Systematic approach to outcome assessment from coded electronic healthcare records in the DaRe2THINK NHS-embedded randomised trial

Abstract

Background: Improving the efficiency of clinical trials is key to their continued importance in directing evidence-based patient care. Digital innovations, in particular the use of electronic healthcare records (EHR), allow for large-scale screening and follow-up of participants. However, it is critical these developments are accompanied by robust and transparent methods that can support high quality and high clinical value research. Methods: The DaRe2THINK trial includes a series of novel processes, including nationwide pseudonymised pre-screening of the primary care EHR across England, digital enrolment, remote e-consent, and no-visit follow-up by linking all primary and secondary care health data with patient-reported outcomes. Findings: DaRe2THINK is a pragmatic, healthcare-embedded randomised trial testing whether earlier use of direct oral anticoagulants in patients with prior or current atrial fibrillation can prevent thromboembolic events and cognitive decline (www.birmingham.ac.uk/dare2think). This paper outlines the systematic approach and methodology employed to define patient information and outcome events. This includes transparency on all medical code lists and phenotypes used in the trial across a variety of national data sources, including Clinical Practice Research Datalink Aurum (primary care), Hospital Episode Statistics (secondary care) and the Office for National Statistics (mortality). Interpretation: Co-designed by a patient and public involvement team, DaRe2THINK presents an opportunity to transform the approach to randomised trials in the setting of routine healthcare, providing high-quality evidence generation in populations representative of the community at-risk.

CODE-EHR best-practice framework for the use of structured electronic health-care records in clinical research

“…Technological advances in EHR systems will help, such as the ability to retrieve EHR data on a daily basis to support clinical trials. 21 Taking advantage of the many real-world data initiatives to support new research: • Government agencies, regulators, charities, and professional bodies have initiated programmes for better use of realworld data that can support further activity and dissemination.…”

“…The stakeholder consensus meetings highlighted clarity of methods as an important concern for future research, supported by evidence that few studies provide sufficient detail to understand the research process. 26,27 The emergence of registry-based and EHRbased randomised controlled trials (NCT04700826; NCT01093404) 21,28 reinforces the imperative for improvements to define new concepts for quality research. With the development of robust analytics supported by machine learning algorithms, 29 similar approaches have already been used to support artificial intelligence in health care.…”

CODE-EHR best practice framework for the use of structured electronic healthcare records in clinical research

“…The stakeholder consensus meetings highlighted this area as a key concern for future research, supported by evidence that very few studies have provided sufficient detail to understand the research process. 24 25 The advent of registry and EHR-based randomised controlled trials 21 26 27 reinforces the imperative to see improvements in these areas, and to define new concepts for quality research. With the development of robust analytics supported by machine learning algorithms, 28 similar approaches have already been used to support artificial intelligence in healthcare.…”

“…Technological advances in EHR systems will help, such as the ability to retrieve EHR data on a daily basis to support clinical trials. 21 Taking advantage of the many real world data initiatives to support new research. Government agencies, regulators, charities and professional bodies have initiated programmes for better use of real-world data that can support further activity and dissemination.…”

Fit for the future: empowering clinical trials with digital technology

“…Application of inclusion and exclusion criteria within the electronic health record (EHR) can provide pre-screening lists, reducing time to identify appropriate participants. 6,7 This may also reduce bias by broadening recruitment to more diverse patients. Advances in artificial intelligence are not only assisting in data analytics, but could provide a method to stratify those at-risk based on individual interacting comorbidities.…”