TRE-FX:Delivering a federated network of trusted research environments to enable safe data analytics

Trusted Research Environments (TREs) are secure locations in which data are placed for researchers to analyse. TREs host administrative data, hospital data or any other data that needs to remain securely isolated, but it is hard for a researcher to perform an analysis across multiple TREs, requesting and gathering the outputs from each one. This is a common problem in the UK's devolved healthcare system of geographical and governance boundaries. There are different ways of implementing TREs and the analysis tools that use them. A solution must be straightforward for existing, independent systems to adopt, must cope with the variety of system implementations, and must work within the "Five Safes" framework that enables data services to provide safe research access to data. TRE-FX assembled leading infrastructure researchers, analysis tool makers, TRE providers and public engagement specialists to streamline the exchange of data requests and results. The "Five Safes RO-Crate" standard packages up (Crates) the Objects needed for Research requests and results with the information needed for the tools and TRE providers to ensure that the crates are reviewed and processed according to Five Safes principles. TRE-FX showed how this works using software components and an end-to-end demonstrator implemented by a TRE in Wales. Two other TREs, in Scotland and England, are preparing to follow suit. Two analysis tool providers (Bitfount and DataSHIELD) modified their systems to use the RO-Crates. The next step is practical implementation as part of the HDR UK programme. Two large European projects will develop the approach further. TRE-FX shows that it is possible to streamline how analysis tools access multiple TREs while enabling the TREs to ensure that the access is safe. The approach scales as more TREs are added and can be adopted by established systems. Researchers will then be able to perform an analysis across multiple TREs much more easily, widening the scope of their research and making more effective use of the UK's data. If we had had this for COVID-19 data analysis, it would have super-charged researchers to be able to quickly answer pressing questions across the UK. This work was funded by UK Research & Innovation [Grant Number MC_PC_23007] as part of Phase 1 of the DARE UK (Data and Analytics Research Environments UK) programme, delivered in partnership with Health Data Research UK (HDR UK) and Administrative Data Research UK (ADR UK)

TRE-FX: Delivering a federated network of trusted research environments to enable safe data analytics

<p>Trusted Research Environments (TREs) are secure locations in which data are placed for researchers to analyse. TREs host administrative data, hospital data or any other data that needs to remain securely isolated, but it is hard for a researcher to perform an analysis across multiple TREs, requesting and gathering the outputs from each one. This is a common problem in the UK's devolved healthcare system of geographical and governance boundaries. </p><p>There are different ways of implementing TREs and the analysis tools that use them. A solution must be straightforward for existing, independent systems to adopt, must cope with the variety of system implementations, and must work within the "Five Safes" framework that enables data services to provide safe research access to data. </p><p>TRE-FX assembled leading infrastructure researchers, analysis tool makers, TRE providers and public engagement specialists to streamline the exchange of data requests and results. The "Five Safes RO-Crate" standard packages up (Crates) the Objects needed for Research requests and results with the information needed for the tools and TRE providers to ensure that the crates are reviewed and processed according to Five Safes principles. TRE-FX showed how this works using software components and an end-to-end demonstrator implemented by a TRE in Wales. Two other TREs, in Scotland and England, are preparing to follow suit. Two analysis tool providers (Bitfount and DataSHIELD) modified their systems to use the RO-Crates. The next step is practical implementation as part of the HDR UK programme. Two large European projects will develop the approach further. </p><p><strong>TRE-FX shows that it is possible to streamline how analysis tools access multiple TREs while enabling the TREs to ensure that the access is safe. </strong>The approach scales as more TREs are added and can be adopted by established systems. Researchers will then be able to perform an analysis across multiple TREs much more easily, widening the scope of their research and making more effective use of the UK's data. If we had had this for COVID-19 data analysis, it would have super-charged researchers to be able to quickly answer pressing questions across the UK. </p><p>This work was funded by UK Research & Innovation [Grant Number MC_PC_23007] as part of Phase 1 of the DARE UK (Data and Analytics Research Environments UK) programme, delivered in partnership with Health Data Research UK (HDR UK) and Administrative Data Research UK (ADR UK). </p&gt

Infrastructure and operating processes of PIONEER, the HDR-UK Data Hub in Acute Care and the workings of the Data Trust Committee: a protocol paper.

INTRODUCTION Health Data Research UK designated seven UK-based Hubs to facilitate health data use for research. PIONEER is the Hub in Acute Care. PIONEER delivered workshops where patients/public citizens agreed key principles to guide access to unconsented, anonymised, routinely collected health data. These were used to inform the protocol. METHODS This paper describes the PIONEER infrastructure and data access processes. PIONEER is a research database and analytical environment that links routinely collected health data across community, ambulance and hospital healthcare providers. PIONEER aims ultimately to improve patient health and care, by making health data discoverable and accessible for research by National Health Service, academic and commercial organisations. The PIONEER protocol incorporates principles identified in the public/patient workshops. This includes all data access requests being reviewed by the Data Trust Committee, a group of public citizens who advise on whether requests should be supported prior to licensed access. ETHICS AND DISSEMINATION East Midlands-Derby REC (20/EM/0158): Confidentiality Advisory Group (20/CAG/0084). www.PIONEERdatahub.co.uk

A Datasheet for the INSIGHT Birmingham, Solihull, and Black Country Diabetic Retinopathy Screening Dataset

Purpose: Diabetic retinopathy (DR) is the most common microvascular complication associated with diabetes mellitus (DM), affecting approximately 40% of this patient population. Early detection of DR is vital to ensure monitoring of disease progression and prompt sight saving treatments as required. This article describes the data contained within the INSIGHT Birmingham, Solihull, and Black Country Diabetic Retinopathy Dataset. Design: Dataset descriptor for routinely collected eye screening data. Participants: All diabetic patients aged 12 years and older, attending annual digital retinal photography-based screening within the Birmingham, Solihull, and Black Country Eye Screening Programme. Methods: The INSIGHT Health Data Research Hub for Eye Health is a National Health Service (NHS)–led ophthalmic bioresource that provides researchers with safe access to anonymized, routinely collected data from contributing NHS hospitals to advance research for patient benefit. This report describes the INSIGHT Birmingham, Solihull, and Black Country DR Screening Dataset, a dataset of anonymized images and linked screening data derived from the United Kingdom’s largest regional DR screening program. Main Outcome Measures: This dataset consists of routinely collected data from the eye screening program. The data primarily include retinal photographs with the associated DR grading data. Additional data such as corresponding demographic details, information regarding patients’ diabetic status, and visual acuity data are also available. Further details regarding available data points are available in the supplementary information, in addition to the INSIGHT webpage included below. Results: At the time point of this analysis (December 31, 2019), the dataset comprised 6 202 161 images from 246 180 patients, with a dataset inception date of January 1, 2007. The dataset includes 1 360 547 grading episodes between R0M0 and R3M1. Conclusions: This dataset descriptor article summarizes the content of the dataset, how it has been curated, and what its potential uses are. Data are available through a structured application process for research studies that support discovery, clinical evidence analyses, and innovation in artificial intelligence technologies for patient benefit. Further information regarding the data repository and contact details can be found at https://www.insight.hdrhub.org/. Financial Disclosure(s): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article

Exploring Regional Linked Data Capability for Research Phase 2: Exploring Variation in Acute Hospital Admissions

No existing national data feeds provide detailed and near-real time information on hospital admissions across the UK. Currently available national data feeds are dated, do not include people still in hospital, and lack detailed coding which can help differentiate between different conditions or diagnoses. Enabling a detailed, near-real time hospital admissions data feed of regional level data would provide vital data for priority research and health and care planning. This collaborative project builds on previous work (Phase 1) led by the Health Data Research UK (HDR UK) Regional Linked Health Data for Research Programme which aims to conduct ‘driver’ projects to explore data capability, data access and feasibility of enabling near real time hospital admissions linked data feeds at regional level. Phase 2 included 4 additional regions and implemented 2 driver use cases – this report summarises the driver use case led by the University of Sheffield which explored variation in acute hospital admissions across the regions. Use Case Insights: This driver use case identified that patients in the Emergency Care Dataset (ECDS) and Admitted Patient Care (APC) datasets were older in the Ambulatory Care Sensitive Conditions (ACSCs) groups on average than the non-ACSC groups. Deprivation was a key factor observed equally in ACSC and non-ACSC groups, and there was a high proportion of patients attending ED with ACSC. High variation existed between hospitals in terms of attendance for ACSC. Further research is needed to establish clearer criteria for potentially avoidable admissions and same day emergency care-eligible patients. Data Capability and Access Insights – Significant variance in data capability and access resulted in delays with clear opportunities for further harmonisation to promote more effective collaboration across multi regional data infrastructure. The report outlines key recommendations to promote harmonised and standarised collaborative working across multi regional data infrastructure