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.  

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 am a member of the Medical Research Council Data Science Strategic Advisory Group.

My interests lie at the intersection of health services research (how to deal with patient problems equitably and efficiently), the use of data in research and service development and 'pressing' clinical problems. Currently there are three areas of active research:

1. Computable Clinical Guidelines and Explainable AI. 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. Cancer diagnosis in Primary Care. Establishing a LHS for diagnosis. Currently moving from a prototype evaluated in a rich simulation to deployment in practices. Funded by CRUK.

3. Evidence-based management and learning from data in COVID-19.  Firstly, 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 Secondly, rapid learning from clinical practice and establishing a learning platform for dealing with Long Covid (LOCOMOTION) - funded by NIHR.

I am an advisor to the Charity Long Covid Support

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Kourtidis P, Nurek M, Delaney B, et al., 2022, Influences of early diagnostic suggestions on clinical reasoning, Cognitive Research: Principles and Implications, Vol:7, ISSN:2365-7464

Greenhalgh T, Sivan M, Delaney B, et al., 2022, Long covid-an update for primary care., Bmj, Vol:378, ISSN:1759-2151, Pages:1-8

Espinosa-Gonzalez A, Prociuk D, Fiorentino F, et al., 2022, Remote COVID-19 assessment in primary care (RECAP) risk prediction tool: derivation and real-world validation studies, The Lancet Digital Health, Vol:4, ISSN:2589-7500, Pages:e646-e656

Mayor N, Meza-Torres B, Okusi C, et al., 2022, Developing a Long COVID Phenotype for Postacute COVID-19 in a National Primary Care Sentinel Cohort: Observational Retrospective Database Analysis, Jmir Public Health and Surveillance, Vol:8, ISSN:2369-2960

Meza-Torres B, Delanerolle G, Okusi C, et al., 2022, Differences in Clinical Presentation With Long COVID After Community and Hospital Infection and Associations With All-Cause Mortality: English Sentinel Network Database Study, Jmir Public Health and Surveillance, Vol:8, ISSN:2369-2960

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