RSF seminar series: DExtER, a semi-automated epidemiology platform for electronic health record research

Learn more Watch now Add to Calendar 10/11/2022 01:30 PM 10/11/2022 02:30 PM Europe/London RSF seminar series: DExtER, a semi-automated epidemiology platform for electronic health record research Location of the event
Tuesday 11 Oct 2022
Time: 13:30 - 14:30

Event type

Virtual seminar

Audience type



A regular series of open invitation talks from leading voices in data science, AI, healthcare and those with lived experience dealing with multiple long-term conditions (MLTC). This seminar series is part of the AI for multiple long-term conditions: Research Support Facility (RSF) project.

Event title: DExtER, a semi-automated epidemiology platform for electronic health record research

Speaker: Professor Krishnarajah Nirantharakumar, Professor in Health Data Science and Public Health, Institute of Applied Health Research, University of Birmingham

About the event

The NHS provides healthcare to 66 million people, with over a million people utilising NHS services every day. Each of these encounters is an opportunity to learn however, this data is often poorly accessible and not in research-ready formats. 
In this session, we will learn more about University of Birmingham’s system, DExtER (PMID: 32856160), a semi-automated system for efficient, transparent and reproducible research. This brings together experts in epidemiology, data science and software engineering. Studies which used to take months, can now be undertaken within days. For example, DExtER enabled the analysis of >70,000 serum testosterone measurements in young women, identifying increased risk of non-alcoholic fatty liver disease and diabetes in women with androgen excess. This led to a major new experimental medicine study (DAISy-PCOS) stratifying risk and developing novel treatments to prevent metabolic complications in women with polycystic ovary syndrome, a lifelong condition affecting 1 in 10 women worldwide. DExtER will also support the recently awarded MuMPreDiCT consortium that will study the effect of multiple long-term conditions on pregnancy outcomes.
In the session, we will also hear about how such an approach has the potential to:

  • Enable representative recruitment for trials and mechanistic studies, by rapidly generating eligible participants based on inclusion and exclusion criteria by searching through millions of electronic patient records within minutes. It is also underpinning data-driven clinical trials, such as RADIANT, a trial to improve testing for diabetes in women with a history of gestational diabetes. Such trials use already-collected NHS health information to reduce the time taken for research, both for patients and NHS staff, with both recruitment and outcomes driven electronically. 
  • Support better clinical decision-making and quality improvement. Publications arising from DExtER have supported clinical decision-making across varied health conditions including idiopathic intracranial hypertension, a health condition that is common among young women. We have shown DExtER can help with audits and monitoring key performance targets. 
  • Answer clinical questions that arise during patient consultations within hours: termed an ‘informatics consult’, a tailored approach particularly important where there is lack of clinical trial evidence.

This talk is part of an ongoing series of events hosted by the AIM RSF, which aims to share information and learning on aspects of interest to the MLTC research community. 

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Dave Chapman

Programme Manager, AI for Multiple long-term conditions Research Support Facility