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 at I-X and in the Institute of Global Health Innovation, with research in Artificial Intelligence, cancer diagnosis and learning systems. 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), data semantics and clinical meaning, and machine learning based model validation and evaluation. Currently there are three areas of active research:
1. Cancer diagnosis in Primary Care. I have led a number of projects in the area of multi-modal data integration, analytics and model validation with a view to generate 'start of the consultation' differential diagnosis lists to improve the timeliness and accuracy of cancer early detection in primary care.
2. Computable Clinical Guidelines and Explainable AI. Completing the LHS cycle with a computational infrastructure for deploying guidelines as decision support linked to the EHR.. Supported byan EPSRC Global Health Development Project (www.ROAD2H.org). Adds explainability to individual guideline recommendations.
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.
MSc: I am vice chair of the Diploma/MSc in Digital Health Leadership and lead the module on Actionable analytics.
PhDs: Currently supervising three students. Please contact me if interested in AI in healthcare using heath data with applications in diagnosis.
I am an advisor to the Charity Long Covid Support
et al., 2023, Impact of Long COVID on productivity and informal caregiving., Eur J Health Econ
et al., 2023, Mapping and evaluating whole nation data flows: transparency, privacy, and guiding infrastructural transformation, The Lancet: Digital Health, Vol:5, ISSN:2589-7500, Pages:e737-e748
et al., 2023, ROAD2H: development and evaluation of an open-sourceexplainable artificial intelligence approach for managingco-morbidity and clinical guidelines, Learning Health Systems, ISSN:2379-6146
et al., 2023, Impact of primary to secondary care data sharing on care quality in NHS England hospitals, Npj Digital Medicine, Vol:6, ISSN:2398-6352, Pages:1-10
et al., 2023, Digital home monitoring for capturing daily fluctuation of symptoms; a longitudinal repeated measures study: Long Covid Multi-disciplinary Consortium to Optimise Treatments and Services across the NHS (a LOCOMOTION study), Bmj Open, Vol:13, ISSN:2044-6055