NHS Digital Data Release Register - reformatted

NHS City and Hackney CCG projects

Project 1

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

🚩 NHS City and Hackney CCG was sent multiple files from the same dataset, in the same month, both with optouts respected and with optouts ignored. NHS City and Hackney CCG may not have compared the two files, but the identifiers are consistent between datasets, and outside of a good TRE NHS Digital can not know what recipients actually do.

Project 1 — NIC-99319-F0R8C

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

Legal basis: Health and Social Care Act 2012, Section 251 approval is in place for the flow of identifiable data, National Health Service Act 2006 - s251 - 'Control of patient information'.

Purposes: ()

Sensitive: Sensitive

When:2017.12 — 2019.07.

Access method: Ongoing, Frequent adhoc flow, Frequent Adhoc Flow

Data-controller type:

Sublicensing allowed:

AGD/predecessor discussions: igard_minutes_14_december_2017.pdf, IGARD_Minutes_11_January_2018.pdf

Datasets:

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

Type of data:

Objectives:

"Objective for processing:
This is a new application for the following purposes:
Invoice Validation
The Clinical Commissioning Group (CCG) receives pseudonymised SUS and local provider flows data. These data are required for the purpose of invoice validation and will be used to confirm the accuracy of backing-data sets and will not be shared outside of the CCG. Data cannot be matched on NHS Number as this is not present in the data, but can be used to validate invoices to a level that is acceptable to the CCG. If there is no data in SUS or local provider flows data that can be used to validate the invoice, another data set is used from providers which shows practice / area codes to confirm the patient is from the CCG area in order to pay an invoice.

Invoice Validation is conducted by City & Hackney CCG using pseudonymised SUS and local provider flows.

Risk Stratification
Risk Stratification provides a forecast of future demand by identifying high risk patients. This enables commissioners to initiate proactive management plans for patients that are potentially high service users. Risk Stratification enables GPs to better target intervention in Primary Care primarily using Pseudonymised data.
For the purposes of direct care, GPs have the ability to access SUS data identifiable at the level of NHS number under S.251 CAG 7-04(a)/2013 following explicit action that initiates a re-identification.

Commissioning
To use pseudonymised data to provide intelligence to support commissioning of health services. The pseudonymised data is required to ensure that analysis of health care provision can be completed to support the needs of the health profile of the population within the CCG area based on the full analysis of multiple pseudonymised datasets.
The CCGs commission services from a range of providers covering a wide array of services. Each of the data flow categories requested supports the commissioned activity of one or more providers.
The following pseudonymised datasets are required to provide intelligence to support commissioning of health services:
Secondary Uses Service (SUS)
Local Provider Flows
Acute
Ambulance
Community
Demand for Service
Diagnostic Service
Emergency Care
Experience, Quality and Outcomes
Mental Health
Other Not Elsewhere Classified
Population Data
Primary Care Services
Public Health Screening
Mental Health Minimum Data Set (MHMDS)
Mental Health Learning Disability Data Set (MHLDDS)
Mental Health Services Data Set (MHSDS)
Maternity Services Data Set (MSDS)
Improving Access to Psychological Therapy (IAPT)
Child and Young People Health Service (CYPHS)
Diagnostic Imaging Data Set (DIDS)
The pseudonymised data is required to ensure that analysis of health care provision can be completed to support the needs of the health profile of the population within the CCG area based on the full analysis of multiple pseudonymised datasets.
Processing for general commissioning will be conducted by North East London Commissioning Support Unit
Queen Mary – University of London process data on behalf of the CCG to evaluate clinical outcomes and recommend best practice with regard to long term conditions and other health priorities within the area and securely transfer the pseudonymised patient level output to the CCG

Expected Benefits:

Expected measurable benefits to health and/or social care including target date:
Invoice Validation
Financial validation of activity
CCG Budget control
Commissioning and performance management
Meeting commissioning objectives without compromising patient confidentiality
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:
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.
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.
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.
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.
Better understanding of the health of and the variations in health outcomes within the population to help understand local population characteristics.
All of the above lead to improved patient experience through more effective commissioning of services.

Commissioning
Supporting Quality Innovation Productivity and Prevention (QIPP) to review demand management, integrated care and pathways.
Analysis to support full business cases.
Develop business models.
Monitor In year projects.
Supporting Joint Strategic Needs Assessment (JSNA) for specific disease types.
Health economic modelling using:
Analysis on provider performance against 18 weeks wait targets.
Learning from and predicting likely patient pathways for certain conditions, in order to influence early interventions and other treatments for patients.
Analysis of outcome measures for differential treatments, accounting for the full patient pathway.
Analysis to understand emergency care and linking A&E and Emergency Urgent Care Flows (EUCC).
Commissioning cycle support for grouping and re-costing previous activity.
Enables monitoring of:
CCG outcome indicators.
Non-financial validation of activity.
Successful delivery of integrated care within the CCG.
Checking frequent or multiple attendances to improve early intervention and avoid admissions.
Case management.
Care service planning.
Commissioning and performance management.
List size verification by GP practices.
Understanding the care of patients in nursing homes.
Feedback to NHS service providers on data quality at an aggregate and individual record level – only on data initially provided by the service providers.
Auditing A&E attendances and related hospital admissions
"

Outputs:

Specific outputs expected, including target date:
Invoice Validation
Addressing poor data quality issues
Production of reports for business intelligence
Budget reporting
Validation of invoices for non-contracted events

Risk Stratification
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. In the first instance, GPs have access to pseudonymised patient level data of their own patients however they also have the ability to access NHS number of their patients following explicit action that initiates a re-identification of the pseudonymised NHS number. Any further identification of the patients will be completed by the GP on their own systems.
Output from the risk stratification tool will provide aggregate reporting of number and percentage of population found to be at risk.
Record level output will be available for commissioners pseudonymised at patient level
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.

Commissioning
Commissioner reporting:
Summary by provider view - plan & actuals year to date (YTD).
Summary by Patient Outcome Data (POD) view - plan & actuals YTD.
Summary by provider view - activity & finance variance by POD.
Planned care by provider view - activity & finance plan & actuals YTD.
Planned care by POD view - activity plan & actuals YTD.
Provider reporting.
Statutory returns.
Statutory returns - monthly activity return.
Statutory returns - quarterly activity return.
Delayed discharges.
Quality & performance referral to treatment reporting.
Readmissions analysis.
Production of aggregate reports for CCG Business Intelligence.
Production of project / programme level dashboards.
Monitoring of acute / community / mental health quality matrix.
Clinical coding reviews / audits.
Budget reporting down to individual GP Practice level.
GP Practice level dashboard reports include high flyers.

Processing:

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

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

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

The Data Controller and any Data Processor will only have access to records of patients of residence and registration within the CCG. Access is limited to those substantive employees with authorised user accounts used for identification and authentication.

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

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

Invoice Validation
The Data Services for Commissioners Regional Office (DSCRO), receives a flow of identifiable SUS data from the SUS Repository.
Data quality management and pseudonymisation of data is completed by the DSCRO and the pseudonymised data is then passed securely to North East London Commissioning Support Unit for the addition of any derived fields.
North East London Commissioning Support Unit then passes the pseudonymised data securely to the CCG.
The CCG conduct the following processing activities for invoice validation purposes:
Checking invoiced activity is registered to the Clinical Commissioning Group (CCG) by using the derived commissioner field in SUS and associated with an invoice from the national SUS data flow to validate corresponding records in the backing data flow
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:
In line with Payment by Results tariffs
Are in relation to patients registered with the CCG GPs or resident within the CCG area.
The health care provided should be paid by the CCG in line with CCG guidance.
The CCG are notified that the invoice has been validated and can be paid. Any discrepancies or non-validated invoices are investigated and resolved

Risk Stratification
Identifiable SUS data is obtained from the SUS Repository by North East London Data Services for Commissioners Regional Office (DSCRO).
Data quality management, standardisation and pseudonymisation of the data is completed by North East London DSCRO and the pseudonymised record level data is transferred securely to North East London CSU, who hold the SUS data within the secure Data Centre on N3. 
GP Data identifiable at the level of NHS number is securely sent from the GP system to North East London CSU.
North East London CSU pseudonymises the GP data using the same pseudo ID as SUS, but without requiring the NHS Number to be stored within the CSU.  The pseudonymisation process uses a lookup table containing a salted NHS Number hash and Pseudo Id only (it does not contain NHS Number). 
The lookup table consists of hash values for all possible NHS Numbers and their pseudonyms.  The NHS Number hash is calculated by adding a salt (a secret string of characters) to the NHS Number and applying a cryptographic hash function to get the final hash value.  Each NHS Number has a unique hash value.  The hash function is non-reversible, i.e. for given the hash value it is not possible to mathematically calculate the input value. 
The lookup table will only be used to pseudonymise GP Data for the purpose of Risk Stratification
The pseudonymised GP and SUS data is loaded into the risk stratification tool where the data is linked.
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 GPs access the pseudonymised NHS number of their own patients however, following an explicit action, they also have the ability to access the NHS Number of those patients for the purpose of direct care.   The GP request for the re-identification of a pseudonymised NHS Number is passed to the North East London DSCRO which returns the NHS Number.
Any further identification of the patients will be completed by the GP on their own systems, using the revealed NHS Numbers.
North East London CSU who hosts the risk stratification system that holds SUS data is limited to those administrative staff with authorised user accounts used for identification and authentication.
Once North East London 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:
SUS
Local Provider Flows (received directly from providers)
Acute
Ambulance
Community
Demand for Service
Diagnostic Service
Emergency Care
Experience, Quality and Outcomes
Mental Health
Other Not Elsewhere Classified
Population Data
Primary Care Services
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)
Data quality management and pseudonymisation is completed within the DSCRO and is then disseminated as follows:
Data Processor 1 – North East London Commissioning Support Unit
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 North East London Commissioning Support Unit.
North East London Commissioning Support Unit add derived fields, link data and provide analysis.
Allowed linkage is between the data sets contained within point 1.
North East London Commissioning Support Unit then pass the processed, pseudonymised and linked data to the CCG. The CCG analyse the data to see patient journeys for pathways or service design, re-design and de-commissioning.
Aggregation of required data for CCG management use will be completed by North East London Commissioning Support Unit or the CCG as instructed by the CCG.
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 – Queen Mary – University of London
Pseudonymised SUS only is securely transferred from the DSCRO to North East London Commissioning Support Unit.
North East London Commissioning Support Unit add derived fields.
North East London Commissioning Support Unit then pass the pseudonymised and linked data to the Clinical Effectiveness Group within Queen Mary University of London
Queen Mary University of London process the data on behalf of the CCG to evaluate clinical outcomes and recommend best practice with regard to long term conditions and other health priorities within the area and securely transfer the pseudonymised patient level output to the CCG.
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.