Imperial College London


Faculty of MedicineDepartment of Surgery & Cancer

Chair in Medical Informatics and Decision Making



+44 (0)20 7594 3427brendan.delaney Website




506Medical SchoolSt Mary's Campus





I am a leading exponent internationally of the “Learning Health System’ (LHS) concept. Although my initial training in research was in heath technology assessment, real-world (pragmatic) clinical trials and clinical research in Family Medicine, since 2003 I have worked in the area of Clinical Informatics, being appointed to a Chair in Medical Informatics at Imperial in 2015 and elected one of the first 100 founding fellows of the new UK Faculty of Clinical Informatics in 2017. I have had wide exposure to European and US clinical informatics through workshops and symposia.  

I was the UK lead investigator on an NIH Clinical Research Roadmap project (The Electronic Primary Care Research Network. HHS268N200425212C) 2006-10. From 2010-15 I led a €9million EU FP7 programme, ‘TRANSFoRm: Patient Safety and Translational Research in Europe’.

TRANSFoRm set about using ontologies, data standards and models to create a common infrastructure for the LHS with three specific use cases (eSource for clinical trials, phenomics and clinical diagnosis.

Prior to moving to Imperial I was Wolfson Professor of General Practice at King's College London. At Imperial, I work in the Institute of Global Health Innovation, with research in Artificial Intelligence, cancer diagnosis and learning systems, eSource for clinical trials and global eHealth. I sit on the Cancer Research UK Population research funding panel and the Medical Research Council Methodology Research panel.

Currently there are three areas of active research:

1. LHS and guidelines. Completing the LHS cycle with a computational infrastructure for deploying guidelines as decision support linked to the EHR. Currently supported as an EPSRC Global Health Development Project (

2. Establishing a LHS for diagnosis. Currently moving from a prototype evaluated in a rich simulation to deployment in practices. Funded by CRUK.

3. Developing a clinical prediction rule for the risk of admission/death in acute Covid in Primary care. RECAP - funded by the Community Jameel Imperial College Covid-19 Excellence fund (and collaborating with Oxford University). RECAP



Sivan M, Rayner C, Delaney B, 2021, Fresh evidence of the scale and scope of long covid, The Bmj, Vol:373, ISSN:0959-8146

Espinosa-Gonzalez AB, Neves AL, Fiorentino F, et al., 2021, Predicting risk of hospital admission in patients with suspected COVID-19 in a community setting: protocol for development and validation of a multivariate risk prediction tool (RECAP) (Preprint), Jmir Research Protocols

Kostopoulou O, Tracey C, Delaney B, 2021, Can decision support combat incompleteness and bias in routine primary care data?, Journal of the American Medical Informatics Association, ISSN:1067-5027

Espinosa-González AB, Delaney BC, Marti J, et al., 2021, The role of the state in financing and regulating primary care in Europe: a taxonomy, Health Policy, Vol:125, ISSN:0168-8510, Pages:168-176

Greenhalgh T, Thompson P, Weiringa S, et al., 2020, What items should be included in an early warning score for remote assessment of suspected COVID-19? qualitative and Delphi study, Bmj Open, Vol:10, ISSN:2044-6055, Pages:1-26

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