NHS Digital Data Release Register - reformatted
LANCASHIRE & SOUTH CUMBRIA NHS FOUNDATION TRUST projects
- A single consolidated new request for commissioning purposes - Lancashire Care NHS Foundation Trust (Hosting the Innovation Agency)
10 data files in total were disseminated unsafely (information about files used safely is missing for TRE/"system access" projects).
A single consolidated new request for commissioning purposes - Lancashire Care NHS Foundation Trust (Hosting the Innovation Agency) — DARS-NIC-79728-X2C2X
Type of data: information not disclosed for TRE projects
Opt outs honoured: 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(2)(b)(ii)
Purposes: No (NHS Trust)
When:DSA runs 2019-04-01 — 2020-03-31
Access method: Ongoing
Data-controller type: LANCASHIRE & SOUTH CUMBRIA NHS FOUNDATION TRUST
Sublicensing allowed: No
- SUS for Commissioners
The Connected Health Cities Programme has the objectives of creating a Learning Health System for three pathways in the northwest coast - COPD, Alcohol and Seizures. They have received an extension to the programme until March 2020. To support the functions of the Connected Health Cities programme, and the development of the Learning Health System they are requesting an extension to their data sharing agreement and the ongoing data flow. In extending the programme and using additional data, it will enable them to gain a richer picture using more historical data and allow them to create more accurate data models to evaluate the efficacy of a learning health system.
The objectives of their programme remain the same:
1) Support the development and delivery of innovative information models and algorithms to front-line staff in timely ways that enable them to better plan, review and adjust the care they offer.
2) Support the development and delivery of innovative information models and algorithms to front-line staff in timely ways that enable them to develop and monitor new or more effective ways of care.
3) Develop models for connecting and engaging people with expertise and experience from across health, social and local government etc to turn data into actionable information and knowledge.
4) Harness the power of data by collecting, linking and collating data securely
Use data that improves understanding of healthcare efficiency and effectiveness and enables new techniques and organisational forms to be tested
Lancashire Care NHS Foundation Trust require pseudonymised SUS for Commissioners data for the purpose of commissioning. Previously the data was provided monthly but, going forward, is required on a quarterly basis.
Lancashire Care NHS Foundation Trust host the Innovation Agency (formerly the North West Coast Academic Health Science Network) as the Innovation Agency are not a legal entity. The Innovation Agency lead on the North West Coast Connected Health Cities (NWC CHC) Programme. To support the functions of the CHC programme, the Innovation Agency (Lancashire Care NHS Foundation Trust) provides honorary contracts for employees from the University of Liverpool and Lancaster to conduct required analytics as members of the NWC Connected Health Cities Analysis Team. In addition, as described above, they also require the expertise of Lancaster University as a data processor.
No other organisations other than those stated will be processing or accessing data provided by NHS Digital.
Lancashire Care NHS Foundation Trust are requesting access to pseudonymised record level data for the purpose of commissioning, by enabling analysis to be undertaken that will support the development of operational algorithms that can promote improved service understanding and delivery, with a particular focus on two specific clinical pathways: Alcohol and Emergency Care (Emergency Care focusing on Chronic Obstructive Pulmonary Disease /COPD and Epilepsy); in the demonstrator phase of this programme.
This work will build on projects that have already been delivered in relation to the two specific clinical pathways and aims to ensure that outputs from those projects can be translated into operational analysis models, and algorithms, that will improve the identification and management of patients suffering from the clinical conditions covered.
This application will enable the NWC CHC Programme to test and define the CHC programme processes. The programme will cover the North West Coast region, so requires data that covers the entire North West Coast CCG Footprint.
The CHC Programme aims to:
- Support the development and delivery of innovative information models and algorithms to front line staff in timely ways that enable them to better plan, review and adjust the care they offer.
- Support the development and delivery of innovative information models and algorithms to front line staff in timely ways that enable them to and develop and monitor new and/or more effective pathways
- Develop models for connecting and engaging people with expertise and experience from across the health, social, local government, voluntary, commercial and public sectors to turn data into information into knowledge
The request is for the area included in the footprint of North West Coast, Clinical Commissioning Groups.
The Programme intends to make use of the SUS data by focussing on two high profile areas as demonstrators - alcohol misuse and unplanned admissions (primarily from COPD and epilepsy) - and helping frontline teams support the STP and other NHS effectiveness programmes. University clinicians will work with local clinicians and other frontline staff to improve these care pathways. Other members of the University team will work with local Business Intelligence staff to make this work sustainable. Importantly, the Innovation Agency is working to ensure patient and public support for The Programme. More detailed information is available at: https://www.connectedhealthcities.org/connected-health-cities/north-west-coast/
By using the SUS data, the project team has gained a clear understanding around three pathways - alcoholic liver disease, COPD and epilepsy to provide information to inform actions on emergency and unplanned admissions. In particular, generating alcohol reports detailing the profiling of admissions, patient characteristics and patient care pathways. This project is already generating insights which are being picked up by regional quality improvement teams and the team is hoping to feed insights into Public Health England Intelligence teams, so that these methods are impactful at a national level. This work with individual Trusts is ongoing. The epilepsy reports detailing management of seizure patients within A&E and onward referral to neurology clinics is influencing services and how they manage seizure patients. The team is looking to evaluate how the findings have improved outcomes for these patients. The work is also influencing the National Seizures Audit Team. Reports on COPD indicating unplanned and emergency admissions are looking at how the team can support local services to improve management of COPD patients. This forms part of regional objectives. All reports have been circulated to key clinicians in the North West Coast region and feedback collected as part of the implementation of a learning health system. The team has developed the technical tools and algorithms which can extract data and models to analyse and gain insights from the data. The models will be better refined with more data and will be able to identify where services have been improved or developed. The team is currently working towards supporting regional teams to embed the work across and working with individual trusts and CCGs as well as health systems. Extending the data flows to support this project until 2020 will ensure that the team is able to refine the tools as well as look at the impact of services and changes to services over a longer time period to demonstrate the impact on patient outcomes. The aim is to have individual regions able to use these tools and techniques by 2020. As well as working on reports for COPD admissions, patient characteristics and care pathways, analysis of the SUS data for Wirral has enabled the team to identify COPD hotspots and to offer an intervention tool at GP practices. SUS analysis in future will allow the team to assess the impact of this intervention. Hotspots have also been identified for the Preston area where the same intervention is being planned.
The CHC Demonstrator project will pilot new fluid and flexible Intelligence models, rapidly sourcing, managing and mining bigger quantities of pseudonymised data.
By the project end:
A demonstrator will have been produced which provides:
- algorithms, tools and models which have facilitated improvements to clinical pathways for the two chosen areas and which will drive future pathway improvements
- mechanisms which will improve the quality, depth and consequent value of future data reporting.
Algorithms, tools and models will be made available to organisations such as CCGs, STPs and CSUs.
By developing algorithms and models using pseudonymised data that covers the entire population of the North West Coast region as opposed to a single CCG footprint, Lancashire Care NHS Foundation Trust and the Connected Health Cities programme will be able to develop analysis outputs to robustly ensure that factors such as demographic spread (e.g. from rural to urban districts), socio-economic variance and geographic cultural differences can all be assessed and accommodated in the prototype models - effectively a learning health system. It will also ensure that analysis which investigates diseases and disabilities that are not common, will be able to utilise a larger cohort than would be possible within single, local health economies. In addition, the Trust can incorporate benchmarking methodologies into the analysis outputs to ensure the tools provide a deeper view on variation across the region.
From a technical perspective, the output model can be refined to incorporate local anomalies and be better designed to address specific issues in specific localities, therefore ultimately offering more benefit to the health organisations across the region. In practical terms, developing tools capable of delivering rich intelligence once at scale will also provide design models for more efficient Intelligence delivery processes in the future.
The resulting algorithms, tools and models are being made available to organisations such as STPs, CCGs and CSUs.
Outputs to date:
- Core datasets, outcome measures and metrics for the selected pathways are defined
- Opportunities to use novel data linkage or analysis to improve intelligence about the progress of patients along the chosen pathways and communication between services is identified
- Work areas for analysts to investigate service improvement and re-design informed by core datasets have been identified
- Documented results from open sources of data that are available (e.g. alcohol sales or events information to predict demand for emergency services, or social media to understand patient experience) to further inform either local and regional policy development or new intelligent indicators to guide clinical service development and design. Demographic data overlays the three linked data sets to identify - emergency and unplanned admission hot-spots.
- Codes were developed with clinical teams to ensure that the correct cohorts of data is captured.
- Aggregated reports with outputs and algorithms capable of identifying service variation and granular clinical cohorts have been delivered to NHS Trusts and commissioners.
- Multi-dimensional analysis models have been built on consistently pseudonymised and linked data, tailored to the precise needs of a wide range of operational managers and clinicians.
- High level data models, and infrastructure design models, for managing pseudonymised datasets at scale are available to support more robust service evaluation and planning within the selected Clinical Pathways.
- Algorithms built on the CHC pseudonymised data collections to identify, categorise and monitor granular patient level cohorts have been tested to Clinicians and professionals working within the specific clinical areas
The first tranche of these algorithms was completed and shared. These will be further developed during the rest of the project.
- Algorithm testing and validity reports will have been delivered to provide assurance around the information governance, statistical and technical approaches utilised by the programme
Development of the principles of a learning health system using these data sets will also investigate the impact of analysing data in this way and providing information/reports to commissioners and clinicians in producing actionable insights from the data.
Work ongoing from March 2019-March 2020 includes:
Development of a Predictive analytics tool for key clinical themes, which is currently being demonstrated to key stakeholders - this work already started this in 2018, but the intention is to continue this with key stakeholders across the region. This work will involve working with emergency and unplanned admissions leads at STP level in 2019-20 and investigating how this approach to the development of this Learning Health System can be possibly extended to other pathways and utilised within existing systems. Additional data will continue to refine and validate this work. The longer the period of data the team has access too, the greater the validity of the model.
Documenting the impact of interventions on the learning health system in a key geographical area of the North West Coast for COPD, alcohol and seizures. These models will be further refined with the additional flow of data until March 2020 adding to the robustness of findings and as well enabling the team to better describe the impact of services over time.
Publish papers on the three key themes describing the algorithms, the methodology of the learning health system and the impact of any interventions implemented or proposed on the pathways under study. Papers are underway with some publications made, and using data until March 2020 will allow the team to produce a more robust project evaluation document including final algorithms and outcomes.
By March 2020 the team will have developed a robust academically validated tool-kit which has been disseminated widely to north west coast commissioners (and others), STP and business informatics teams to demonstrate the tools and techniques in developing a learning health system in a number of pathways and services over a 7 year time period.
1) North West Data Services for Commissioners Regional Office (North West DSCRO part of NHS Digital) receives a flow of identifiable SUS data.
2) Data quality management and pseudonymisation of data is completed by North West DSCRO and the pseudonymised data is then passed securely to Arden and GEM CSU. Arden and GEM CSU then apply the following processing on the data:
Additional checks for Data Quality issues such as local duplication of records, or adjustments for known North West data recording issues
The creation of a number of key additional derived fields that support Business Intelligence delivery in the North West region. (e.g. New to Follow Up Outpatient Ratio construction, Readmission Rate construction, Alcohol Related Admission derivations and others)
Localise the data to support Trust and CCG local reporting capabilities (e.g. the construction of Point of Delivery [PODs] classifications which are used across the North West as a sub-classification for reporting local Payment by Results (PbR) and non-PbR activity.)
3) Arden and GEM CSU then pass the processed, pseudonymised data to the secure AIMES CHC Data Warehouse environment for the addition of derived fields.
4) AIMES CHC Data Warehouse will then make the processed, pseudonymised data available to the following data processors via their secure VPN:
a) University of Liverpool analysts with Lancashire Care honorary contracts.
b) Lancaster University.
Access is via secure VPN and analysis of the data is to identify patient cohort journeys for pathways or service design, re-design and de-commissioning.
5) Patient level data will only be shared with those organisations as specifically stated above. Only aggregated reports with small number suppression can be shared externally.
AIMES may only process the data as defined within this agreement, and the data controller will ensure that a robust agreement is in place with AIMES .
Once data is sent from Arden and GEM CSU, the data will be deleted and not held on the CSU servers. Arden and GEM CSU will not share data with any organisations other that those listed within the Data Sharing Agreement.
Data will be accessed by
- named analysts which includes University of Liverpool staff members where a signed honorary contract is in place
- analysts at Lancaster University. Lancaster University is a Data Processor as stipulated within this agreement.
Data must only be used as stipulated within this Data Sharing Agreement.
Data Processors must only act upon specific instructions from the Data Controller.
The Data Controller confirms that the Data Processors listed within this agreement have each:
- Confirmed that they understand their roles and responsibilities on behalf of the Data Controller as defined within the Data Sharing Agreement.
- Confirmed that the Processing Activities described within the Data Sharing Agreement are accurate and achievable in terms of the particular Data Processors processing.
No record level data will be linked other than as specifically detailed within this application/agreement. Data will only be shared with those parties listed and will only be used for the purposes laid out in the application/agreement. The data to be released from the NHS Digital will not be national data, but only that data relating to the specific locality of interest of the applicant.
The pseudonymised record level data (anonymised in accordance with the ICO Anonymisation Code of Practice) may contain:
Pseudonymised NHS number only
Year of Birth or Age only
Year of Death or Age only
Lower Super Output Area only
Where the Data Controller and or/its Data Processor is receiving pseudonymised data, the Local Patient Identifiers (Local Patient Identifier and Local Event Identifier) can flow, where strictly required, in identifiable form.
All organisations party to this agreement must comply with the Data Sharing Framework Contract requirements, including those regarding the use (and purposes of that use) by Personnel (as defined within the Data Sharing Framework Contract i.e. employees, agents and contractors of the Data Recipient who may have access to that data).