes stipulated within the Data Sharing Agreement. External aggregated reports only with small number suppression can be shared as set out within NHS Digital guidance applicable to each data set.

Data processor 2 - MedeAnalytics International Ltd
1) Pseudonymised SUS and Local Provider data, only is securely transferred from the DSCRO to North England CSU.
2) North of England Commissioning Support Unit (NECS) add derived fields, link data and provide analysis.
3) North England CSU also receives identifiable GP data from GP Practices. This data is kept secure and separate from any other data and is pseudonymised once it has entered the CSU. Any identifiable data is then destroyed.
4) North England CSU then securely pass the pseudonymised data to MedeAnalytics International Ltd for the addition of derived fields, linkage of data sets and analysis.
5) Allowed linkage is between the data sets contained within point 1 and point 3 (only once pseudonymised).
6) MedeAnalytics International Ltd also receive pseudonymised Social care data from providers.
7) MedeAnalytics International Ltd then link and process the pseudonymised data and pass the processed, pseudonymised and linked data to the CCG. The CCG analyse the data to see patient journeys for pathways or service design, re-design and de-commissioning.
8) Re-identification is only permitted for the purposes of direct care.
9) Aggregation of required data for CCG management use will be completed by North of England Commissioning Support Unit (NECS) or the CCG as instructed by the CCG.
10) Patient level data will not be shared outside of the CCG and will only be shared within the CCG on a need to know basis, as per the purposes stipulated within the Data Sharing Agreement. External aggregated reports only with small number suppression can be shared as set out within NHS Digital guidance applicable to each data set.


Data Processor 3 - Sheffield Hallam University
1) Pseudonymised SUS and Local Provider data only is securely transferred from the DSCRO to North England CSU.
2) North of England Commissioning Support Unit (NECS) add derived fields, link data and provide analysis.
3) Allowed linkage is between the data sets contained within point 1.
4) North of England Commissioning Support Unit (NECS) then pass the processed, pseudonymised and linked data to the CCG.
5) The CCG analyse the data to see patient journeys for pathways or service design, re-design and de-commissioning.
6) The CCG then pass the pseudonymised data securely to Sheffield Hallam University to analyse the data to see patient journeys for pathways or service design, re-design and de-commissioning.
7) Aggregation of required data for CCG management use will be completed by Sheffield Hallam University as instructed by the CCG.
8) Patient level data will not be shared outside of Sheffield Hallam University and will only be shared within Sheffield Hallam University 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 Processor 4 - University of Sheffield
1) Pseudonymised SUS and Local Provider data only is securely transferred from the DSCRO to North England CSU.
2) North of England Commissioning Support Unit (NECS) add derived fields, link data and provide analysis.
3) Allowed linkage is between the data sets contained within point 1.
4) North of England Commissioning Support Unit (NECS) then pass the processed, pseudonymised and linked data to the CCG.
5) The CCG analyse the data to see patient journeys for pathways or service design, re-design and de-commissioning.
6) The CCG then pass the pseudonymised data securely to University of Sheffield to analyse the data to see patient journeys for pathways or service design, re-design and de-commissioning.
7) Aggregation of required data for CCG management use will be completed by the University of Sheffield as instructed by the CCG.
8) Patient level data will not be shared outside of the University of Sheffield and will only be shared within the University of Sheffield 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 Processor 5 – Attain Health Management Services Ltd
1) Pseudonymised SUS and Local Provider data only is securely transferred from the DSCRO to North England CSU.
2) North of England Commissioning Support Unit (NECS) add derived fields, link data and provide analysis.
3) Allowed linkage is between the data sets contained within point 1.
4) North of England Commissioning Support Unit (NECS) then pass the processed, pseudonymised and linked data to Attain.
5) Attain analyse the data to see patient journeys for pathways or service design, re-design and de-commissioning and then send the pseudonymised data to the CCG
6) Aggregation of required data for CCG management use will be completed by Attain or the CCG as instructed by the CCG.
7) Patient level data will not be shared outside of the CCG and will only be shared within the CCG on a need to know basis, as per the purposes stipulated within the Data Sharing Agreement. External aggregated reports only with small number suppression can be shared as set out within NHS Digital guidance applicable to each data set.