case study in distributed team science in research using electronic health records

Abstract

IntroductionDue to various regulatory barriers, it is increasingly difficult to move pseudonymised routine health data across platforms and among jurisdictions. To tackle this challenge, we summarized five approaches considered to support a scientific research project focused on the risk of the new non-vitamin K Target Specific Oral Anticoagulants (TSOACs) and collaborated between the Farr institute in Wales and Scotland. ApproachIn Wales, routinely collected health records held in the Secure Anonymous Information Linkage (SAIL) Databank were used to identify the study cohort. In Scotland, data was extracted from national dataset resources administered by the eData Research & Innovation Service (eDRIS) and stored in the Scottish National Data Safe Haven. We adopted a federated data and multiple analysts approach, but arranged simultaneous accesses for Welsh and Scottish analysts to generate study cohorts separately by implementing the same algorithm. Our study cohort across two countries was boosted to 6,829 patients towards risk analysis. Source datasets and data types applied to generate cohorts were reviewed and compared by analysts based on both sites to ensure the consistency and harmonised output. DiscussionThis project used a fusion of two approaches among five considered. The approach we adopted is a simple, yet efficient and cost-effective method to ensure consistency in analysis and coherence with multiple governance systems. It has limitations and potentials of extending and scaling. It can also be considered as an initialisation of a developing infrastructure to support a distributed team science approach to research using Electronic Health Records (EHRs) across the UK and more widely.

Exploring barriers and solutions in advancing cross-centre population data science

“…This should include a review of the relevant instruments in discussion with legal experts and data controllers. This is also important because data centres will have differing operating models and means of data access which must be taken into account to determine the optimum combined model for an instance of cross-centre working to ensure safety and maximum data utility [23,31].…”

“…These were one pooling and four federated options: i) movement of data to one of the centres; ii) data remaining at each centre and accessed by a single analyst; iii) data remaining at each centre and accessed by separate analysts employed at each centre; iv) data access by an analyst using a brokered remote connection between the centres; and v) single analyst directing queries across distributed data. For data governance and resource reasons, they settled on a combination of models ii) and iii) whereby analysts employed at each centre were given simultaneous access to data at both sites using the same algorithm [23].…”

A Profile of the SAIL Databank on the UK Secure Research Platform

“…The other involves the development of federated data access, rather than moving the data. A variety of options is being explored including intercentre connections to allow a researcher to access data held at multiple sites, or to enable queries to be applied to distributed data [51].…”

Improving prescribing through big data approaches—Ten years of the Scottish Prescribing Information System

“…A total of 267 publications were identified over the period January 2009 to March 2019. After screening titles and abstracts, and removing duplicates and conference abstracts, 40 full publication articles were included and reviewed . The number of articles published by year for the period 2014–2018, totalling 37, is presented in Figure A; a further 3 studies were published in the first months of 2019.…”