NHS Digital Data Release Register - reformatted

NHS Greater Manchester Icb - 00t projects

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


DSfC - NHS Bolton CCG - RS & IV — DARS-NIC-47180-P3Z1Q

Opt outs honoured: No - data flow is not identifiable, Yes - patient objections upheld, Identifiable (Section 251, Section 251 NHS Act 2006)

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

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

Sensitive: Sensitive

When:DSA runs 2019-10-18 — 2022-10-17 2018.06 — 2021.05.

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

Data-controller type: NHS BOLTON CCG, NHS GREATER MANCHESTER ICB - 00T

Sublicensing allowed: No

Datasets:

  1. Ambulance-Local Provider Flows
  2. Children and Young People Health
  3. Community-Local Provider Flows
  4. Diagnostic Imaging Dataset
  5. Emergency Care-Local Provider Flows
  6. Experience, Quality and Outcomes-Local Provider Flows
  7. Improving Access to Psychological Therapies Data Set
  8. Maternity Services Data Set
  9. Mental Health Minimum Data Set
  10. Mental Health Services Data Set
  11. Mental Health-Local Provider Flows
  12. Population Data-Local Provider Flows
  13. Primary Care Services-Local Provider Flows
  14. Acute-Local Provider Flows
  15. Demand for Service-Local Provider Flows
  16. Diagnostic Services-Local Provider Flows
  17. Mental Health and Learning Disabilities Data Set
  18. Other Not Elsewhere Classified (NEC)-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)

Objectives:

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 and GEM Commissioning Support Unit.

Commissioning
To use pseudonymised data to provide intelligence to support the commissioning of health services. The data (containing both clinical and financial information) is analysed so that health care provision can be planned to support the needs of the population within the CCG area.
The CCGs commission services from a range of providers covering a wide array of services. Each of the data flow categories requested supports the commissioned activity of one or more providers.
The following pseudonymised datasets are required to provide intelligence to support commissioning of health services:
- Secondary Uses Service (SUS)
- Local Provider Flows
o Acute
o Ambulance
o Community
o Demand for Service
o Diagnostic Service
o Emergency Care
o Experience, Quality and Outcomes
o Mental Health
o Other Not Elsewhere Classified
o Population Data
o Primary Care Services
o Public Health Screening
- Mental Health Minimum Data Set (MHMDS)
- Mental Health Learning Disability Data Set (MHLDDS)
- Mental Health Services Data Set (MHSDS)
- Maternity Services Data Set (MSDS)
- Improving Access to Psychological Therapy (IAPT)
- Child and Young People Health Service (CYPHS)
- 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 Arden and GEM Commissioning Support Unit, Greater Manchester Shared Services (GMSS), Salford Royal NHS Foundation Trust hosting:Advancing Quality Alliance (AQuA)
Salford Royal NHS Foundation Trust hosting: Academic Health Sciences Network (Utilisation Management Team)

Yielded Benefits:

Expected Benefits:

Risk Stratification
Risk stratification promotes improved case management in primary care and will lead to the following benefits being realised:
1. Improved planning by better understanding patient flows through the healthcare system, thus allowing commissioners to design appropriate pathways to improve patient flow and allowing commissioners to identify priorities and identify plans to address these.
2. Improved quality of services through reduced emergency readmissions, especially avoidable emergency admissions. This is achieved through mapping of frequent users of emergency services 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:

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.

CCGs should work with general practices within their CCG to help them fulfil data controller responsibilities regarding flow of identifiable data into risk stratification tools.

No patient level data will be linked other than as specifically detailed within this agreement. Data will only be shared with those parties listed and will only be used for the purposes laid out in the application/agreement. The data to be released from NHS Digital will not be national data, but only that data relating to the specific locality of interest of the applicant.
The DSCRO (part of NHS Digital) will apply Type 2 objections before any identifiable data leaves the DSCRO.
NHS Digital reminds all organisations party to this agreement of the need to comply with the Data Sharing Framework Contract requirements, including those regarding the use (and purposes of that use) by “Personnel” (as defined within the Data Sharing Framework Contract ie: employees, agents and contractors of the Data Recipient who may have access to that data)

Segregation
Where the Data Processor and/or the Data Controller hold both identifiable and pseudonymised data, the data will be held separately so data cannot be linked.
All access to data is audited.

Risk Stratification
1. Identifiable SUS data is obtained from the SUS Repository to the Data Services for Commissioners Regional Office (DSCRO).
2. Data quality management and standardisation of data is completed by the DSCRO and the data identifiable at the level of NHS number is transferred securely to Arden and 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 and 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 and GEM 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.

Commissioning
The Data Services for Commissioners Regional Office (DSCRO) obtains the following data sets:
1. SUS
2. Local Provider Flows (received directly from providers)
o Acute
o Ambulance
o Community
o Demand for Service
o Diagnostic Service
o Emergency Care
o Experience, Quality and Outcomes
o Mental Health
o Other Not Elsewhere Classified
o Population Data
o Primary Care Services
o Public Health Screening
3. Mental Health Minimum Data Set (MHMDS)
4. Mental Health Learning Disability Data Set (MHLDDS)
5. Mental Health Services Data Set (MHSDS)
6. Maternity Services Data Set (MSDS)
7. Improving Access to Psychological Therapy (IAPT)
8. Child and Young People Health Service (CYPHS)
9. Diagnostic Imaging Data Set (DIDS)


Data quality management and pseudonymisation is completed within the DSCRO and is then disseminated as follows:
Data Processor 1 – Arden and GEM Commissioning Support Unit
1) Pseudonymised SUS, Local Provider data, Mental Health data (MHSDS, MHMDS, MHLDDS), Maternity data (MSDS), Improving Access to Psychological Therapies data (IAPT), Child and Young People’s Health data (CYPHS) and Diagnostic Imaging data (DIDS) only is securely transferred from the DSCRO to Arden and GEM CSU.
2) Arden and GEM CSU add derived fields, link data and provide analysis to:
o See patient journeys for pathways or service design, re-design and de-commissioning (CSU or CCG).
o Check recorded activity against contracts or invoices and facilitate discussions with providers (CSU or CCG).
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 GEM Commissioning Support Unit then pass the processed, pseudonymised and linked data to the CCG. The CCG analyse the data to:
o See patient journeys for pathways or service design, re-design and de-commissioning (CSU or CCG).
o Check recorded activity against contracts or invoices and facilitate discussions with providers (CSU or CCG).
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
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 2 – Greater Manchester Shared Services
1) Pseudonymised SUS, Mental Health data (MHSDS, MHMDS, MHLDDS), Improving Access to Psychological Therapies data (IAPT), only is securely transferred from the DSCRO to Data Processor 2 Greater Manchester Shared Services.
2) GMSS CSU add derived fields, link data and provide analysis to:
o See patient journeys for pathways or service design, re-design and de-commissioning (CSU or CCG).
o Check recorded activity against contracts or invoices and facilitate discussions with providers (CSU or CCG).
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) GMSS then pass the processed, pseudonymised and linked data to the CCG. The CCG analyse the data to:
o See patient journeys for pathways or service design, re-design and de-commissioning (CSU or CCG).
o Check recorded activity against contracts or invoices and facilitate discussions with providers (CSU or CCG).
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
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 3 – Advancing Quality Alliance
1) Pseudonymised SUS, Local Provider data, Mental Health data (MHSDS, MHMDS, MHLDDS), only is securely transferred from the DSCRO to Advancing Quality Alliance.
2) Advancing Quality Alliance add derived fields, link data and provide analysis to:
o See patient journeys for pathways or service design, re-design and de-commissioning (CSU or CCG).
o Check recorded activity against contracts or invoices and facilitate discussions with providers (CSU or CCG).
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) Advancing Quality Alliance then pass the processed, pseudonymised and linked data to the CCG. The CCG analyse the data to:
o See patient journeys for pathways or service design, re-design and de-commissioning (CSU or CCG).
o Check recorded activity against contracts or invoices and facilitate discussions with providers (CSU or CCG).
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
5) Patient level data will not be shared outside of the CCG and will only be shared within the CCG on a need to know basis, as per the purposes stipulated within the Data Sharing Agreement. External aggregated reports only with small number suppression can be shared.
Data Processor 4 – Academic Health Sciences Network (Utilisation Management Team ) (SUS Only)
1) Pseudonymised SUS, only is securely transferred from the DSCRO to Academic Health Sciences Network (Utilisation Management Team)
2) Academic Health Sciences Network (Utilisation Management Team )add derived fields, link data and provide analysis to:
o See patient journeys for pathways or service design, re-design and de-commissioning (CSU or CCG).
o Check recorded activity against contracts or invoices and facilitate discussions with providers (CSU or CCG).
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) Academic Health Sciences Network (Utilisation Management Team) then pass the processed, pseudonymised and linked data to the CCG. The CCG analyse the data to:
o See patient journeys for pathways or service design, re-design and de-commissioning (CSU or CCG).
o Check recorded activity against contracts or invoices and facilitate discussions with providers (CSU or CCG).
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
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.


DSfC - NHS Bolton CCG - STP - Comm — DARS-NIC-139091-F3T3H

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

Legal basis: Health and Social Care Act 2012 – s261(1) and s261(2)(b)(ii), Health and Social Care Act 2012 - s261 - 'Other dissemination of information', Health and Social Care Act 2012 – s261(1) and s261(2)(b)(ii), Health and Social Care Act 2012 – s261(2)(b)(ii)

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

Sensitive: Sensitive, and Non Sensitive, and Non-Sensitive

When:DSA runs 2019-02-01 — 2022-01-31 2019.01 — 2021.05.

Access method: Frequent Adhoc Flow, One-Off

Data-controller type: NHS BOLTON CCG, NHS BURY CCG, NHS HEYWOOD, MIDDLETON AND ROCHDALE CCG, NHS MANCHESTER CCG, NHS OLDHAM CCG, NHS SALFORD CCG, NHS STOCKPORT CCG, NHS TAMESIDE AND GLOSSOP CCG, NHS TRAFFORD CCG, NHS WIGAN BOROUGH CCG, NHS BOLTON CCG, NHS BURY CCG, NHS CHESHIRE CCG, NHS HEYWOOD, MIDDLETON AND ROCHDALE CCG, NHS MANCHESTER CCG, NHS OLDHAM CCG, NHS SALFORD CCG, NHS STOCKPORT CCG, NHS TAMESIDE AND GLOSSOP CCG, NHS TRAFFORD CCG, NHS WIGAN BOROUGH CCG, NHS GREATER MANCHESTER ICB - 00T, NHS GREATER MANCHESTER ICB - 00V, NHS GREATER MANCHESTER ICB - 00Y, NHS GREATER MANCHESTER ICB - 01D, NHS GREATER MANCHESTER ICB - 01G, NHS GREATER MANCHESTER ICB - 01W, NHS GREATER MANCHESTER ICB - 01Y, NHS GREATER MANCHESTER ICB - 02A, NHS GREATER MANCHESTER ICB - 02H, NHS GREATER MANCHESTER ICB - 14L, NHS CHESHIRE AND MERSEYSIDE ICB - 27D, NHS GREATER MANCHESTER ICB - 00T, NHS GREATER MANCHESTER ICB - 00V, NHS GREATER MANCHESTER ICB - 00Y, NHS GREATER MANCHESTER ICB - 01D, NHS GREATER MANCHESTER ICB - 01G, NHS GREATER MANCHESTER ICB - 01W, NHS GREATER MANCHESTER ICB - 01Y, NHS GREATER MANCHESTER ICB - 02A, NHS GREATER MANCHESTER ICB - 02H, NHS GREATER MANCHESTER ICB - 14L

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. Civil Registration - Births
  24. Civil Registration - Deaths
  25. National Diabetes Audit
  26. Patient Reported Outcome Measures
  27. e-Referral Service for Commissioning
  28. National Cancer Waiting Times Monitoring DataSet (NCWTMDS)
  29. Improving Access to Psychological Therapies Data Set_v1.5
  30. Adult Social Care
  31. Medicines dispensed in Primary Care (NHSBSA data)
  32. Personal Demographic Service
  33. Summary Hospital-level Mortality Indicator

Objectives:

Commissioning
The NHS and local councils have come together in 44 areas covering all of England to develop proposals to improve health and care. They have formed new partnerships – known as sustainability and transformation partnerships – to plan jointly for the next few years.

Sustainability and transformation partnerships build on collaborative work that began under the NHS Shared Planning Guidance for 2016/17 – 2020/21, to support implementation of the Five Year Forward View. They are supported by six national health and care bodies: NHS England; NHS Improvement; the Care Quality Commission (CQC); Health Education England (HEE); Public Health England (PHE) and the National Institute for Health and Care Excellence (NICE).

The CCGs are part of the Greater Manchester Sustainable Transformation Partnership. The STP is responsible for implementing large parts of the 5 year forward view from NHS England. The STP is implementing several initiatives:

- Putting the patient at the heart of the health system
- Working across organisational boundaries to deliver care and including social care, public Health, providers and GPs as well as CCGs
- Reviewing patient pathways to improve patient experience whilst reducing costs e.g. reduce the number of standard tests a patient may have and only have the ones they need
- Planning the demand and capacity across the healthcare system across 10 CCGs to ensure we have the right buildings, services and staff to cope with demand whilst reducing the impact on costs
- Working to prevent or capture conditions early as they are cheaper to treat
- Introduce initiatives to change behaviours e.g. move more care into the community
- Patient pathway planning for the above


To ensure the patient is at the heart of care, the STP is focussing on where services are required across the geographical region. This assists to ensure delivery of care in the right place for patients who may move and change services across CCGs.

The CCG will work proactively and collaboratively with the other CCGs in the STP to redesign services across boundaries to integrate services. Collaborative sharing is required for CCGs to understand these requirements.

The CCGs will 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 STP 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 CCGs are Joint Data Controllers and will receive data for the area of residence and registration for the CCGs listed:
- NHS Bolton CCG
- NHS Bury CCG
- NHS Heywood, Middleton and Rochdale CCG
- NHS Manchester CCG
- NHS Oldham CCG
- NHS Salford CCG
- NHS Stockport CCG
- NHS Tameside & Glossop CCG
- NHS Trafford CCG
- NHS Wigan Borough CCG
The following pseudonymised datasets are required to provide intelligence to support commissioning of health services:
- Secondary Uses Service (SUS+)
- Local Provider Flows
o Acute
o Ambulance
o Community
o Demand for Service
o Diagnostic Service
o Emergency Care
o Experience, Quality and Outcomes
o Mental Health
o Other Not Elsewhere Classified
o Population Data
o Primary Care Services
o Public Health Screening
- Mental Health Minimum Data Set (MHMDS)
- Mental Health Learning Disability Data Set (MHLDDS)
- Mental Health Services Data Set (MHSDS)
- Maternity Services Data Set (MSDS)
- Improving Access to Psychological Therapy (IAPT)
- Child and Young People Health Service (CYPHS)
- Community Services Data Set (CSDS)
- Diagnostic Imaging Data Set (DIDS)
- National Cancer Waiting Times (CWT)
The pseudonymised data is required to for the following purposes:
 Population health management:
• Understanding the interdependency of care services
• Targeting care more effectively
• Using value as the redesign principle
 Data Quality and Validation – allowing data quality checks on the submitted data
 Thoroughly investigating the needs of the population, to ensure the right services are available for individuals when and where they need them
 Understanding cohorts of residents who are at risk of becoming users of some of the more expensive services, to better understand and manage those needs
 Monitoring population health and care interactions to understand where people may slip through the net, or where the provision of care may be being duplicated
 Modelling activity across all data sets to understand how services interact with each other, and to understand how changes in one service may affect flows through another
 Service redesign
 Health Needs Assessment – identification of underlying disease prevalence within the local population
 Patient stratification and predictive modelling - to identify specific patients at risk of requiring hospital admission and other avoidable factors such as risk of falls, computed using algorithms executed against linked de-identified data, and identification of future service delivery models

The pseudonymised data is required to ensure that analysis of health care provision can be completed to support the needs of the health profile of the population within the STP 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 and Greater Manchester Shared Service.

Yielded Benefits:

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.
6. Feedback to NHS service providers on data quality at an aggregate and individual record level – only on data initially provided by the service providers.
7. Improved planning by better understanding patient flows through the healthcare system, thus allowing commissioners to design appropriate pathways to improve patient flow and allowing commissioners to identify priorities and identify plans to address these.
8. Improved quality of services through reduced emergency readmissions, especially avoidable emergency admissions. This is achieved through mapping of frequent users of emergency services and early intervention of appropriate care.
9. Improved access to services by identifying which services may be in demand but have poor access, and from this identify areas where improvement is required.
10. Potentially reduced premature mortality by more targeted intervention in primary care, which supports the commissioner to meets its requirement to reduce premature mortality in line with the CCG Outcome Framework.
11. Better understanding of the health of and the variations in health outcomes within the population to help understand local population characteristics.
12. Better understanding of contract requirements, contract execution, and required services for management of existing contracts, and to assist with identification and planning of future contracts
13. Insights into patient outcomes, and identification of the possible efficacy of outcomes-based contracting opportunities.
14. Insights into patient outcomes, and identification of the possible efficacy of outcomes-based contracting opportunities.
15. Reviewing current service provision
a. Cost-benefit analysis and service impact assessments to underpin service transformation across health economy
b. Service planning and re-design (development of NMoC and integrated care pathways, new partnerships, working with new providers etc.)
c. Impact analysis for different models or productivity measures, efficiency and experience
d. Service and pathway review
e. Service utilisation review
16. Ensuring compliance with evidence and guidance
a. Testing approaches with evidence and compliance with guidance.
17. Monitoring outcomes
a. Analysis of variation in outcomes across population group
18. Understanding how services impact across the health economy
a. Service evaluation
b. Programme reviews
c. Analysis of productivity, outcomes, experience, plan, targets and actuals
d. Assessing value for money and efficiency gains
e. Understanding impact of services on health inequalities
19. Understanding how services impact on the health of the population and patient cohorts
a. Measuring and assessing improvement in service provision, patient experience & outcomes and the cost to achieve this
b. Propensity matching and scoring
c. Triple aim analysis
20. Understanding future drivers for change across health economy
a. Forecasting health and care needs for population and population cohorts across STPs
b. Identifying changes in disease trends and prevalence
c. Efficiencies that can be gained from procuring services across wider footprints, from new innovations
d. Predictive modelling
21. Delivering services that meet changing needs of population
a. Analysis to support policy development
b. Ethical and equality impact assessments
c. Implementation of NMOC
d. Ensuring inclusion of required elements in future contracts
e. Workforce planning
22. Maximising services and outcomes within financial envelopes across health economy
a. What-if analysis
b. Cost-benefit analysis
c. Health economics analysis
d. Scenario planning and modelling
e. Investment and disinvestment in services analysis
f. Opportunity analysis

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:
a. Patients at highest risk of admission
b. Most expensive patients (top 15%)
c. Frail and elderly
d. Patients that are currently in hospital
e. Patients with most referrals to secondary care
f. Patients with most emergency activity
g. Patients with most expensive prescriptions
h. Patients recently moving from one care setting to another
i. Discharged from hospital
ii. Discharged from community
13. Identifying and managing preventable and existing conditions
a. Identifying types of individuals and population cohorts at risk of non-elective re-admission
b. Risk stratification to identify populations suitable for case management
c. Risk profiling and predictive modelling
d. Risk stratification for planning services for population cohorts
e. Identification of disease incidence and diagnosis stratification
14. Reducing health inequalities
a. Identifying cohorts of patients who have worse health outcomes typically deprived, ethnic groups, homeless, travellers etc. to enable services to proactively target their needs
b. Socio-demographic analysis
15. Managing demand
a. Waiting times analysis
b. Service demand and supply modelling
c. Understanding cross-border and overseas visitor
d. Winter planning
e. Emergency preparedness, business continuity, recovery and contingency planning
16. Care co-ordination and planning
a. Planning packages of care
b. Service planning
c. Planning care co-ordination
17. Monitoring individual patient health, service utilisation, pathway compliance experience & outcomes across the heath and care system
a. Patient pathway analysis across health and care
b. Outcomes & experience analysis
c. Analysis to support services to react to terror situations
d. Analysis to identify vulnerable patients with potential safeguarding issues
e. Understanding equity of care and unwarranted variation
f. Modelling patient flow
g. Tracking patient pathways
h. Monitoring to support New Models of Care, Accountable Care Organisations and Sustainable Transformation Partnerships
i. Identifying duplications in care
j. Identifying gaps in care, missed diagnoses and triple fail events
k. Analysing individual and aggregated timelines
18. Undertaking budget planning, management and reporting
a. Tracking financial performance against plans
b. Budget reporting
c. Tariff development
d. Developing and monitoring capitated budgets
e. Developing and monitoring individual-level budgets
f. Future budget planning and forecasting
g. Paying for care of overseas visitors and cross-border flow
19. Monitoring the value for money
a. Service-level costing & comparisons
b. Identification of cost pressures
c. Cost benefit analysis
d. Equity of spend across services and population cohorts
e. Finance impact assessment
20. Comparing population groups, peers, national and international best practice
a. Identification of variation in productivity, cost, outcomes, quality, experience, compared with peers, national and international & best practice
b. Benchmarking against other parts of the country
c. Identifying unwarranted variations
21. Comparing expected levels
a. Standardised comparisons for prevalence, activity, cost, quality, experience, outcomes for given populations
22. Comparing local targets & plan
a. Monitoring of local variation in productivity, cost, outcomes, quality and experience
b. Local performance dashboards by service provider, commissioner, geography, NMOC, STPs
23. Monitoring activity and cost compliance against contract and agreed plans
a. Contract monitoring
b. Contract reconciliation and challenge
c. Invoice validation
24. Monitoring provider quality, demand, experience and outcomes against contract and agreed plans
a. Performance dashboards
b. CQUIN reporting
c. Clinical audit
d. Patient experience surveys
e. Demand, supply, outcome & experience analysis
f. Monitoring cross-border flows and overseas visitor activity
25. Improving provider data quality
a. Coding audit
b. Data quality validation and review
c. Checking validity of patient identity and commissioner assignment

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 STP.

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

All access to data is managed under Roles-Based Access Controls

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

NHS Digital reminds all organisations party to this agreement of the need to comply with the Data Sharing Framework Contract requirements, including those regarding the use (and purposes of that use) by “Personnel” (as defined within the Data Sharing Framework Contract ie: employees, agents and contractors of the Data Recipient who may have access to that data)

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

All access to data is auditable by NHS Digital.

Commissioning
The Data Services for Commissioners Regional Office (DSCRO) obtains the following data sets:
1. SUS+
2. Local Provider Flows (received directly from providers)
a. Acute
b. Ambulance
c. Community
d. Demand for Service
e. Diagnostic Service
f. Emergency Care
g. Experience, Quality and Outcomes
h. Mental Health
i. Other Not Elsewhere Classified
j. Population Data
k. Primary Care Services
l. Public Health Screening
3. Mental Health Minimum Data Set (MHMDS)
4. Mental Health Learning Disability Data Set (MHLDDS)
5. Mental Health Services Data Set (MHSDS)
6. Maternity Services Data Set (MSDS)
7. Improving Access to Psychological Therapy (IAPT)
8. Child and Young People Health Service (CYPHS)
9. Community Services Data Set (CSDS)
10. Diagnostic Imaging Data Set (DIDS)
11. National Cancer Waiting Times (CWT)
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), Community Services Data Set (CSDS), Diagnostic Imaging data (DIDS) and National Cancer Waiting Times (CWT) 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 provide data management and 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 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 as set out within NHS Digital guidance applicable to each data set.

Data Processor 2 - Greater Manchester Shared Service
1. Pseudonymised SUS, Local Provider data, Mental Health data (MHSDS, MHMDS, MHLDDS), Maternity data (MSDS), Improving Access to Psychological Therapies data (IAPT), Child and Young People’s Health data (CYPHS), Community Services Data Set (CSDS), Diagnostic Imaging data (DIDS) and National Cancer Waiting Times (CWT) 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 provide data management and add derived fields. Arden and Greater East Midlands Commissioning Support Unit then send the data to Greater Manchester Shared Services.
3. Greater Manchester Shared Services 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
4. Allowed linkage is between the data sets contained within point 1.
5. Greater Manchester Shared Services then pass the processed, pseudonymised and linked data to the CCG.
6. Aggregation of required data for CCG management use will be completed by Greater Manchester Shared Services 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.


DSfC - NHS Bolton CCG - Van Comm — DARS-NIC-191209-G3Z6Z

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, and Non Sensitive, and Non-Sensitive

When:DSA runs 2019-05-01 — 2022-04-30 2018.06 — 2020.07.

Access method: Frequent adhoc flow, Frequent Adhoc Flow

Data-controller type: NHS BOLTON CCG, NHS BURY CCG, NHS CHESHIRE CCG, NHS HEYWOOD, MIDDLETON AND ROCHDALE CCG, NHS MANCHESTER CCG, NHS OLDHAM CCG, NHS SALFORD CCG, NHS STOCKPORT CCG, NHS TAMESIDE AND GLOSSOP CCG, NHS TRAFFORD CCG, NHS WIGAN BOROUGH CCG, NHS CHESHIRE AND MERSEYSIDE ICB - 27D, NHS GREATER MANCHESTER ICB - 00T, NHS GREATER MANCHESTER ICB - 00V, NHS GREATER MANCHESTER ICB - 00Y, NHS GREATER MANCHESTER ICB - 01D, NHS GREATER MANCHESTER ICB - 01G, NHS GREATER MANCHESTER ICB - 01W, NHS GREATER MANCHESTER ICB - 01Y, NHS GREATER MANCHESTER ICB - 02A, NHS GREATER MANCHESTER ICB - 02H, NHS GREATER MANCHESTER ICB - 14L

Sublicensing allowed: No

Datasets:

  1. Acute-Local Provider Flows
  2. Civil Registration - Deaths

Objectives:


Greater Manchester and Eastern Cheshire Cancer Vanguard (which includes the 11 CCGs) is responsible for ensuring that the delivery of cancer services for the Greater Manchester and Eastern Cheshire (GM&EC) population meet national standards and that all patients have equal access to care.

To ensure this happens, the Cancer Vanguard’s Intelligence Service needs data for ongoing evaluation of care and outcomes at regional and local levels. These data need to be

1. Available within a suitable timeframe that allows swift response as soon as any evidence of need arises
2. Able to be aggregated into cohorts that are fully representative of the GM&EC cancer pathway population
3. At pseudonymised record level to allow full interrogation of each pathway and sub-pathway to understand where in the system problems lie and what actions need to be taken.

To support the work of the Cancer Vanguard, local flows have been established by the relevant CCGs. The local datasets required are listed below;
• Cancer Outcomes and Services Dataset (COSD)
• Cancer Waiting Times data (CWT)
• Systemic Anti-Cancer Therapy Dataset (SACT)
• National Radiotherapy Dataset (RTDS)

These, referred together, are the Acute Local Provider flows.

This timely access to these local datasets will be used to facilitate local clinical outcomes and performance evaluation in as close to real time as is possible. The datasets will not replicate the full PHE cancer registration service but provide a set of early indicators of how well the system is performing that will allow effective interventions to be made as needed.

Being on a local scale and based on a single Vanguard cancer system the cancer intelligence service will generate metrics more quickly than the national service. Outputs from this approach are not designed to replace national statistics but to act as vital interim information for CCGs ahead of the release of official statistics.

The Cancer Vanguard provides services across the CCGs in the GM&EC region and relevant cancer service providers;
Greater Manchester and Eastern Cheshire CCGs:
- NHS Bolton CCG
- NHS Bury CCG
- NHS Eastern Cheshire CCG
- NHS Heywood, Middleton and Rochdale CCG
- NHS Oldham CCG
- NHS Salford CCG
- NHS Manchester CCG
- NHS Stockport CCG
- NHS Tameside and Glossop CCG
- NHS Trafford CCG
- NHS Wigan Borough CCG

The following pseudonymised dataset is required to provide intelligence to support commissioning of health services:
- Local Provider Flows
o Acute

Yielded Benefits:

Expected Benefits:


Benefits to patient outcomes
1. Will identify and compare areas of weakness and strengths within the present system that will be acted upon by CCGs and providers to improve services and outcomes.
a. Patients diagnosed at late stage have shorter survival outcomes. The identification of groups of patients at increased risk of delayed diagnosis will be used to identify why late diagnosis occurring and then enable the most effective targeted measures to be implemented in order to improve earlier diagnosis for these patients and thus improved patient outcomes
2. Will be used to compare and contrast service performance, clinical outcomes and patient experience to highlight areas where changes are required.
a. Identification of services and CCGs that are under-performing in terms of 62 day waiting times, percentage patients diagnosed at late stage cancer and percentage of patients who survive one year, compared to the regional and England average, will enable us to identify why some patients are waiting longer for treatment, why some patients are being diagnosed late and why some patients have poorer outcomes so that the most effective targeted improvement measures can be implemented in order to reduce variation across the region, and ensure waiting times, earlier diagnosis and outcomes are equitable for all patients.
3. Will provide evidence of good practice across the system.
a. Identification of best practice will be used to show the level that should be achievable by all services and CCGs and used to develop measures to improve services and ultimately patient experience and outcomes across the region.
Benefits to the Wider GM Cancer System
1. Increased access to detailed timely intelligence on local service performance, outcomes and patient experience without new or extra data collection.
2. Increased understanding of service and outcomes variation by providing easy access to organisation level metrics and data, in an understandable format
3. Improvement in data quality by demonstrating possible problems within data collection at individual centres and allow them to be rectified
4. Provides an evidence base of outcomes and patient experience facilitating investigation of differences which is open and difficult to challenge
5. Allows good practice to be shared and emulated
6. Allows benchmarking against other organisations
These benefits are expected to start to be measurable early 2018/2019.

Outputs:


The Cancer Intelligence Service will use the datasets for different levels of analysis and reporting.
All the below are non-disclosive:
1) Dashboard of performance and out outcomes metrics mapped to all data. Dashboard reports will be aggregated with small number suppression.
2) Dashboard of performance and out outcomes metrics mapped to individual CCGs. Dashboard reports will be aggregated with small number suppression.
3) Audience specific reports (based on aggregate data with small number suppression) with key messages and narrative to aid interpretation of the metrics dashboard.
4) Bespoke investigatory analysis and audit to highlight areas of concern and best practice, help improve patient’s care, reduce unwanted variation and aid decision making.

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 record level data will be linked to the Acute Local Provider flows. All outputs to the CCGs listed will only contain aggregate data with small number suppression. 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) Local Provider Flows where the GM&EC Cancer Vanguard is referenced, received directly from providers)
a. Acute
1. Cancer Outcomes and Services Dataset (COSD)
2. Cancer waiting Times data (CWT)
3. Systemic Anti-Cancer Therapy Dataset (SACT)
4. National Radiotherapy Dataset (RTDS)
Data quality management and pseudonymisation is completed within the DSCRO and is then disseminated as follows:
1) Pseudonymised Local Provider data only is securely transferred from the DSCRO to Arden and Greater East Midlands Commissioning Support Unit.
2) Arden and Greater East Midlands Commissioning Support Unit then apply the following processing on the data:
a. Additional checks for Data Quality issues such as local duplication of records, or adjustments for known North West data recording issues
b. The creation of a number of additional locally derived fields that support further analysis.
c. ‘Localise’ the data where appropriate to support Trust and CCG local reporting capabilities.
3) Arden and Greater East Midlands Commissioning Support Unit then pass the processed, pseudonymised and linkable data to the GM&EC Cancer Intelligence Service hosted by the Christie NHS Foundation Trust. The cancer intelligence service consists of a small team who are all employees of the trust.
4) The Cancer Intelligence Service link the pseudonymised patient-level subsets (i-iv above) datasets to create a single dataset that comprises one single record for each patient’s cancer pathway from referral to post treatment and after care experiences and outcomes.
5) Aggregation of required data for CCG management use will be completed by the Christie NHS Foundation Trust as instructed by the CCG.
6) Patient level data will not be shared outside of the CCGs and its data processors and will only be shared within the Data Controller on a need to know basis, as per the purposes stipulated within the Data Sharing Agreement. Only aggregated reports with small number suppression can be shared externally.