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 (www.ROAD2H.org).
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
et al., 2021, An early warning risk prediction tool (RECAP-V1) for patients diagnosed with COVID-19: the protocol for a statistical analysis plan, Jmir Research Protocols, Vol:10, ISSN:1929-0748
et al., 2021, An Early Warning Risk Prediction Tool (RECAP-V1) for Patients Diagnosed With COVID-19: Protocol for a Statistical Analysis Plan, Jmir Research Protocols, Vol:10, Pages:e30083-e30083
et al., 2021, Challenges to implementing electronic trial data collection in primary care: a qualitative study, Bmc Family Practice, Vol:22
et al., 2021, Using Computable Phenotypes in Point-of-Care Clinical Trial Recruitment., Stud Health Technol Inform, Vol:281, Pages:560-564
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, Jmir Research Protocols, Vol:10, ISSN:1929-0748