NHS Digital Data Release Register - reformatted

Manchester University NHS Foundation Trust

🚩 Manchester University NHS Foundation Trust received multiple files from the same dataset, in the same month, both with optouts respected and with optouts ignored. Manchester University NHS Foundation Trust may not have compared the two datasets, but the identifiers are consistent between datasets for the same recipient, and NHS Digital does not know what their recipients actually do.

Project 1 — DARS-NIC-401890-W6Q8W

Opt outs honoured: No - data flow is not identifiable (Consent (Reasonable Expectation))

Sensitive: Sensitive, and Non Sensitive

When: 2021/05 — 2021/05.

Repeats: One-Off

Legal basis: Health and Social Care Act 2012 – s261(2)(c)

Categories: Anonymised - ICO code compliant

Datasets:

  • Emergency Care Data Set (ECDS)
  • Hospital Episode Statistics Outpatients
  • Hospital Episode Statistics Admitted Patient Care
  • Civil Registration - Deaths
  • Hospital Episode Statistics Accident and Emergency

Objectives:

The primary purpose of the TRIAGE study is to establish the workload burden associated with a newly developed clinical pathway called “Triage-HF Plus”. This pathway uses a risk score generated from implanted cardiac devices (Heart Failure Risk Score “HFRS”) to guide a remote monitoring service – where patients identified at ‘high’ risk of decompensated heart failure are telephoned by clinical staff in an attempt to 1) diagnose and treat issues early in the course of illness and 2) reduce risk of hospitalisation. An important part of evaluating this pathway is to establish the burden of implementing the pathway on the provider – an essential part of health economic analysis. Secondary objectives of the TRIAGE study include exploration of the relationship between clinical pathway and events (healthcare utilisation and mortality), specifically: 1) Comparison of frequency and type of clinical touchpoints and patient outcomes pre-Triage-HF Plus (current state) and post-Triage-HF Plus Implementation (future state, prospective). 2) Estimation of cost of Triage-HF Plus care pathway at each site 3) Exploration of relationships between Triage-HF with patient demographics and heart failure events 4) Exploration of relationships between Triage-HF with patient demographics and non-heart failure events such as frailty The TRIAGE study follows on from the research project entitled ‘Triage-HF Plus: Cardiac Implantable Electronic Device Remote Monitoring Combined with Telephone Triage to Identify and Manage Worsening Heart Failure’, hereafter referred to as ‘Retrospective TRIAGE’. ‘Retrospective TRIAGE’ was a retrospective service evaluation project evaluating the predictive efficacy of the HFRS. This evaluation piece was supported by a Section 251 approval. The TRIAGE study follows on from this piece of work – a prospective study with informed consent from each participant. For the TRIAGE study, patients are being recruited from three hospital sites across North West England, with an initial recruitment target of a single cohort of 450 participants. The first patient was recruited in September 2019. Inclusion criteria identify patients 18 years or older with a compatible Medtronic cardiac device. Patients must be able to provide written, informed consent in the English language. Data from NHS Digital is requested to provide pseudonymised record-level information about both healthcare utilisation and mortality for each participant, allowing evaluation of the efficacy of the Triage-HF Plus pathway. Healthcare utilisation data will be provided from the Emergency Care Data Set (ECDS), Hospital Episode Statistics (HES) Admitted Patient Care (APC), HES Accident & Emergency (A&E) and HES Outpatients data. Civil Registration (Deaths) Secondary Care Cut will provide the required mortality information. NHS Digital HES data is required because many participants’ local hospitals will be elsewhere in the region and the local data will not provide the necessary complete set of information. There are no less intrusive ways of achieving this data reliably. It is not feasible to rely on patients to remember dates of hospital attendances. Data requested will be minimised to the 24 months prior to, and up to 3 years after date of recruitment for each participant. Clinical data requested has been reviewed by 2 clinical researchers at Manchester University NHS Foundation Trust (MFT - previously known as Central Manchester NHS Foundation Trust), and the University of Manchester data analysis team. This is to ensure that the data is both minimised and sufficient for achieving study outcomes. Data is required to be record-level in order to perform a meaningful analysis. In order to undertake a thorough analysis of workload burden pre- and post- implementation of the Triage-HF Plus pathway, data are required for all healthcare utilisation touchpoints. Although the pathway is centred on data from cardiac devices, previous research has suggested that a high HFRS may represent non-cardiac illnesses and physiological disturbances from procedures/operations, potentially leading to both useful clinical warnings, and 'false positives' creating extra unnecessary workload. Information relating to any of the clinical classification codes from 'International Statistical Classification of Diseases and Related Health Problems 10th Revision' (ICD-10 - for diagnoses) and 'OPCS Classification of Interventions and Procedures' (OPCS4), as well as all 'Secondary Use Service' codes (hospital payment information for carrying out different services) are therefore requested for the cohort in order to address the study hypotheses. The TRIAGE study is a stand-alone study. It is not part of a wider project or collaboration. No follow-up work is currently planned. NHS Digital data collected as part of this application will be used as described above for the TRIAGE study and for potential inclusion in an affiliated PhD project only. The PhD candidate is a substantive employee of MFT who is completing their thesis as a student of the University of Manchester. MFT are the sole data controller for this study. MFT and the University of Manchester will be joint data processors. Medtronic Limited are the funder and commercial partner for the study. They provide device data to MFT via the One Hospital Clinical Service platform. Medtronic have no access to record level NHS Digital data and have no influence over study results or outputs generated. They will have access to aggregated data with small numbers suppressed. Medtronic may benefit from the study if results show that the associated Triage HF Plus clinical pathway reduces NHS workforce burden, and guidelines change to support the uptake of this pathway. TRIAGE is sponsored by MFT in partnership with Medtronic Limited. Pennine Acute Hospitals NHS Trust and Wythenshawe hospital will provide cohort identifiers to MFT to send to NHS Digital. These organisations are involved in the clinical evaluation of patients within the overall study but do not have access to NHS Digital data. The Article 6 GDPR justification for processing is 6(1)(e): ‘processing is necessary for the performance of a task carried out in the public interest or in the exercise of official authority vested in the controller’ The collection of data from NHS Digital is essential for the completion of the TRIAGE study. This is in the public interest as: (1) results from the TRIAGE study will advance academic and clinical knowledge and understanding of workload burden associated with, and efficacy of the Triage-HF Plus care pathway, (2) results from the TRIAGE study will in likelihood lead to either further clinical studies or direct change in clinical service provision with the aim of improving patient care and (3) all participants have expressed willingness of their data to be used as part of the TRIAGE study to advance academic knowledge. The Article 9 GDPR justification for processing is 9(2)(j): ‘processing is necessary for archiving purposes in the public interest, scientific or historical research purposes or statistical purposes in accordance with Article 89(1) based on Union or Member State law which shall be proportionate to the aim pursued, respect the essence of the right to data protection and provide for suitable and specific measures to safeguard the fundamental rights and the interests of the data subject.’ Data processing is in the public interest for the benefit of health and social care. The study is sponsored and being run in NHS hospital sites, and results will feed directly into NHS clinical improvement projects. Safeguards to protect data subjects are in place as per MFT data security and data protection policies.

Expected Benefits:

The main benefit of the TRIAGE study will be a greater understanding of the workload associated with the Triage-HF Plus Clinical Pathway. Previous work has demonstrated that the heart failure risk score used to guide the pathway can effectively identify heart failure patients at increased risk of hospitalisation and death. This study will provide information to describe practical implications of implementing the pathway in clinical practice – required by healthcare providers to evaluate the risks and benefits of changing clinical workflows. If results show low workforce burden, this may contribute to a change in national guidance, for example upgrading recommendations to more strongly support remote monitoring is incorporated into routine clinical practice. For example, remote monitoring may replace traditional face-to-face clinical review in specified circumstances. If results show high workforce burden, this may contribute to reconfiguration of clinical services. This will benefit patients by improving patient care, and the NHS by evaluating the best service delivery. We would anticipate new sites would take 2-3 years to fully implement the Triage HF Plus Pathway and evaluate significant new changes. Improved knowledge of the clinical, organisational and financial implications of implementing the Triage-HF Plus clinical pathway will be of benefit to health and social care as outlined below: Researchers: Improved knowledge in this research area opens new avenues of research within the field of device diagnostic/heart failure research. As outlined above this may justify modifying clinical pathways and facilitate the rapid scale up implementation of this pathway across the UK. Publication and dissemination of data is expected to commence in October 2023. Clinicians: TRIAGE will provide greater understanding of how implementation of the Triage HF Plus Clinical Pathway would affect how individual providers and clinicians serve their patients. Results may influence other Cardiology clinical departments both national and international in terms of how they integrate remote monitoring into their workstreams. Target date: Dissemination of work to improve knowledge of relevant health professionals: October 2023. Patients: This work will hopefully lead to improved patient care. This may not occur as a direct result of this evaluation, rather further down the line as a results of service improvement projects.

Outputs:

When processing is complete, outputs will include: 1. Submission to peer review journals (for example Circulation, European Heart Journal, European Journal of Heart Failure) 2. Presentations at: a) Internal meetings at Manchester University NHS Foundation Trust (MFT), the University of Manchester (UoM) and Medtronic. b) Seminars and workshops at academic events (where appropriate) c) National and international academic conferences (for example Heart Rhythm Society, Heart Rhythm Congress, European Society of Cardiology). A member of the study team will present the results of TRIAGE during at least one international cardiology congress. d) Patient engagement events e.g. open lectures and talks (http://www.engagement.manchester.ac.uk/about/index.html) e) Local meetings, held in Greater Manchester, will be attended by clinicians, healthcare practitioners, healthcare scientists, clinical managers, heart failure charities and patients. 3. A lay summary of results for patients and interested non-academic parties disseminated via: a) Research Webpage for Manchester Heart Centre. b) Letters/emails to study participants c) MFT, UoM and other publicly accessible websites (e.g. Medtronic) d) Results may be disseminated in the form of progress reports for interested parties 4. A PhD thesis by co-principal investigator at MFT and British Heart Foundation Clinical Research Training Fellow at University of Manchester/ MFT. Data originating from NHS Digital may be included in this PhD thesis which is a published document. The target time for release of these outputs is October 2023. No data presented will be identifiable. Aggregate level data with small numbers suppressed will be presented in all outputs in line with the HES analysis guide to avoid risk of re-identification. For example, any data with less than 7 patients will not be shown. Through the outputs stated above, the results of this evaluation will be disseminated to a target audience of researchers, data scientists, academic groups, innovative technology-focused organisations and research participants. All involved organisations have established links with a wide variety of communities and will help ensure output is brought to the attention of policy makers and industry collaborators. Interested audiences will be health professionals but will not be restricted i.e. results will be available to the public. MFT has a research collaboration agreement in place with Medtronic outlining data and knowledge ownership and access rights. Results will be shared with all stakeholders (including Medtronic) but Medtronic will only see aggregated outputs of the NHS Digital data with small number suppression. Stakeholders will have no influence on the evaluation results.

Processing:

The data flows will be as follows: Information provided to NHS Digital will be record-level identifiable patient data. Personal data for each study participant will be provided to NHS Digital by Manchester University NHS Foundation Trust (MFT). Data transfer will occur via an encrypted data transfer service. This data will be Study ID (unique 6-digit number “study participant number/SPN”), the recruitment date for each patient into the trial, and personal identifiers only: NHS Number, Date of Birth, Sex, Postcode. Linked pseudonymised NHS Digital health data (Emergency Care Data Set (ECDS), Hospital Episode Statistics (HES) Admitted Patient Care (APC), HES Accident & Emergency (A&E), HES Outpatients data and Civil Registration (Deaths) Secondary Care Cut) will be provided to MFT via an encrypted transfer system. Access to confidential information at MFT is restricted to on-site computers or via remote access. On and off-site, access requires trust usernames and passwords to access the MFT server. NHS Digital data will be stored within a specified password-protected sub-folder, for which access is limited to a small number of specified authorised personnel on the TRIAGE study team who are substantive employees of MFT and have been appropriately trained in data protection and confidentiality. The received NHS Digital data will be checked and cleaned/formatted at MFT. This will occur either on-site or via secure remote access in line with national UK Government guidance during the COVID-19 pandemic to work from home wherever feasible. Off-site remote access is facilitated via VMware Horizon using RSA software tokens. In addition to a unique time-sensitive username and password required to access the remote access portal, this process also requires the same security processes as per on-site access. It is not possible to download any data from remote access onto external computers. This arrangement is in line with the NHS Digital Data Access Request Service Temporary Remote Access Guidance. After checking and cleaning of the NHS Digital data, it will be combined with other evaluation data (cardiac device data from Medtronic One Hospital Clinical Service platform and data from case report forms) using each participant’s SPN only to create a complete pseudonymised research database for analysis. It will not be linked with any other databases. In order to facilitate the movement of data out of MFT to the University of Manchester (UoM), the SPN-coded database will undergo an additional ‘sense check’ to ensure no participant could be identified and all non-essential information for the purpose of analysis is removed. Any data leaving the MFT will be reviewed by the study Chief Investigator to ensure compliance with local guidance, approvals, and legislation. The SPN-coded database will be shared via encrypted transfer service with trusted academic collaborators based in the Centre for Health Informatics at the UoM, who will be appointed to undertake data analysis as explained to study participants in the Patient Information Sheet. The SPN-coded database will be transferred into the UoM Data Safe Haven (DSH) and remain in the DSH for all analysis. There will be no attempt to re-identify individual participants. Analysis of the linked de-identified data will assess the workforce burden associated with the pathway by comparing healthcare utilisation (HCU) before and after implementation of the clinical pathway, assess health economics pre and post-implementation, and involve exploration of a number of secondary objectives as previously described. NHS Digital data regarding (1) accident and emergency attendances (2) admitted care episodes, (3) outpatient attendances and (4) mortality are required to perform a meaningful analysis which will influence provision of care in the future. The DSH is purpose built for the secure management and processing of personal, special category and confidential information. Access to the DSH is only granted to a limited number of named researchers who are substantive employees of the UoM, have completed the University’s Data Protection Training and are authorised by the Research Governance, Ethics and Integrity team (RGEIT). Each DSH user has a project file within the DSH, which is accessible to that user only. Access to the DSH is restricted to on-campus connectivity only. Only named individuals are authorised to move data in or out of the DSH, under specific authorisation of a named information governance lead of the project. No personnel outside the direct employment of the UoM or MFT will have access to the SPN-coded database containing record level NHS Digital data. The SPN-coded database is downloaded into the DSH via a single point - a static IP addressed laptop within the RGEIT office. After logging on to the laptop with their UoM username and password, the named academic analysts will access the DSH using the University’s 2-factor authentication Service (Duo) to verify their identity. SPN-coded data will be transferred into a password-protected sub-folder in the principal investigator’s project folder within the DSH using the Secure Transfer Service. File transfers, copy/paste and print functionality has been disabled within the DSH. Record level data will not leave the DSH/MFT secure servers. Data analysis will be performed using software within the DSH (mainly Microsoft Excel, SPSS, R, GraphPad). Data will be stored in the DSH until one year after the final data dissemination, anticipated October 2024, providing enough time to complete all analysis and publish study findings. A valid data sharing agreement will be maintained until all such analysis has been completed. Remote access to the UoM DSH is via Virtual Desktop Infrastructure technology to ensure the data is only processed within, and never leaves the DSH virtual environment. Due to this ‘painted screen’ approach, no remnants of the data are ever stored on the client device through mechanisms such as temp files or browser caches. All other data releases will be made in line with HES analysis guidance, aggregated with small numbers under 7 suppressed. No NHS Digital data will be transferred outside the UK.


Project 2 — DARS-NIC-384524-C7M2Q

Opt outs honoured: No - data flow is not identifiable

Sensitive: Non Sensitive

When: 2016/09 — 2018/12.

Repeats: Ongoing, One-Off

Legal basis: Health and Social Care Act 2012, Health and Social Care Act 2012 - s261 - 'Other dissemination of information'

Categories: Anonymised - ICO code compliant

Datasets:

  • Hospital Episode Statistics Critical Care
  • Hospital Episode Statistics Admitted Patient Care

Objectives:

NHS England has established a Clinical Reference Group (CRG) for burn care services. The CRG includes representatives for areas across England – specifically: North East, Greater Manchester, Cheshire and Mersey, Yorkshire and The Humber, West Midlands, East Midlands, East of England, London NW, London NE, London S, South West, Wessex, Thames Valley and the South East Coast. Specialist burn care services include all burn care delivered by Burn Centres, Burn Units and Burn Facilities delivered as part of a provider network. As a member of the CRG, University Hospital of South Manchester (UHSM) has been commissioned by NHS England to ensure that burn service capacity is adequate for demand across the NHS and specialised services are placed as required in different parts of the country. UHSM is responsible for maintaining the National Burn Registry (NBR) database which is a clinical database containing patient identifiable data on all hospital admissions due to (or including) burns and of the patient treatment and care for each episode. Care providers across England and Wales have an obligation to provide data on burn injuries and the course of treatment given. UHSM use HES data to ensure that the data received from the burn care centres is accurate within agreed thresholds and falls within the scope of data that is required. The scope of the service includes all acute care, rehabilitation and reconstruction. For this reason, UHSM requires HES data that includes plastic surgery codes (160) as well as burn care codes as care is often coded under the specialty rather than burn care (161). It also includes care for severe dermatological skin loss conditions which is why these conditions are included. HES Critical Care data is required as this will show the level of care patients will have received and is within the scope of level of data that burn centres are required to submit to include on the National Burn Registry database. Critical Care is required to ensure that burn care centres are submitting the relevant data to UHSM. It will also allow capacity changes that are expected by NHS England to be necessary in paediatric critical burn care to be modelled. UHSM currently holds HES data from 2002/03-2010/11. This data has previously been used by UHSM to assess the work of the National Burn Care Group, including the designation as specialised services. Further changes to the provider service profile after 2010 need to be assessed using more recent data which will also allow a volume validation against the NBR database. On receipt of new HES data, UHSM will compare the two in order to identify and assess changes to care providers’ service profiles (e.g. age breakdown, length of stay, etc.). Due to changes in the profile the 2002/03-2010/11 data can be compared with the more recent data both for the HES dataset and the Burns registry after processing. Once satisfied that the data requested is correctly assigned to the relevant fields UHSM will destroy the 2002/03-2010/11 data. Data destruction will be completed according to HSCIC guidelines. The data will be destroyed by March 2017 at the latest. UHSM’s role is to ensure that the NBR is an accurate and complete reflection of burns data. Where discrepancies are identified, UHSM notifies the provider and CRG and monitors to ensure corrective action is taken. Providers may challenge or query UHSM’s findings. UHSM may then support providers in identifying the reasons for discrepancies. In doing this, UHSM might provide aggregated figures highlighting specific areas of discrepancy (e.g. age breakdown, length of stay, etc.). Outputs may compare volumes of episodes in HES and the NBR for specific providers but no record level HES data is shared with third parties and the aim is to identify categories rather than individual episodes. Any outputs would contain aggregated data with small numbers suppressed in line with the HES Analysis Guide. UHSM may also receive challenges or queries from NHS England. UHSM will respond to queries using the NBR database but may require HES data to validate findings. In such instances, the outputs will be reports on volumes, potentially categorised by profile (i.e. age breakdown, length of stay, etc.) and no record level HES data will be shared with any third party. UHSM needs to retain the HES data for use in such activities for a rolling period of up to 2 years so that UHSM may run additional completion and quality checks within the timeframe.

Expected Benefits:

The National Burn Registry is used to determine and report the following; • The extent of specialised burn services compliance with their Service Specification. This is delivered annually and due in April. • Quantification of the demand and capacity trends for burn care 2003-2015 to inform the commissioning of service in line with the burn care CRG Strategy 2015-19. This is delivered annually and due in September. These are beneficial to healthcare as CRG use them to make informed recommendations to NHS England in order to ensure that resources are used effectively throughout England and Wales. The intention is to design a sustainable series of burn services throughout the NHS to support safe and appropriate care for this unpredictable emergency workload by use statistical process control (SPC) techniques to look at long term data to compensate for the variation in demand and evaluate the capacity requirements both geographically and at differing levels of provision. The results will enable the distribution of funding to each Trust to ensure that burn service capacity meets the demand across the NHS and specialised services in burn care are placed as required in different parts of the country. Using the HES data will ensure that the calculations are correct for each specific centre and that the results are as accurate as possible. The value of the HES data is in making sure the NBR data is complete/accurate and improving the quality of data collection. These benefits support the wider benefits to health care achieved by using the NBR data. Benefits achieved using the HES data previously supplied have included the recognition of the need for the development of additional Burn facilities in the South East of England and Midlands. See: http://www.londonhp.nhs.uk/publications/london-and-south-east-england-specialised-burns-project/.

Outputs:

The outputs of using the HES data are: 1. Analysis/verification of accuracy and completeness of data in the NBR database. This is undertaken annually. 2. Where discrepancies between the NBR and HES data exceed agreed thresholds, UHSM reports to NHS England via the CRG details of provider(s) responsible for the discrepancies in order for the provider(s) to resubmit data to the NBR with the relevant fields. Should UHSM need to raise discrepancy issues with NHS England than it is simply to point out any numeric mismatches between the HES data analysis and analysis of the NBR database. The two data sources will never entirely match but if the levels of discrepancy exceed agreed thresholds then it is only the simple raw numbers as a tabulated comparison that will be presented to the CRG and NHS England commissioners. It will be at a very simple level because CRG and NHS England only wish to be assured that the NBR database is an accurate reflection of activity and can thus be relied on. Analysis of the HES data is the only form of validation available. 3. Statistical outputs for use in supporting care providers in analysing causes of discrepancies in order to improve completeness of reporting and accuracy of the NBR database. This is undertaken as required on a rolling annual basis. 4. Statistical outputs to be used in responses to specific queries by NHS England. This is undertaken as required. As an example, NHS England intends to undertake specific pieces of work regarding severe paediatric burn injury and providing a highly specialised service for complex skin failure for adults and children. UHSM will be involved in undertaking capacity on demand analysis for these processes using the NBR database with HES validation as required. UHSM expects it will take 6-12 months to complete initial analysis and follow up queries/challenges from NHS England. Following the data completeness checks with the HES data, UHSM reports the results of the analyses of the NBR to the burn care CRG. These results form the basis of the CRG’s recommendations to NHS England concerning service provision to fit in with their strategy for burn care 2014 to 2019. The analysis will identify mismatches between capacity to deal with burns and demand for such services at a national level in addition to geographical areas of mismatch of demand and capacity. The NBR outputs are sent directly to CRG only. These are reported to the CRG in their quarterly meetings and put in the annual report and used as the basis of their recommendations to NHS England. CRG’s outputs will be available to the public, free of charge.

Processing:

UHSM receive extracts of HES data filtered to specific diagnosis codes indicating burn injury, plastic surgery and care for severe dermatological skin loss conditions. UHSM further processes the received data to create a reduced cohort that correlates with NBR inclusion criteria. This process of reduction requires analysis that could not be automated by the HSCIC. UHSM then uses the reduced HES data to verify the accuracy of the NBR data as a representation of burn injury admissions for acute care, for rehabilitation and for late reconstruction. The HES data is compared to data in the NBR and forms the basis of completeness and quality checks. The HES data will not be linked to NBR data or added into the NBR database. Comparison of the workload volumes in the HES data is made against NBR database outputs to ensure that the overall activity numbers and bed days are within acceptable limits. Once the NBR data is within acceptable limits the capacity analysis for each service and geographical area is then carried out strictly in the NBR data to support the commissioning plans for the entirety of burn care in successive commissioning rounds. To clarify, the HES data will not be used for this analysis. Access to the patient level HES data is restricted to only authorised UHSM employees with involvement with the NBR database who need to access the data for the purposes outlined in this application. In addition to comparison of workload volumes, HES data will only be used to answer any challenges that may arise from NHS England or from a care provider. In such scenarios, UHSM may conduct further analyses of the HES data to produce comparisons with NBR data at lower levels. For example, UHSM may produce figures to show numerical differences between the volumes of records supplied by a provider and those derived from HES broken down to age or length of stay in order to highlight where shortfalls or excesses are occurring. All outputs comprise of aggregated data with small numbers suppressed in line with the HES Analysis Guide. UHSM only provides such services in response to challenges raised by NHS bodies. UHSM may undertake analyses of the NBR database using HES data for validation purposes in response to specific queries from NHS England. In such activities, HES data is used only for validation purposes and outputs will contain at most, aggregated data with small numbers suppressed in line with the HES Analysis Guide for the purpose of comparison with statistics derived from analysis of the NBR database.


Project 3 — DARS-NIC-376374-F8D0M

Opt outs honoured: No - data flow is not identifiable

Sensitive: Sensitive

When: 2016/12 — 2019/01.

Repeats: One-Off, Ongoing

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

Categories: Anonymised - ICO code compliant

Datasets:

  • Hospital Episode Statistics Critical Care
  • Hospital Episode Statistics Admitted Patient Care
  • Hospital Episode Statistics Accident and Emergency
  • Hospital Episode Statistics Outpatients

Objectives:

As a large specialist organisation providing complex care to patients from a broad range of demographic backgrounds the Central Manchester University Hospitals NHS Foundation Trust (CMFT) strives to provide the best care possible for its patients. This entails understanding the complex co-morbidities of its patient population, through which it aims to establish a patient co-morbidity index for the Greater Manchester area. This data will be used for two projects of analytical work: Firstly, benchmarking the Trust against other Trusts to guide their clinical coding improvement strategy. The Business Analyst team will have direct access to HES data to create bespoke, specialty and Health Resource Group (HRG) specific reports. These reports, in their aggregate format, will be shared with other staff at the Trust. They will not be identifiable and small numbers will be suppressed in line with the HES analysis guide. Secondly, the HES data will be used to inform the Trust’s strategic development plans. The information will be analysed to determine areas (geographical by residence, GP practice, CCG etc.) where service provision could be enhanced (e.g. outreach or community clinics). This information will also be used to determine growing/declining service needs of the regional population. The full HES data set is required for this application. This is to allow for analysis across the UK, as the Trust is a national tertiary centre with national tertiary patient services. The Trust compares itself as a whole and individual services to Trusts and services across England. Analysis is at service, site, and HRG level that is not limited to the North West providers. Analysis is ongoing to understand and identify service and HRG level peers. The trust provides a full range of services and therefore requires the full HES dataset requested; other data minimisation efforts are not appropriate. The Trust’s aim is to constantly improve its position as a leading healthcare provider both locally and Nationwide. There are a wide range of patient services that are positioned nationally, for example, the Royal Manchester Children’s Hospital would not suitably compare to the Northwest cohort, whereas the 10 specialist Children’s Trusts across England stretch from Bristol to Newcastle. Similarly, the care that many patients receive at The Royal Eye Hospital is only comparable to that at Moorfields Eye Hospital in London, and the Paediatric Burns Service would compare its specialism to that at Chelsea and Westminster. Analysing the data across the whole country will help CMFT identify the best care outcomes delivered at these similar sites and, from this analysis, work to ensure their services meet their own high standards. Knowledge of output area (OA) and GRIDLINK fields (geographical reference fields) will allow the Business Analytics team to very accurately extrapolate geo-demographic changes occurring within the catchment areas Central Manchester Foundation Trust (CMFT) serves. The Trust will link their existing activity numbers at OA level, to the publically available ONS population prediction statistics. This will enable them to anticipate not only the scale of the demographic growth for demand on services, but also casemix of service needs within specific geographical boundaries. This will enable the Trust to proactively plan for the needs of patients before those needs occur. This way access to NHS services across all acute and specialist services will be improved. Another use of the OA data in HES would support the applicant’s analysis of the local patient population – the percentage of total patient activity which belongs to the Trust. In knowing this, CMFT will be able to investigate the reasons for variation in patient accessing CMFT, whether that be due to ease of transport to their site(s), patient experience or whether they simply don’t provide the services needed. In doing this at OA level, the applicant would be able to understand and plan service expansions (e.g. additional community clinics) which will improve public health and patient access to NHS services.

Yielded Benefits:

HES data has been used to inform reviews into the Trust’s quality of clinical coding and highlight areas of potential performance improvement, looking initially at hospital length of stay. Specifically HES was used for the following: • Analysis of HES data has revealed a significant shortfall in the coding of co-morbidities at the Trust (both regionally and compared to similar hospitals nationally). This finding has prompted a series of coding reviews aimed at capturing these conditions and resulted in more accurate patient records and diagnosis histories. The correct recording of long-terms conditions such as diabetes and asthma, with dementia another condition of particular local importance, enables patients to be assigned more appropriate pathways and treatment, ultimately leading to higher quality care. • HES data is an integral part of the Trust’s recently developed capacity planning model, which will be used on an annual basis for analysing bed requirements. The data from HES are used to provide the length of stay benchmarks used to identify service lines that are significantly different to comparable hospitals. These benchmarks highlight areas of potentially inappropriate or inefficient care, or areas that may be amenable to service redesign and new/better pathways. Whilst it takes time for the results of service change to be fully realised it is expected that focussing on the outlying areas identified through the benchmarking exercise will reduce average lengths of hospital stay, benefiting both the local health economy and patients. Ongoing analysis of HES will be used to track the Trust’s progress in reducing length of stay relative to peers in key areas.

Expected Benefits:

Improving the Trust’s clinical coding develops the accuracy, precision and detail afforded in those policies for the population they serve. The Trust will use clinical coding information to determine disease prevalence rates so as to inform national and local commissioning policy. Any proposed process or service changes are discussed with CMFT's commissioners through established contracting channels (annual contract negotiation process). Any findings derived from the use of the data is shared with commissioners (in aggregated form and small numbers suppressed in line with HES Analysis Guide) to support service changes and local discussions. An example of this would be in Rheumatology, where it was identified that the Trust was not coding a series of comorbidities due to the nursing notes not having the relevant section to record items such as vitamin deficiency or uvetitis. Through local discussions with commissioners based on evidence from the analysis, the local coding/recording policy has changed and as a result the estimated value change has been applied to the 16/17 and 17/18 contract. It also means the patient record accurately reflects the true condition of the patients. This is the direct driver behind one of the applicant’s current Commissioning for Quality and Innovation payments (CQUINS): consistent coding of dental procedures across regional providers. Being able to identify areas where certain procedures and conditions are not being fully captured will ultimately safeguard the patient for any future clinical contact whilst securing the appropriate funding to the practice. It is also a vital factor in maintaining the Trust’s specialist status, which secures the continued provision of specialist care to our patient population. Better understanding the service needs of the Trust’s local and regional population will inform the Trust’s strategic development plans: thus the proportion of activity seen in an appropriate setting, potentially closer to patients’ homes in the community will increase. For example, should a discovery be made that acute asthmatic conditions arriving at A&E or positive screenings/admissions for cancer tend to come from a small number of geographical clusters, the Trust would then be able to run patient education seminars in GP practices or community centres in those localities to raise awareness of asthma self-care or the importance of screening programmes. Both these benefits are key strategic objectives which will take a minimum of 3 years to fully realise. The analysis of the HES data set and coding review has identified a number of areas requiring further investigation and clinical input. An example area was paediatric rheumatology where CMFT have identified a process gap in the Juvenile idiopathic arthritis patient pathway. The Doctor sees the child and identifies the pathway, the child then visits the nurse several times over the next 3 months to receive joint injections to relieve pain. The child is well cared for but the nursing documentation that goes into the notes did not include a section to include comorbidities. CMFT are now in the process of reviewing the medical record following this investigation. Spinal surgery is an area where very poorly children are seen for highly specialist care. Due to the nature of this care not many centres have the skilled workforce to carry the procedures out. As a result a lot of underlying conditions are taken for granted and factors such as wheelchair and stoma status are not always recorded in the notes. This has been highlighted to them and processes are being changed. A&E cases are an area where patients are in for a short time and notes are not always fully completed. The peer data has raised a number of areas that CMFT have been able to focus on and ensure care is taken by the medics to complete key comorbidity recording.

Outputs:

Outputs of analysis are in the form of reports and dashboards, which highlight any pertinent issues relating to the quality of patient care, and provide recommendations regarding the implementation of specific measures to improve efficiency and effectiveness of care. Performance indicators for clinical coding breadth and depth will be created, benchmarking specific services within the Trust with others, within 12 months of access. The Performance indicators for clinical coding breath is still currently in development which will be supported by the continuation of the Agreement. This will be at an aggregate HRG and/or service level with small numbers not suppressed. The audience for these types of reports will be only ever be within the Trust, executive board members, senior directors and management. There will be specialty level reporting for the alerting specialties where multi-disciplinary clinical leads will work with the indicators to ensure recorded case mix appropriately reflects their service. National/regional ‘access’ dashboard will be created detailing the activity conducted across the country, with attention to the North West region, detailing types of services being accessed by which patient groups. This may involve time-lapse geospatial analysis and imaging for specific services and or geographical areas of focus. This will be within 24 months of data access. Data will only ever be presented in aggregate format with small numbers suppressed in line with the HES analysis guide. Reports and dashboards will only be shared within the trust to be viewed by clinicians, managers and informatics staff. No output will be published in journals. A coding review process is now in place, supported by the HES data sets. An example has been provided in the benefits section of this purpose.

Processing:

On receiving the data the data was uploaded by the Informatics department database administrator (DBA) to a ‘HES’ database on a secure SQL server. The server is accessible only within the Trust and access is controlled by permissions linked to Trust user accounts. Permissions to view the HES database are only given to specific Trust employees. Permission requests are received and managed by the Head of Information & Analysis via email to ensure an audit trail of the request. Permissions are only given to a limited and select cohort of the Trusts Business Analysts, within the information department for the purposes set out in this application. The data will not be linked to any other patient identifiable datasets or any other non-identifiable data sets. Informatics specialists then write SQL queries to extract relevant information to their analyses and create new tables within the HES database with the results. Reports and dashboards can link to this data without revealing any of the raw data due to the permissions that have been set. The reports are formatted using a number of different business intelligence tools such as MS Excel and SQL Server Reporting Services (SSRS) reports; depending on the intended audience and the data being analysed. These reports and dashboards are then shared in the trust to help improve the effective delivery of healthcare and the patient experience. This will be in the form of reports, dashboards, and analysis and may be distributed through a number of channels including email, presentations, and papers. Audiences will range from senior management to operational teams. Data in the reports and dashboards will only ever be presented in aggregate format with small numbers suppressed in line with the HES analysis guide. Informatics specialists are based within the Trust’s Business Analytics team and are substantively employed by the Trust.


Project 4 — DARS-NIC-324040-N7L9R

Opt outs honoured: No - consent provided by participants of research study (Consent (Reasonable Expectation))

Sensitive: Sensitive, and Non Sensitive

When: 2020/08 — 2020/08.

Repeats: One-Off

Legal basis: Health and Social Care Act 2012 – s261(7)

Categories: Identifiable

Datasets:

  • Hospital Episode Statistics Admitted Patient Care
  • HES:Civil Registration (Deaths) bridge
  • Civil Registration - Deaths

Objectives:

Cardiac Magnetic Resonance (CMR) imaging is an established clinical test used to diagnose heart conditions and to guide patient management. Magnetic resonance imaging was invented in the 1970’s, with CMR being established in the 1980’s. It produces very detailed images and video loops of the heart to help cardiologists diagnose and treat heart diseases. Its introduction drastically outperformed previous heart imaging techniques, predominantly echocardiography, in the level of detail it provides. However, CMR is still a relatively new technique and thus, whilst the prognostic value of some components of the CMR examination are established, the prognostic value of other information CMR provides are not well established. Furthermore, CMR imaging is a rapidly changing field and new image sequences (parts of the CMR scan) are being introduced and adopted into clinical practice all the time. The diagnostic and prognostic utility of these new parts of the CMR scan often are not well established. The public interest justification for article 9(2)(j) – for public health purposes, is therefore that the University Hospital of South Manchester (UHSM) CMR study aims to investigate the diagnostic and prognostic utility of CMR scanning in a large cohort of unselected patients who are already undergoing CMR scanning for clinical indications. This means the study team are assessing how well CMR is in not only being able to identify heart conditions (diagnosis), but also how specific heart findings impact on people’s life expectancy and quality of life. The study team are currently looking to assess a subset of the patients from the UHSM CMR study. This subset of patients was recruited between 1st June 2016 and 31st May 2018. CMR indices will be related to the presence and severity of cardiovascular disease and other markers of cardiac disease. For example, the study team will measure how much scar tissue the heart contains and whether this impacts on how well the heart is able to pump blood. The justification for Article 6(1)(e) processing therefore, as necessary for the performance of a task carried out in the public interest or in the exercise of official authority vested in the controller, is that few centres have sufficient patient through-put to provide this information. The Manchester University NHS Foundation Trust (previously University Hospital of South Manchester (UHSM)) CMR Unit is one of the highest volume CMR centres world-wide with capacity for 5000 scans per year. Thus the UHSM CMR centre is well placed to do this study, indeed it has an obligation to carry out this work and provide this information to the community in order to improve patient care, locally and internationally. All patients recruited into the UHSM CMR study provide written informed consent to be enrolled into the study, with specific consent given stating: ‘I understand that the information held and maintained by The Health and Social Care Information Centre and other central UK NHS bodies, audits and registries, and local healthcare bodies and clinical commissioning groups, may be used to help contact me or provide information about my health status.’ The UHSM CMR study requires follow-up information on the health status of the study cohort. This is limited to three variables (death, cause of death, hospital admission for heart failure). Such data will be used to perform survival analysis and multivariable regression analysis and risk modelling for death (all-cause and/or cardiovascular) and heart failure hospitalisation. Hospital episode statistics (HES) are an important source of data and can identify patients who have been admitted to hospital for heart failure. Civil Registration Data (CRF) mortality information can provide information relating to patients who have died including the cause of death. For both data sets the period of interest is between 1st June 2016 and 31st May 2018. This is the recruitment period in the cohort of patients the study team are currently assessing. For data minimisation, no data is requested for patients recruited outside of this time window. No demographic filtering will be required. For the HES data set, the only variables that will be requested are hospital admission date and diagnoses, in order to identify if a patient was treated in hospital for heart failure. No other variables will be selected. For the CRD data set, the study team will only require the variables date of death and cause of death. For data minimisation, no other variables will be requested. Identifying data will only be used to confirm the health status of the individuals who have provided written consent to be included in the study and for this information to be obtained by the research team. Information will be kept for a period of 5 years in case of a data challenge to the published findings. The alternative method for obtaining such outcome data is to contact each patient individually (which they have consented to), however this is more intrusive to patients and less practical to given the large cohort size (approximately 4000). Manchester University NHS Foundation NHS Trust are the sole data controller and data processor for the UHSM CMR study. The study is funded by the National Institute for Health Research

Expected Benefits:

Cardiovascular disease is an intolerable burden on the population, with heart failure alone affecting 2-3% of the population and represents the most common reason for hospitalisation in patients over the age of 65. Despite advances in cardiovascular medicine, outcomes remain unacceptably poor - heart failure has a 50% mortality rate within 5 years of diagnosis. Heart failure is an enormous public health problem, with more than half of the population over 45 at risk. Identification of patients at risk of adverse outcome continues to be inadequate. If, as hypothesised, myocardial fibrosis quantification does prove important as a prognostic factor and allows more effective risk stratification, it would have a large and immediate impact, allowing better guided intervention (personalised medicine), in a more timely (potentially preventative) manner, and thus could lead to significant improvements in the health and wealth of the nation. CMR is a safe and non-invasive imaging method. The NHS website describes MRI scanning as “painless and harmless” and “one of the safest medical procedures currently available”. Given the non-invasive approach, safety, quality and versatility of CMR imaging, it is increasing being requested as the initial cardiac imaging modality of choice and its use rapidly expanding. Manchester University NHS Trust alone has the capacity to perform approximately 5000 scans per year. As a result, a wealth of data is being produced that can be utilised to better understand cardiovascular disease mechanisms. CMR imaging has transformed the practice of cardiovascular medicine. It has the potential to tell so much about heart tissue characteristics that have simply not been possible before. An example of such advancement is the ability of CMR to accurately calculate the amount of heart scarring (‘myocardial fibrosis’) within the heart, a phenomenon known to increase the risk of death and worsening heart failure. These techniques are now being used in clinical studies as outcome measures assessing the response to novel targeted interventions, for example the PIROUETTE study in heart failure with preserved ejection fraction. CMR therefore represents a ‘window’ into understanding what factors are driving a patients cardiovascular illness, however it remains unclear what factors are the most important disease mechanisms that produce such poor outcomes. This is only possible by examining large cohorts of patients and looking at the many measures of heart structure and function that the study team can relate to variables of ‘death’ and ‘heart failure’. The research will therefore be of benefit to patients in developing this technology and CMR techniques. This study will therefore provide a database of sufficient size, depth and quality, utilising the latest CMR measurements and techniques in understanding the disease mechanisms responsible for cardiovascular disease and heart failure. It will allow more effective identification of patients with an increased risk of adverse outcome, will better guide intervention (personalised medicine), in a more timely (preventative) manner, and thus the project could lead to significant and immediate benefits for patients and the NHS. It will allow the study team to produce more accurate models for patient risk stratification and to identify those patients who are likely to develop cardiovascular disease in the future, and, ultimately, intervene to prevent its occurrence. By understanding which of these CMR imaging parameters are most associated with poor outcomes can the study team understand which techniques to develop further to ultimately stratify disease mechanism and provide targeted treatment and assess its impact.

Outputs:

The study team intends to produce a large high quality database, containing NHS Digital data, populated with clinically relevant disease markers and labels associated with cardiovascular disease. The outputs from this database will include comprehensive descriptive modelling of cardiovascular diseases, predominantly relating to heart failure and cardiomyopathy. Furthermore, specific causes of cardiovascular disease (for example, myocardial fibrosis or ‘heart scarring’) can be interrogated and, with the information from NHS Digital, their impact on outcome assessed. This is important to potentially identify newer ways of treating patients through meaningful disease mechanisms using targeted therapies. The outputs from the database will include submission of academic papers to peer reviewed journals, commencing in 2020 onwards. The results will be disseminated amongst the medical community via presentations at academic conferences both nationally and internationally. The findings and results will also be presented to patient groups. The study have a dedicated patient advisory group. This is funded by NIHR. The group comprises 6 patients and is chaired by a representative of a patient-led charity. The group helped confirmed the studies importance for patients. The group has met annually throughout the study to provide input on study progress and management. The study team will discuss the findings with the group and get their input on the interpretation of the findings. The dissemination will include presentation to patient groups, and members of the advisory group will take part in these presentations This will form the bases for a series of peer reviewed journal articles, commencing in 2020 onwards. Findings from such articles will be disseminated amongst the medical community via presentations at both national and international conferences. Additionally, findings will be communicated to patients via Patient and Public involvement (PPI) including charity foundations (including the Pumping Marvellous Foundation, the largest UK Heart Failure Charity). Furthermore, the UHSM CMR Study provides an annual update to a Patient Advisory Group (PAG), consisting of patients and members of the public.

Processing:

The UHSM CMR study data is stored in a database on an NHS computer, with restricted access and password entry. Patients’ NHS number and date of birth are recorded in the database. Other identifiable information is separated from the database and stored with the associated study number on a separate computer under password protection in a locked office, in order to contact the participants if required. The flow of data into NHS Digital is limited to personal identifying information required for data linkage and verification, including study ID, NHS number, date of birth, and gender. Data files can be sent via secure nhs.net email account with associated [SECURE] encryption application. All participants provided written informed consent to take part in the study, including for access to their health records and information held and maintained by NHS Digital formally known as The Health and Social Care Information Centre. Flow of data out of NHS Digital will consist of a download of data files containing the study ID and NHS number that was initially supplied in order to match the patient correctly, plus matching fields from NHS Digital with the requested health status and outcome data. Identifying data provided will be the minimum required for basic data linkage and to ensure quality assurance of matching. Data files will be downloaded onto an NHS computer with restricted access and password entry, contained in a managed environment in a locked office. This will therefore only be accessible to a limited number of qualified research staff. Outcome data supplied by NHS Digital will be processed and included in the UHSM CMR Study database to allow for subsequent ethically approved analysis. Outcome data will be linked to the participant record contained within the database. The UHSM CMR Study database is pseudonymised. Dates of events, including date of death and date of hospital admission, are required to calculate time periods for subsequent survival analysis. All data processing is performed onsite within an NHS trust. Database access is limited to qualified research staff associated with the study. Manchester University NHS Foundation Trust is the sole data controller for this study. All research staff working with the data are substantive employees at the host site. Data will not be shared with any third parties who are not affiliated with the study. Heart failure and heart disease has a very high incidence in the United Kingdom, therefore it is considered highly unlikely that re-identification will occur.


Project 5 — DARS-NIC-304146-M5F6Y

Opt outs honoured: Yes - patient objections upheld (Section 251 NHS Act 2006)

Sensitive: Non Sensitive

When: 2021/05 — 2021/05.

Repeats: One-Off

Legal basis: National Health Service Act 2006 - s251 - 'Control of patient information'.

Categories: Anonymised - ICO code compliant

Datasets:

  • Hospital Episode Statistics Admitted Patient Care

Objectives:

This application from The University of Manchester seeks to determine whether a diagnostic chest pain algorithm can be updated with machine learning techniques to prevent known loss in accuracy over time. The purpose of the study is to improve cardiovascular risk prediction in the emergency department (ED). The project seeks to do this in two parts. Firstly University of Manchester aim to maintain and improve an existing acute myocardial infarction clinical prediction model currently in clinical use and in the process validate a method for updating all clinical prediction models. Secondly University of Manchester intend to examine the prognostic value of emergency department data in predicting long term cardiovascular outcomes. University of Manchester are requesting data on cardiovascular outcomes that occur subsequent to the index attendance at an emergency department. The time frame is of interest is short term and long term (1 - 10 years) for the first cohort and only short term (1 year) for the second. This will enable the use of machine learning to update the diagnostic algorithm, and also assess the prognostic ability of ED data for long term cardiovascular outcomes. The Troponin-only Manchester Acute Coronary Syndromes (T-MACS) decision aid was derived by the group to improve the early diagnosis of acute coronary syndromes (ACS) or ‘heart attack’. T-MACS uses data available when patients arrive in the Emergency Department (ED) to calculate the probability of ACS. Patients are then assigned to four risk groups including a ‘rule-out’ group that can be immediately discharged, and a ‘rule-in’ group that can receive early treatment. T-MACS has been validated extensively and has been used at Manchester University NHS Foundation Trust (trust in the geographical area of the project) since June 2016, avoiding unnecessary hospital admission for approximately two thirds of patients. T-MACS won the MFT Transformation Prize (2016) and will be implemented across Greater Manchester as a Health Innovation Manchester exemplar project. All clinical prediction models degrade over time, in a process called calibration drift. In this work University of Manchester will seek to update and improve the TMACS algorithm, two methods will be used; a single ‘one-off’ update and a method that continuously updates itself. This will enable the algorithm to be continually refined and optimised, avoiding the problem of ‘calibration drift’ due to changes in clinical practice and patient demographics. This will promote patient safety (by reducing the potential for missed diagnoses); enable patients to benefit from more accurate diagnoses at the earliest opportunity; will facilitate increasingly personalised healthcare; will reduce the need for future, expensive and prolonged clinical research studies to update diagnostic algorithms; and will prevent over-use of healthcare resources. Emergency medicine has been at the forefront of the implementation of clinical prediction models. The need for timely, accurate exclusion of high-risk pathologies combined with an NHS wide efficiency drive has been adeptly met by such tools. Current extensively used risk assessment tools include the Wells’ Score for deep vein thrombosis and pulmonary embolism, the CURB-65 score to aid the treatment of community acquired pneumonia(6), Ottawa ankle rules to guide the use of x-rays in suspected ankle fractures, and also the Canadian C-spine rule to determine if it is possible to clinically exclude a cervical spine fracture. These prediction models are derived and validated in peer-reviewed papers, before being implemented clinically and audited for clinical accuracy and impact. The populations being similar and comparable is an integral part of each model’s applicability to the individual urgent care settings. However, this static comparability is an inherent flaw, as the model cannot be tailored to each site accounting for different patient populations, further it is static and therefore cannot adapt to changes in evolving diagnostic technology through time. Classical statistical and modern machine learning methods have previously been proposed to overcome such limitations machine learning would allow prediction models to be updated (re-derived) without human input. This has several advantages over classical statistical methods as it can be done quickly, continually, and accurately/ other clinical prediction models have been updated using similar methodologies. EuroSCORE is a risk prediction model used in European cardiac surgery which was shown to demonstrate calibration drift due to changing demographics. The updating of these models is often done from large cumulative data sets from multiple different hospitals. The use of these collective datasets poses challenges of comparability, different demographics in the larger hospitals may make the clinical prediction model at a smaller hospital more inaccurate than if it had just used its own data. Wiens et al explored this issue and the different methods to combat it. They investigated the use of only combining data for analysis when similar features were available from all sites. They suggest that such an analysis is at its greatest strength when it has more shared data features. This would be the case for Greater Manchester -TMACS as the tool would collect similar data from all sites, therefore it would have almost identical data features making it a near perfect data set for such a cumulative analysis. Long term cardiovascular risk prediction In a pilot trial comparing T-MACS to standard care, University of Manchester evaluated patient satisfaction. While overall satisfaction was high (mean overall score 3.78/5), patients gave lower ratings (mean 2.78/5) for “advice you got about ways to avoid illness and stay healthy”. Patients are dissatisfied with an approach that simply informs them that they ‘do not have ACS’ but that does not address future cardiovascular risk. This sentiment was echoed by two patient groups. Whereas such tasks may previously have fallen to inpatient teams, the widespread use of early rule out strategies means that emergency physicians must increasingly bear responsibility for informing patients of their future risk. There were 23.4 million patient presentations to UK Emergency Departments (ED's) in 2016, and this has been increasing by 10% each year. Patients who do not see their General Practitioner (GP) frequently are more likely to attend the ED, meaning that ED's are interacting with a portion of society under-served by primary care. Patients now expect clinical staff in the acute care setting to have tools to inform them of their long-term cardiovascular risk. Cardiovascular disease (CVD) remains the leading cause of premature death in the western world. Primary prevention to reduce mean blood pressure and cholesterol by 10% could reduce the incidence of major CVD by 45%. However, because important risk factors for CVD such as hypertension and hyperlipidaemia are usually asymptomatic, identification of at-risk individuals can be challenging. While in the ED, all patients with suspected ACS will have vital signs recorded. However, these data are not currently used to identify patients at risk of CVD, which represents an important missed opportunity. Previous research has demonstrated that patients with hypertension in the ED have over 90% probability of having persistently elevated blood pressure in the community setting. Contrary to popular belief, hypertension in the ED cannot be wholly attributed to pain or anxiety. Recent studies have shown that hypertension measured in the ED is predictive of 10-year adverse cardiovascular outcomes. Furthermore there is limited evidence from Farkouh et al, that acute chest pain algorithms are predictive of long term cardiovascular disease. Farkouh et al demonstrated that patients deemed high risk had a hazard ratio of 2.45 (95% CI 1.67-3.58) for cardio and cerebro-vascular events at a follow up of 7.3 years. In primary care, the QRISK-2® (or QRISK-3®) tool is routinely used to predict patients’ 10-year risk of CVD. If the 10-year risk exceeds 10%, the National Institute for Health and Care Excellence (NICE) recommends that statin therapy should be considered. A range of other measures (advice on smoking cessation, weight loss, diet, exercise and review of comorbidities) should also be undertaken. This tool could potentially be used in the ED because most of the data required are already routinely collected. This could identify patients at high risk of CVD who would otherwise have been unidentified. Thus patients would not only be better informed but University of Manchester could prevent more incident CVD. Patients in the ED have different characteristics to those attending for screening in primary care. University of Manchester must validate prediction model in this setting before use. Legal Basis for Processing under GDPR Justification: Article 6(1)(e) - public interest - The accurate prediction of heart attacks in the emergency department is vital as it is such a high risk condition. Currently a diagnostic algorithm (T-MACS) is deployed across Greater Manchester, and University of Manchester are seeking to maintain and improve its accuracy. Unfortunately, diagnostic algorithms like T-MACS also have some important disadvantages. Over time, they tend to become less accurate because important factors change including the age of patients, the number of long-term medical conditions they have, the tests that are used and the ways in which healthcare workers practice. This means that the research must be repeated, which is inefficient and expensive. Cardiovascular disease is the leading cause of premature death in the western world, and unfortunately Greater Manchester currently has double the national average of preventable cardiovascular deaths. Emergency Departments present an opportunity with; increasing footfall, pre-existing data collection systems, teachable moments around chest pain presentations and interacting with a portion of society that does not regularly interact with primary care. Article 9(2)(j) - The accurate prediction of heart attacks in the emergency department is vital as it is such a high risk condition. Currently a diagnostic algorithm (T-MACS) is deployed across Greater Manchester, and University of Manchester are seeking to maintain and improve its accuracy. Unfortunately, diagnostic algorithms like T-MACS also have some important disadvantages. Over time, they tend to become less accurate because important factors change including the age of patients, the number of long-term medical conditions they have, the tests that are used and the ways in which healthcare workers practice. This means that the research must be repeated, which is inefficient and expensive. The study has been approved by the research ethics committee and the confidentiality advisory group This whole project will form part of a PhD thesis. Only summary level data with small numbers suppressed in line with the HES Analysis Guide will be reported and no record level data will be published. The person undertaking the PhD is a substantive employee of the University of Manchester. This project will handle routinely collected data from patients who had presented to the emergency department with chest pain. University of Manchester plan to analyse short and long term cardiovascular outcomes; short term outcomes to refine and prevent the degradation of an acute coronary syndrome rule out strategy and long term outcomes to identify predictors for long term cardiovascular disease in the acute care setting. University of Manchester plan to include two separate patient cohorts, which will enable the University of Manchester to maximise the value of the work, as follows: To evaluate short-term outcomes (refinement of the T-MACS mathematical algorithm) University of Manchester intend to use a cohort from 2016 to the present day. This will include patients presenting to Emergency Departments across Greater Manchester. These patients have full data for T-MACS (required to optimise short-term risk prediction), and the follow-up period (12 months) is sufficient for that analysis. (Estimated 16000 patients) University of Manchester will use the data to optimise the machine learning approach to refine the T-MACS algorithm. University of Manchester will send to NHS digital two cohorts of data, both will contain patient identifiers and the date and time of index event (attendance to an emergency department with the presenting complaint of chest pain). University of Manchester intend to identify the optimal predictors of long-term cardiovascular disease among patients presenting to the Emergency Department with chest pain. To do this University of Manchester will use cohorts that cover the introduction of conventional cardiac troponin and high sensitivity cardiac troponin. This long term outcome cohort (Cohort 1) will be exclusively from Manchester University NHS Foundation Trust and match the time periods of covering the introduction of the technology (2009-2010, 2011-2012 , 2016-2017). The cohort will consist of patients who presented to the adult Emergency Department with chest pain and will include any patient over 18. Summary: Cohort 1 - 10 year outcome cohort Contributing NHS trust: Manchester University NHS Foundation Trust Estimated sample size: 21,000 Data requested from NHSD: diagnostic & intervention codes, codes including cardiovascular death, acute myocardial infarction, stroke or coronary revascularization. Multiple NHS trusts are providing Manchester University NHS Foundation Trust with data as part of a Greater Manchester service improvement programme. This data forms part of the second cohort, for cross-linking. NHS trusts to include Manchester University NHS Foundation Trust, Stockport NHS Foundation Trust, Wrightington Wigan and Leigh NHS Foundation Trust, and East Lancashire Hospitals NHS Foundation Trust will send the data to University of Manchester who will then send onto NHS Digital. Cohort 2 - 1 year outcome cohort. Contributing NHS trusts - Manchester University NHS Foundation Trust, East Lancashire Hospitals NHS Foundation Trusts, Stockport NHS Foundation Trust, and Wrightington Wigan and Leigh NHS Foundation Trust Estimated sample size: ~ 15,000 Data requested from NHSD: diagnostic & intervention codes, readmission [1 year from index event] - all intervention and diagnostic codes For the short term outcome at one year from index event the diagnostic and intervention codes are required regardless of it is cardiac in nature, this is in order to understand the reasons for re-attendances and to ascertain if it is due to an inaccuracy in the index admission diagnosis. For all the cohorts the data controller and processor is the University of Manchester. The contributing NHS sites will transfer the identifiable data to the university. NHSD will only receive and return data to the Data Safe Haven at the University of Manchester. Data will be returned with a study ID in place of the identifiable variables. The rationale for two data-pulls is that the local database is dynamic, constantly adding new patients as the data is collected from routine clinical activities. The second data pull will add additional patients from the existing sites and any additional sites that are yet to join the project (currently only anticipated to be Wrightington Wigan and Leigh NHS Foundation Trust). University of Manchester believe that these data sets are the minimum required to successfully gain the aforementioned data points Once cross linked University of Manchester intend for the data to be pseudonymised. Identifiers will be passed to NHS Digital to facilitate the linkage - but all clinical data will be sent back to the University of Manchester with identifiers removed, and a unique study-ID in place of the identifiers. The University of Manchester require 10 years of data retrospectively. 10 years is the standard for long term cardiovascular outcomes as mentioned in the literature. The data has already been geographically restrained due to the cohort only being from Greater Manchester emergency department attenders. The project group does not believe that there is an alternative method to gain outcome data for patients seen ten years ago. The data controller and the data processor is the University of Manchester. Funders - National Institute of Health Research - Doctoral Research Fellowship - Manchester University NHS Foundation Trust - Data Driven Healthcare award

Expected Benefits:

Promotion of health The dissemination of the data will enable analysis which with safeguard and improve healthcare. This is through three main mechanisms; (a) Developing a method for updating and maintain clinical prediction models (b) Updating and improving T-MACS which is current in clinical practice across Greater Manchester (c) Allowing emergency departments to prognosticate for long term cardiovascular outcomes in a high risk population. Public interest The public benefits greatly from clinical prediction models across all medical practice, but these are all slowly losing accuracy over time. The dissemination will allow a method to be validate for safeguarding and protecting these vital tools. T-MACS will be updated, maintained and improved. This will enable its continued use to safely, accurately and rapidly diagnose myocardial infarctions for patients attending the emergency department with chest pain. Identifying and potentially modifying long term cardiovascular outcomes in the emergency department has the obvious benefit of improving the populations health. Furthermore it makes efficient use of NHS services maximising each patient encounter for the greatest benefit to patient’s health. Dissemination of findings First, the findings will be submitted for publication in peer reviewed journals. This is likely to involve publication of (a) the validated method for updating clinical models, (b) the updated and improved T-MACS model, and (c) the prognostic ability of routinely collected ED data to predict long term cardiovascular outcomes. The primary target audience for the clinical study will be emergency medicine physicians, GPs, acute medicine physicians, cardiologists, clinical biochemists, public health professionals and industry leaders in acute diagnostics. However, given the novelty of this work it is likely to appeal to a wide general medical readership. Therefore, submission will primarily strive to achieve publication in a high impact medical journal such as the New England Journal of Medicine or the Lancet. Presentations at international and national conferences with relevant target audiences will be sought (e.g. European Society for Emergency Medicine Annual Congress, European Society of Cardiology Annual Conference, Royal College of Emergency Medicine Annual Scientific Conference). In addition, a public engagement strategy will be developed in conjunction with Public Programmes and the patient groups, in order that the local population have the opportunity to learn about the work and to engage with future work. This will include a press release to be generated with the host institution, Health Innovation Manchester and the NIHR, to be disseminated in conjunction with at least one patient story. A social media strategy will be developed to enhance the impact of the work. As part of the planning for this piece of research we have already engaged a PPI group to gauge opinion on the research plan this includes eight patient representatives from the Wythenshawe ëTicker Clubí. This is an organisation that actively represents patients with cardiac pathologies, and consists of patients who have had such pathologies themselves and associated interventions. The group were supportive of the idea, and were particularly pleased that it would be safeguarding the clinical decision rule for the future. There were concerns regarding the communication between the patient and the clinician in conveying the calculated risk of ACS/MACE, particularly now that more autonomy will be given to the AI. However the group were satisfied that with careful explanation and shared decision making models this would be minimised and countered. We intend to hold another four meetings through the study period, one at the mid-point of phases one and two, and another at the end of each phase. This will enable the research to gain the most from patient and public involvement. Impact This project, if successful, will develop methodology that will protect all clinical prediction models from degradation. Given the widespread use of clinical prediction models this will have a large impact on all areas of clinical practice, ensuring that the accuracy and safety of these clinical tools maintained. This will inform the wider scientific community in how best to avoid calibration drift in other clinical prediction models. There will also be the improvement of the TMACS algorithm increasing accuracy, benefiting patient care and safeguarding it against future calibration drift. If the findings are positive implementation strategy will be developed in conjunction with Health Innovation Manchester, which provides direct access to the Joint Commissioning Board in Greater Manchester. This study and the results will form part of a PhD thesis for a National Institute of Health Research doctoral research fellow.

Outputs:

The outputs of the research will include: (a) The results of the data processing will form part of the doctoral work of a National Institute for Health Research Doctoral Research Fellow. As part of the funding arrangements from the National institute of Health Research's funding requirements the results will also form part of regular reports to them. (b) The findings will be submitted for publication in peer reviewed journals. This is likely to involve publication of (a) the refined statistical methodology for updating models, (b) updating and maintaining the diagnostic algorithm, (c) the prognostic value of routinely measured data for long term cardiovascular risk prediction in ED . The primary target audience for the clinical study will be emergency medicine physicians, acute medicine physicians, cardiologists, clinical biochemists, public health professionals and industry leaders in acute diagnostics. However, given the novelty of this work it is likely to appeal to a wide general medical readership. Therefore, submission will primarily strive to achieve publication in a high impact medical journal such as the New England Journal of Medicine or the Lancet. (c) Presentations at international and national conferences with relevant target audiences will be sought (e.g. European Society for Emergency Medicine Annual Congress, European Society of Cardiology Annual Conference, Royal College of Emergency Medicine Annual Scientific Conference). In addition, a public engagement strategy will be developed in conjunction with Public Programmes and the patient groups, in order that the local population have the opportunity to learn about the work and to engage with future work. The data will be reported upon in aggregate and summary statistics, it will not comment specifically on individuals patients. All outputs will contain aggregated results with small number suppression applied - in line with the HES Analysis guide. This will include a press release to be generated with the host institution, Health Innovation Manchester and the NIHR, to be disseminated in conjunction with at least one patient story. A social media strategy will be developed to enhance the impact of the work, this will be aided by the research team being authors on a popular international blog and podcast. Twitter updates will also be given. The project intends for the publications to be open access and for the outputs to be generated within 6 months from the end date, which is currently scheduled for 2022.

Processing:

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). University of Manchester will send to NHS digital two cohorts of data, both will contain patient identifiers (NHS number, Date of Birth, Postcode and the Study ID) and the date and time of index event (attendance to an emergency department with the presenting complaint of chest pain). The research only requires data since the patient was seen in an emergency department with chest pain, as such the University of Manchester have only requested data within this time frame. The data has already been narrowed by geography as only the sites whom are using the troponin-only Manchester acute coronary syndrome diagnostic algorithm have been selected. This includes a selection of trusts within Greater Manchester. In order for the results of the research to be generalisable University of Manchester are not seeking to narrow the demographics. This will ensure that the research and potential improvement in diagnostic accuracy is of benefit to the widest patient population. University of Manchester are seeking to ascertain if any patients treated in emergency departments for chest pain re-attend and are subsequently diagnosed with a heart attack or undergo revascularisation. The cohort has already been pre-selected as chest pain presenters to the emergency department. For the short term outcome at one year from index event the diagnostic and intervention codes are required regardless of it is cardiac in nature, this is in order to understand the reasons for re-attendances and to ascertain if it is due to an inaccuracy in the index admission diagnosis. For the long term outcome a cohort could be created that extracted only codes for myocardial infarction, revascularisation, and stroke. NHS digital will link the cohort with the HES Admitted Patient Care Dataset. The data is then to be pseudonymised and transferred securely to the University of Manchester's Data Safe Haven. Data transferred to NHS Digital will contain identifiable information, data returned to the University of Manchester will be pseudonymised. NHS Digital will remove identifiers and replace with a study ID before release. On receipt the data will be linked back to the clinical data set with unique study ID numbers, once linked the study id will be removed. NHS trusts including Manchester University NHS Foundation Trust, Stockport NHS Foundation Trust, Wrightington Wigan and Leigh NHS Foundation Trust, and East Lancashire Hospitals NHS Foundation Trust will send the data to University of Manchester for the cohorts. University of Manchester - will receive and store the data from NHS Digital. Data will be transferred to NHS Digital from The University of Manchester using Secure Electronic File Transfer (SEFT) and received from NHS Digital via SEFT also. In summary the value of known prognostic factors will be evaluated for predicting long term cardiovascular outcomes, by means of cox regression and known models will be tested with logistic regression. Short term cardiovascular outcomes will be used to update the current clinical prediction model and ascertain the optimum method for updating models. The data will be pseudonymised prior to being returned to the University of Manchester. No individual level data will be published in finalised outputs - all data will be presented in aggregated results with small numbers suppressed in line with the HES Analysis Guide. University of Manchester can confirm that there will be no attempt to re-identify individuals. The data will only be processed by University of Manchester staff and doctoral students. The doctoral students are substantive employees of the University of Manchester. All of the research team have been trained in data protection and confidentiality as per the University of Manchester's guidelines. The data will be held and processed within the University of Manchester Data Safe Haven - a secure virtual machine environment with fully auditable logs. The data will not be removed from the University of Manchester's Data Safe Haven, it will be securely deleted from it once it has been processed and the project finalised.


Project 6 — DARS-NIC-206314-N1N7K

Opt outs honoured: No - consent provided by participants of research study (Consent (Reasonable Expectation))

Sensitive: Non Sensitive

When: 2020/05 — 2021/02.

Repeats: One-Off

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

Categories: Identifiable

Datasets:

  • Hospital Episode Statistics Accident and Emergency
  • Hospital Episode Statistics Admitted Patient Care

Objectives:

Aim/Purpose Data from NHS Digital is requested as part of the PATTErn study to provide record-level information about healthcare utilization for each participant. The purpose of the PATTErn study is to examine the relationship between physical activity (as measured by cardiac devices) and non-elective hospitalisation attendances/admissions (NEHA) in older people with cardiac devices. Our hypothesis is that physical activity tends to decline in the days/weeks leading up to NEHAs. We will be exploring physical activity trends surrounding NEHAs, thus require information about these events in order to perform the analysis. Patients are being recruited from a tertiary care service; therefore, NHS Digital HES data is required as many participants’ local hospital will be elsewhere in the region. Article 6 GDPR justification for processing 6.1(e) ‘processing is necessary for the performance of a task carried out in the public interest or in the exercise of official authority vested in the controller’ All patients participating in the PATTErn are required to provide written, informed consent. This is recorded on the HRA approved consent form. Two sections on the consent for refer to NHS Digital data: 1) ‘I confirm that I have read and understand the Participant Information Sheet dated 13/08/2018 (version 4.4) for the above study and have had the opportunity to consider the information’. - The patient information sheet contains detailed information about collection and use of data from NHS Digital (attached) 2) ‘I understand that information about my hospital attendances in the last 12 months will be collected from NHS Digital. This process will involve sharing of personal details with the NHS Digital service.’ The collection of data from NHS Digital is essential for the completion of the PATTErn study. This is in the public interest as: (1) all participants have expressed willingness of their data to be used as part of the PATTErn study to advance academic knowledge (as evidenced above), (2) results from the PATTErn study will advance academic and clinical knowledge and understanding of the relationship between PA and hospitalisation events and (3) results from the PATTErn study will in likelihood lead to either further clinical studies or direct change in clinical service provision with the aim of improving patient care. Article 9 GDPR justification for processing 9.2(j) processing is necessary for archiving purposes in the public interest, scientific or historical research purposes or statistical purposes in accordance with Article 89(1) based on Union or Member State law which shall be proportionate to the aim pursued, respect the essence of the right to data protection and provide for suitable and specific measures to safeguard the fundamental rights and the interests of the data subject. As laid out above, data processing is in the public interest for the benefit of health and social care. Safeguards to protect data subjects are in place as per University of Manchester data security and data protection policies (see processing activities). How will the data requested achieve the aim identified? NEHA data from NHS Digital will allow us to investigate the relationship between physical activity and non-elective hospital attendances/admission. This is the core aim of the PATTErn study. NHS Digital data will provide dates and information about NEHAs. NEHA ‘events’ will be added to participant timelines to facilitate temporal trend analyses. Background to Study The PATTErn study is a stand-alone study. It is sponsored by the University of Manchester, and forms the central part of a PhD. PATTErn is an observational research study with full HRA approval (granted 18th June 2018, substantial amendment approval granted 1st October 2018) which opened for recruitment on the 12th October 2018. the Phd is being carried out by a substantive employee of the University of Manchester. The PATTErn study is a stand-alone study. It is not part of a wider project or collaboration. No follow-up work is currently planned. NHS Digital data collected as part of this application will be used as described above for the PATTErn study only. Data Subjects Recruitment target is 150 participants (it is anticipated recruitment will extend above this target). There is a single cohort. Inclusion criteria identify patients over the age of 60 under follow-up at Manchester Heart Centre with a Medtronic cardiac device in situ for > 6 months, which has the capacity to monitor physical activity. Patients must be able to provide written, informed consent in the English language. Purpose of Request The purpose of this project is to investigate physical activity patterns in older people with cardiac devices, and how these correlate with non-elective hospitalisation events. I. Data regarding (1) non-elective hospital attendances and (2) non-elective hospital events is required to provide the ‘outcome’ data for this study. II. Information regarding the dates of these events is required to facilitate predictive modelling. III. Data is required to be identifiable so we can link events with each study participant. IV. Data for the 12 months prior to date of recruitment is required as per study protocol and patient information sheets/consent forms. V. There are no less intrusive ways of achieving this data reliably. It is not feasible to rely on patients to remember dates of hospital attendances. Organisations involved in study 1. University of Manchester (sole data controller and data processor) Role: sponsor of study Data remit: facilitate transfer of data from NHS Digital, storage, processing and analysis of data 2. Manchester University NHS Foundation Trust (MFT) - **note previously known as Central Manchester NHS Foundation Trust** Role: study site Data remit: MFT will not receive any NHS Digital data. It will, however, provide study participant identifiers to NHS Digital, as this is where personal data from study participants is collected and stored, including the enrolment log, which links personal information with the study participant number (SPN). MFT will not have access to disseminated NHS Digital data. 3. Medtronic Inc Role: funding the post of Principle Investigator and some research costs associated with the study (from April 2018-April 2019) Data remit: Medtronic have no access to any of the NHS Digital data. It is not involved in this aspect of the study. Medtronic's role in the study is to facilitate processing of pseudo anonymised device downloads. 4. British Heart Foundation Role: funding the post of Principle Investigator (BHF Clinical Research Training Fellowship) and some research costs associated with the study (from April 2019 – April 2021) Data remit: The BHF have NO ACCESS to identifiable data or NHS Digital data. It is not involved in this aspect of the study. Will assist in dissemination of results. The PATTErn study is not being undertaken for commercial purposes, however there is commercial involvement in the study. The PATTErn study is an academic study, forming part of the PI's PhD. The PI's post at Manchester University is funded by Medtronic - a company which manufactures cardiac devices. Funding from Medtronic ended in April 2019 when funding was taken over by the British Heart Foundation. Funders have no influence over study results or outputs generated. Funders have no access to NHS Digital data.

Yielded Benefits:

The purpose of this request is to disseminate an additional field (ADMIMETH) to identify elective or non-elective admissions within the HES Admitted Patient Care dataset. This data is required in order to fulfill the purpose outlined in this agreement. At this current stage no Yielded Benefits have been realised due to not receiving the required data for processing. The Data Controller will update the Yielded Benefits following processing of the required data should a further renewal of the agreement be required.

Expected Benefits:

The main benefit of this study will be improved knowledge of the association between physical activity and hospitalization events in older people with cardiac devices. This will be of benefit to health and social care as outlined below: Researchers: Improved knowledge in this research area opens up new avenues of research within the field of ageing/heart failure research. If physical activity from cardiac devices is shown to correlate with hospitalization events, this may justify future studies investigating the impact of interventions based on this data e.g. physical rehabilitation, referral to Geriatric Medicine services. The PI will submit the results of the study as part of her PhD. Target date: PhD end date, November 2021, future research strategy plan January 2022. Clinicians: A greater understanding of the significance of physical activity data from cardiac devices will help health professionals make the best use of cardiac device data to serve their patients. For example, this study may lead to a greater emphasis on recording and reviewing activity data during routine device reviews. Further, down the line, this may lead to service improvements projects using activity data as a trigger for signposting to services such as community physiotherapy or exercise groups. Target date: Dissemination of work to improve knowledge of relevant health professionals: October 2021. Patients: This work will hopefully lead to improved patient care. This may not occur as a direct result of this study, rather further down the line as results inform service improvement projects. Participants in the study whom have consented to receive information on study results will be better informed on the significance of physical activity, and effort will be made to emphasize the importance of physical activity for the maintenance of heath and function. Recruitment aim = 150 patients. Target date for postage of lay summary to participants: April 2021 As per section 5(c) only patients able to consent in the English language are eligible for recruitment into the study, and all research outputs will be published in the English language only. The impact of this is that participation in the study and availability of results will be restricted to English speaking patients/members of the public. The benefits of the study may not be able to be extrapolated to reflect the broader population as the study has focused on a patient cohort who are able to provide written, informed consent in the English language.

Outputs:

Dissemination of Results When processing is complete, outputs will include: 1. Submission to peer review journal. The UoM supports open access publication. 2. Presentations at: a) Internal meetings at UoM and MFT b) Seminars and workshops at academic events (where appropriate) c) National and international academic conferences d) Patient engagement events 3. A ‘lay summary’ of results for study participants and interested non-academic parties. This is anticipated to take the form of a 2-page pdf document, which will be disseminated via: a) letters/emails to study participants b) PATTErn study webpage on publicly accessible UoM website c) British Heart Foundation literature/website/online publication 4. Reports to: a) UoM – in form of progress reports (study sponsor), PhD thesis and coursework b) British Heart Foundation – in form of progress reports (study part-funder and PhD fellowship funder) c) Medtronic Inc. – in form of progress reports (study part-funder) The target time for release of these outputs is April 2021. Level of data contained in outputs The majority of outputs will contain only aggregate level data with small numbers suppressed in line with HES analysis guide. Some ‘case study’ timelines for individual participants may be published to visualise the association between physical activity and non-elective hospital admission/attendance events – however all dates will be removed (dates replaced with timeline of day 0-365) and no data will be presented which could directly identify the individual e.g. specific details of reasons for hospital attendance/admission or multiple co-morbidity data. This is considered essential for displaying the clinical significance of activity data. Dissemination and communication approach Through the outputs stated above, the results of the PATTErn study will be disseminated to target audience of researchers, data scientists, academic groups, innovative technology-focused organisations and research participants. The British Heart Foundation (BHF) has established links with a wide variety of communities – and will help ensure output is brought to the attention of policy makers. Communication channels will include Academic channels – peer review journal manuscripts, presentation at conferences Website and newsletters – via UoM, BHF and MFT Open lectures and talks – public promotion of results via the Manchester Institute for Collaborative Research on Ageing (MICRA). We will present the results of the PATTErn study at at least one MICRA event. Exploitation of results/outputs The UoM has a research collaboration agreement in place with Medtronic outlining data and knowledge ownership and access rights. In line with UoM postgraduate research policy, all newly created knowledge, tools or technologies will be under the ownership of the UoM. Results will be shared with all stakeholders (including Medtronic) as stated above in ‘Level of data contained in outputs’. Funders will have no influence on the study results. As part of the study inclusion/exclusion criteria, patients must be able to provide written, informed consent in the English language in order to be recruited. This restriction was put in place primarily due to the lack of resources at the recruiting site to (a) provide a service to translate all study documents and (b) provide a face-to-face translator for study consent and assessment. Outputs will be published in the English language only. These factors will restrict participation and dissemination of work to English speaking members of the public. The impact of the English language only consent on the outputs of the study is that they may well be limited to only reflecting outcomes for a cohort of patients who are able to provide written, informed consent in the English language and the benefits of the study may also be limited to this cohort and may not be able to be extrapolated across to reflect the broader population.

Processing:

Sharing of identifiable data from participants with NHS Digital is included in the Participant Information Sheet (which all participants must confirm they have read before consenting to participate), and is explicitly stated (with corresponding participant initials) on the consent form. Data Flows Information transferred to and from NHS Digital will be record-level, directly identifiable patient data. Personal data for each study participant will be provided to NHS Digital by the study site - Manchester University NHS Foundation Trust (MFT). This data will be Study ID (unique 6-digit number) and personal identifiers only: NHS Number, Date Of Birth, Gender, Postcode. NHS Digital data will then be provided to the study sponsor - the University of Manchester. Data will be received directly into the Data Safe Haven (see below). The output of NHS Digital data received will be record-level. Data will be tagged with study participant number (SPN), and personal identifiers (NHS Number, Date of Birth, Gender, Postcode). Disseminated data from NHS Digital will not be shared with any other organisation (including MFT and Medtronic Inc). Medtronic Inc role is the funding the post of Principle Investigator and some research costs associated with the study they have a remit to facilitate processing of pseudonymised device downloads. No access to identifiable data or NHS Digital data is permitted the information is limited to that of the device information. The Data Safe Haven (DSH) NHS Digital data will be received via the Data Safe Haven (DSH) at the University of Manchester (UoM). Access to the DSH is only granted to substantive employees of the UoM whom have completed the University’s Data Protection Training and authorised by the Research Governance, Ethics and Integrity team (RGEIT). Each DSH user has a project file within the DSH, which is accessible to that user only. Access to the Data Safe Haven is restricted to on-campus connectivity only. Only the Principal Investigator (PI) and Study IG Lead are authorised to move data in or out of the DSH. Any person in addition to the PI or study IG lead requesting access to the PI’s project folder within the DSH will require approval by either the PI or study IG lead, and authorisation by the RGEIT. No personnel outside the direct employment of the University of Manchester will have access to the NHS Digital data. NHS Digital data is downloaded into the DSH via a single point - a static IP addressed laptop within the RGEIT office. For this study, NHS Digital data will be downloaded by the PI. The PI will create a separate password-protected sub-folder within their project folder, which can only be accessed by either the PI or Co-study IG Lead. After logging on to the laptop with their UoM username and password, the PI will access the DSH using the University’s 2-factor authentication Service (Duo) to verify their identity. Data from NHS Digital is transferred into the separate password-protected sub-folder in PIs project folder within the DSH using the Secure Transfer Service. One the data is stored; this will be manually reviewed by the PI to ensure there are no obvious issues with the data. The PI will then create a separate pseudonymised data file, removing all personal identifiers, leaving the SPN as the only record-level identifier. Once this pseudonymised data file is checked and authorised by both the PI and Co-study IG Lead, the original NHS Digital data file will be destroyed. Identifiable participant data will not leave the DSH. The pseudonymised NHS Digital data file will be stored within the PI’s project file in the DSH. Here it will be combined with other study data to create a pseudonymised research database for analysis. This data includes: NHS Digital data for each participant. This will be combined with data collected from: 1) The participant directly using study questionnaires and physical assessments (recorded onto the case report form) 2) Cardiac device data (SPN-coded) 3) Data from medical records (recorded onto case report forms) The pseudononymised research database will remain within the DSH. The pseudonymised research database will be used for the majority of all analysis. Data will then be used to analyse temporal trends in physical activity surrounding non-elective hospitalisation episodes. Analysis will be performed using software within the DSH (mainly Excel, SPSS, R, GraphPad). Data Flow out with the DSH In order to facilitate the movement of data out with the DSH, an anonymised research database will be created within the DSH. Anonymization will take place in line with ICO guidance. Any data leaving the DSH will be reviewed by either the PI or study IG lead to ensure it is compliant with UoM guidance, HES analysis guidance and ICO anonymization guidance all releases of data will be aggregated with small numbers suppressed in line with the HES analysis guide. No NHS Digital data will be transferred outside the UK. Data Linkage NHS Digital data will be combined with data collected as part of the PATTErn study as stated above and explained in the patient information sheet. It will not be linked with any other databases. Study Participant Data Anonymity Once data has been pseudonymised the identifiers will be securely destroyed and there will be no attempt to re-identify individual participants. Additional Information One aspect of the study will take place out with the UK; however, this will not involve NHS Digital data. Pseudonymised cardiac device download files require processing by the Medtronic technical team whom are based in Maastricht, the Netherlands. These files will be transferred across and back using an online encrypted transfer service. This data will then be added to the pseudo anonymised research database at the University of Manchester. This process does not involve any NHS Digital data. 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 ie: employees, agents and contractors of the Data Recipient who may have access to that data).


Project 7 — DARS-NIC-204376-Y0V5Y

Opt outs honoured: Yes - patient objections upheld (Section 251 NHS Act 2006)

Sensitive: Non Sensitive, and Sensitive

When: 2020/01 — 2020/01.

Repeats: One-Off

Legal basis: Health and Social Care Act 2012 – s261(7)

Categories: Identifiable

Datasets:

  • Hospital Episode Statistics Admitted Patient Care
  • Hospital Episode Statistics Outpatients
  • Hospital Episode Statistics Critical Care
  • Hospital Episode Statistics Accident and Emergency
  • HES:Civil Registration (Deaths) bridge
  • Civil Registration - Deaths

Objectives:

Modern day pacemakers not only treat slow and fast heart rhythms but also have the ability to detect key changes in the patient’s overall condition that can help identify patients who may be suboptimally managed or unwell. The health care team in this unit at Manchester University Hospital NHS Foundation Trust (MUFT) analyses data from patients with Medtronic implanted cardiac devices on remote follow-up. This is collected by transmitting downloads from home via CareLink, an internet based service which allows doctors to remotely monitor an implantable cardiac device and monitor the care of patients managed by the service. MUFT use the results to improve the care delivered to patients. This process is called service evaluation. This particular service evaluation is called Triage-HF Plus and the purpose of the evaluation is to improve patient pathways and guide future models of care for patients followed up by remote monitoring. ‘Triage HF Plus’ is a new clinical pathway which was implemented at MUFT in June 2016. This evaluation was discussed with the Health Research Authority (HRA) at time of onset and was designated as a non-research study as the aim was to evaluate current care. The evaluation started in June 2016, and MUFT now have 3-years of cardiac device data available. The pathway dictates all patients who perform a transmission from home which is calculated to have a high ‘Heart Failure Risk Score (HFRS) are contacted by telephone to establish if they have any symptoms. If patients have symptoms of worsening heart failure after telephone assessment they are deemed ‘triage positive’, and if not, ‘triage negative’. Clinical action is then taken directly at the time of phone call. Patients with a low- or medium- HFRS are managed as per their usual care plan. In order to provide essential outcome data to evaluate the impact of this new clinical pathway, MUFT require additional short– and medium- term health care utilisation and death data for all patients with devices facilitating remote-monitoring at MUFT during the evaluation period (21st June 2016 to 21st September 2018). Using the existing clinical data from patients with a HFRS enabled device, linked to NHS Digital Hospital Episode Statistics (HES) data, MUFT and academic partners based at the University of Manchester (UoM) will examine i) adverse outcomes at 30- and 90-days (all-cause hospitalisation, heart failure hospitalisation, mortality) ii) adverse outcomes at 12-months. This longer-term data will help MUFT evaluate the safety of the pathway, and help guide future changes to the pathway (for example, intensifying monitoring for high HFRS patients, and relaxing face-to-face monitoring frequencies for low HFRS patients). iii) Examine healthcare utilisation across the different Heart Failure Risk Score groups (this will require access to outpatient data as well as data for hospitalisation/death/use of outpatient services in the 12 months prior to implementation of the pathway. (iv) Examine healthcare utilisation for ‘Triage-positive’ and negative cases This falls under the medical purpose in s251 as defined as a ‘medical diagnosis’ for the provision of care and treatment. The data requested from NHS Digital will be used solely for this project. Results will be published in a peer-review medical journal, and presented at conferences. Results will likely feed into a bigger programme of service improvement by better use of cardiac device remote monitoring systems. No elements of this work are taking place outside of England/Wales. The data requested is justified under Article 6(1)e of the General Data Protection Regulation. The collection of data from NHS Digital is essential to provide robust outcomes data for patients managed by the new pathway in order to establish if outcomes are improved. This is in the public interest as: (1) results from the Triage-HF Plus evaluation will advance academic and clinical knowledge regarding the true utility of health-related data, obtained from implanted devices, to complement the monitoring of heart failure stability, (2) whether managing patients using the Triage-HF Plus pathway results in improved care and outcomes for patients, (3) results from the Triage-HF Plus evaluation may lead to either further clinical studies or direct change in clinical service provision with the aim of improving patient care. The data requested is justified under Article 9(2)h of the General Data Protection Regulation. As laid out above, processing is necessary for the purposes of preventive or occupational medicine, for the assessment of the working capacity of the employee, medical diagnosis, the provision of health or social care or treatment or the management of health or social care systems and services (see processing activities). Hospitalisation, healthcare utilisation and mortality data from NHS Digital will allow MUFT to investigate the impact of the Triage HF Plus clinical pathway. NHS Digital data will provide dates and information about hospital attendances, admissions, Outpatient attendances and deaths. These events will be added to timelines at an individual record level to facilitate predictive modelling. Background to the evaluation Triage HF Plus (Identifying and Managing Heart Failure Episodes In Ambulatory Patients With A Cardiac Implantable Electronic Device (CIED) Using Device Based Diagnostics and Automated CareLink Alerts) is a non-research, service evaluation project. At the Trust, Medtronic CareLink transmissions from the ambulatory Cardiac Implantable Electronic Devices (CIED) population are routinely reviewed as part of standard clinical practice and this practice is endorsed by international guidelines. All patients have provided written informed consent ahead of enrolment onto the CareLink remote monitoring network and patients provide verbal informed consent to having their CareAlerts (capable of generating automatic alerts) enabled. CareAlerts are a functionality of the device that need to be enabled in order for alerts about abnormal health-related data to be sent electronically to the clinical team. In patients where alerts have not been enabled, no alert will be sent to the clinical team. Patients will have attended a face-to-face visit to have these alerts enabled as part of routine care at some point during their follow-up. Advances in heart failure diagnostics mean that it is also possible to identify patients at risk of worsening heart failure using health-related data recorded by the CIED. The Medtronic ‘Heart Failure Risk Score’ (HFRS) is an algorithm that uses input from health-related data recorded by the CIED stratify patients as low-, medium- or high-risk of a heart failure event in the next 30 days. Since June 2016 MUFT have been using HFRS-based alerts for the remote identification of worsening heart failure in the population of CIED patients who are enrolled on the CareLink platform. The Research and Innovation department at Manchester Foundation Trust reviewed the service evaluation proposal to respond to Medtronic CareLink HFRS-based alerts and approved the use of the One Clinical Service database as a data collection tool to support the evaluation. One Clinical Service (operated by Medtronic) is a secure care management service/ database that pulls data from the 'cloud' where device-related data sits. Using OCS clinicians can view physiological data and add clinical data in a secure environment. The hospital is the data controller, while Medtronic is the data processor on behalf of the Hospital. On the basis that MUFT routinely respond to CareLink alerts in the existing practice, the Triage HF Plus service evaluation represents an extension of the existing clinical service. High-risk HFRS alerts prompt a telephone consultation with the patient, using the information provided by the remote transmission and patient reported symptoms appropriate clinical actions in line with clinical guidelines are instigated – this has become known as the ‘Triage-HF Plus pathway’. Service evaluation has so far confirmed the Triage-HF Plus pathway appears to accurately identify patients whom would benefit from a telephone assessment of heart failure stability and general health. Purpose of Request The purpose of this evaluation is to evaluate the Triage HF Plus pathway for the remote monitoring of heart failure stability and establish the accuracy of the pathway to predict adverse events and healthcare utilisation. I. Data regarding (1) non-elective hospital attendances and (2) non-elective hospital admissions (3) Outpatient attendances and (4) death are required to provide the outcome data for this evaluation. II. Information regarding the dates of these events is required to facilitate predictive modelling. III. Data is required to be identifiable so MUFT can link events with each individual in order to facilitate predictive modelling. IV. Data for the 12 months prior to date of enrolment in the pathway is required to examine healthcare utilisation associated with implementation of the Triage HF Plus clinical pathway. V. There are no less intrusive ways of achieving this data reliably. Proposed use of data Recognising that remote monitoring of health-related data derived from CIEDs offers the potential to reconfigure clinical services based on their clinical condition, MUFT wish to examine health care utilisation (HCU) and mortality in the low-, medium- and high-HFRS groups to examine and validate the current care pathways and to assist in the development of future state care pathways. In view of this consideration, MUFT are applying to collect -12 month, +30 day, +90 day and +12 month outcome data (elective / non-elective hospital attendances and mortality) for the patients enrolled in the evaluation. Cohort MUFT have approximately 500 patients at the trust who are implanted with HFRS enabled devices capable of stratifying the risk of heart failure events based on health-related data, who have consented to remote monitoring using the CareLink platform. Section 251 support is also in place to facilitate linkage with HES and Civil Registration Death data. The section 251 approval allows the flow of the following identifiers to flow from Manchester University NHS Foundation Trust to NHS Digital. NHS number Date of birth Sex Postcode As MUFT is a tertiary cardiac centre serving the wider area of Greater Manchester, East Cheshire and Lancashire, MUFT require central NHS Digital data to accurately establish local hospital attendances. MUFT also require civil registration data from NHS Digital as this is more reliable than mortality data collected at MUFT. Support sought MUFT are requesting time limited access to allow NHS Digital to undertake linkage of existing data held by the clinical team to HES /Civil registration data to collect follow-up / additional data on an existing dataset that is already held by the trust. By linking the two datasets MUFT will obtain additional information to help develop future state care models for CIED patients enrolled in remote monitoring programs. The Confidentiality Advisory Group and Secretary of State for Health and Social Care have considered the application at the precedent set CAG and have authorised Time Limited Access to undertake the record linkage/ sample validation and to anonymise the data. MUFT have engaged informally with NHS Digital who will act as a third party to carry out data linkage and confirmed the minimum data identifiers required to undertake the linkage. Organisations involved in evaluation 1. Manchester University NHS Foundation Trust (MUFT) - **note previously known as Central Manchester NHS Foundation Trust** Role: (i) Sole data controller (ii) Data processor Data remit: Provide Participant ID Numbers (PIDN) and patient identifiers to NHS Digital to facilitate linkage. * NHS Number * Date of Birth * Gender * Postcode NHS Digital will return a pseudonymised extract linked HES-Civil Registration Deaths to MUFT. The data sent by NHS Digital does not need to be re-identified once received back by MUFT. MUFT have already prepared a pseudonymised clinical dataset (device and medical record data) which will be analysed along side the pseudonymised datasets returned from NHS Digital. Both datasets use a unique random 6-digit PIN that can be used to link the two datasets. This avoids the need to re-identify any data. MUFT will only be handling pseudonymised (de-identified) data from this point forwards. MUFT will also facilitate the transfer of psuedonymised data to and from NHS Digital, storage, processing and (in part) analysis of data. 2. University of Manchester Role: Data processor Data remit: Analysis of pseudonymised research database. UoM will receive psuedonymised NHS Digital data and this will be transferred via MUFT (data controller). 3. Medtronic Inc Role: Providing patient level data concerning health-related data from Medtronic CareLink platform prior to linkage by NHS Digital. Data remit: Medtronic have no access to any of the NHS Digital data. It is not involved in this aspect of the data processing. Medtronic's role in the evaluation is to provide Heart Failure Risk Status (HFRS) data to MUFT. As such Medtronic is neither a data controller or data processor within this agreement. Should their role change the agreement will require an amendment to NHS Digital and necessary approvals sought. This agreement covers data for the cohort of participants covered by Section 251 approval.

Expected Benefits:

The main benefit of this evaluation study will be a greater understanding of the utility of a combined HFRS/telephone triage service i.e. the Triage HF Clinical Pathway to identify patients at risk of 30-day hospitalisation or death. If results show good diagnostic utility (and The health economics evaluation confirms low burden on implementation) – this may contribute to a change in national guidance – upgrading recommendations to more strongly supporting this strategy is incorporated into routine clinical practice. If results show poor diagnostic utility, this may contribute to a downgrading of current guidance. On a local level, results will help MUFT to adapt the current service provided to cardiac device patients to improve care. For example, if patients are being admitted to hospital within 30 days of a high alert despite feeling well at the time of alert, MUFT may implement a new pathway streamlining all patients to have a face-to-face clinical review. This will benefit patients by improving patient care, and the NHS by evaluating the best service delivery. MUFT anticipate it would take 2-3 years to fully implement and evaluate significant new changes. Another benefit of this study will be improved knowledge of the utility of the Triage-HF Plus clinical pathway as a pragmatic screening tool to identify patients at increased risk of hospitalisation. This will be of benefit to health and social care as outlined below: Researchers: Improved knowledge in this research area opens up new avenues of research within the field of device/heart failure research. If device data is shown to accurately identify patients at increased risk of heart failure hospitalisation then this may justify modifying clinical pathways and facilitate the rapid scale up implementation of this pathway across the UK. Publication and dissemination of data is expected to commence in May 2021. Clinicians: A greater understanding of the significance of health-related data from cardiac devices will help health professionals make the best use of cardiac device data to serve their patients. For example, this study may lead to a greater emphasis on enrolling patients onto remote monitoring platforms to enable abnormal results to be reviewed by clinical teams. Target date: Dissemination of work to improve knowledge of relevant health professionals: May 2021. Patients: This work will hopefully lead to improved patient care. This may not occur as a direct result of this evaluation, rather further down the line as results inform service improvement projects.

Outputs:

Results will be submitted to peer-review journals and presented locally at meetings, and at national/international conferences. Local meetings, held in Greater Manchester, will be attended by clinicians, healthcare practitioners, healthcare scientists, clinical managers, heart failure charities and patients. No data presented will be identifiable. Small numbers will be suppressed in all outputs in line with the HES analysis guide to avoid risk of re-identification – any data with less than 5 patients will not be shown. Interested audiences will be health professionals – but will not be restricted i.e. results will be available to the public. Dissemination of Results When processing is complete, outputs will include: 1. Submission to peer review journal. 2. Presentations at: a) Internal meetings at UoM and MUFT b) Seminars and workshops at academic events (where appropriate) c) National and international academic conferences d) Patient engagement events 3. A lay summary of results for patients and interested non-academic parties will be placed on the Research Webpage for Manchester Heart Centre. a) letters/emails to study participants b) MUFT, Health Innovation Manchester, UoM and on publicly accessible websites c) Results may be disseminated in the form of progress reports for interested parties (may include Dr Taylor’s PhD thesis). The target time for release of these outputs is May 2021. Level of data contained in outputs All outputs will contain only aggregate level data with small numbers suppressed in line with HES analysis guide. Some case study timelines for individual participants may be published to visualise the association between health related device data and non-elective hospital admission/attendance events however all dates will be removed (dates replaced with timeline of day e.g. 0-365) and no data will be presented which could directly identify the individual e.g. specific details of reasons for hospital attendance/admission or multiple co-morbidity data. This is considered essential for displaying the clinical significance of device data. Dissemination and communication approach Through the outputs stated above, the results of this evaluation will be disseminated to target audience of researchers, data scientists, academic groups, innovative technology-focused organisations and research participants. Health Innovation Manchester (HInM) has established links with a wide variety of communities and will help ensure output is brought to the attention of policy makers and industry collaborators. Communication channels will include Academic channels peer review journal manuscripts, presentation at conferences Website and newsletters via UoM, (Health Innovation Manchester (HInM) and MUFT Open lectures and talks public promotion of results via HInM public engagement meetings. MFT clinicians and academics will present the results of this Triage-HF Plus evaluation at at least one cardiology congress. Exploitation of results/outputs MUFT has a research collaboration agreement in place with Medtronic outlining data and knowledge ownership and access rights. Results will be shared with all stakeholders (including Medtronic who have supplied the health-related data from implanted devices) but Medtronic will only see aggregated outputs of the NHS Digital data. Stakeholders will have no influence on the evaluation results.

Processing:

NHS Digital data will be linked with individual patients in the study cohort by Participant ID Number (PIDN). Both datasets use a unique random 6-digit PIDN that can be used to link the 2 datasets. This avoids the need to re-identify any data. Manchester University NHS Foundation Trust (MUFT) will act as the co-coordinating organisation. MUFT will provide NHS Digital with identifiers alongside corresponding PIDN for each patient in the cohort, and receive in return NHS Digital data linked with PIDN. No data will flow to other organisations not noted in this agreement or outside the UK. All organisations party to data sharing 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 ie: employees, agents and contractors of the Data Recipient who may have access to that data). Sharing of a minimal identifiable data set from participants with NHS Digital is outlined in the approved CAG Section 251 application. Using information booklets and posters in waiting rooms MUFT have stated the plans to use routinely collected health-data from implanted devices to inform the development of new data-driven, technology enabled remote monitoring pathways for patients with heart failure who also have implanted devices. Data Flows Information transferred to NHS Digital will be record-level, directly identifiable patient data. Data received from NHS Digital will be record-level pseudonymised data. Personal data for each study participant will be provided to NHS Digital by the data controller (Manchester University NHS Foundation Trust, MUFT). Data transfer will occur via an encrypted data transfer service. This data will be PIDN (unique random 6-digit number) and personal identifiers only: NHS Number, Date of Birth, Gender, Postcode. Linked NHS Digital data will then be provided to the data controller (MUFT) via an encrypted transfer system. The output of NHS Digital data received will be record-level. Data will be analysed with PIDN only. Disseminated pseudonymised data from NHS Digital will only be shared with named individuals working at MUFT, and the academic collaborators at UoM. Data will not be transferred to any other organisation. A dataset with aggregated data with small numbers suppressed will be made available and will be viewed by collaborators outwith MUFT and UoM for the purpose of supported analysis. The collaborator is Medtronic, who are supporting the health-economic analysis of the new clinical pathway. Data Safe Haven (DSH) Pseudonymised NHS Digital data processed by the data controller (MUFT) will be received via the DSH at the University of Manchester (UoM) where additional processing and analysis can be undertaken. Access to the DSH is only granted to substantive employees of the UoM whom have completed the Universities Data Protection Training and authorised by the Research Governance, Ethics and Integrity team (RGEIT). Each DSH user has a project file within the DSH, which is accessible to that user only. Access to the DSH is restricted to on-campus connectivity only. Only named individuals are authorised to move data in or out of the DSH. No personnel outside the direct employment of the University of Manchester will have access to the processed NHS Digital data. NHS Digital data is downloaded into the DSH via a single point - a static IP addressed laptop within the RGEIT office. For this evaluation, NHS Digital data will be received by the data controller (MUFT). The data controller will create a separate password-protected sub-folder within their project folder on the MUFT server, which can only be accessed by named individuals. Once the data is stored; this will be manually reviewed to ensure there are no obvious issues with the data. Here it will be combined with other evaluation data to create a pseudonymised research database for analysis. This data includes: NHS Digital data for each participant. This will be combined with data collected from (1) Cardiac device data and (2) Data from medical records (stored in pseudonymised database) to create a pseudonymised research database. The pseudonymised research database will remain within the MUFT and UoM. The pseudonymised database will be used for all analysis. Once this pseudonymised data file is checked and authorised the original NHS Digital data file will be destroyed. The pseudonymised research database will be transferred from MUFT to UoM via the DSH. After logging on to the laptop with their UoM username and password, the named academic analysts will access the DSH using the University’s 2-factor authentication Service (Duo) to verify their identity. Data is transferred into the separate password-protected sub-folder in PIs project folder within the DSH using the Secure Transfer Service. Record level data will not leave the DSH/MUFT secure servers. The pseudonymised NHS Digital data file will be stored within a secure project file in the DSH. Data will be stored in the DSH until such time that all peer-reviewed publications have been disseminated and the clinical team are confident that no further analysis will be required (maximum storage 10 years). Data analysis will be performed using software within the DSH (mainly Excel, SPSS, R, GraphPad). No NHS Digital data will be transferred outside England and Wales. NHS Digital data will be combined with data collected as part of the TRIAGE evaluation project as stated above. It will not be linked with any other databases. Outside MUFT, it will not be possible to re-identify individuals based on data provided. Access to the key linking PIDN and patient identifiers will be kept securely at MUFT with access granted to named individuals only. This file will be destroyed once all data analysis is complete and results disseminated and peer review complete.