Profile
Dr Jai Prashar is an academic medical doctor and clinical data scientist, currently on the competitive Specialised (Academic) Foundation Programme pathway at Barts Health NHS Trust in London. He trained at University College London Medical School, where he graduated with a medical degree and intercalated BSc (Hons) in Mathematics and Computational Medicine.
He is an Honorary Research Fellow at the Centre for Clinical Pharmacology and Precision Medicine at Queen Mary University of London, working on post-hoc analyses of randomised controlled trials and epidemiological analysis of national health survey data in hypertension and cardiovascular health. He is an invited peer reviewer for several journals and has won several national academic awards, including the FPH’s Sir John Brotherston Prize in Public Health. He is passionate about data-driven approaches to service design and population health, including the application of machine learning to routine care data/RWD to improve healthcare outcomes, and the role of interoperability in the learning health system model. Clinically, he works actively as a medical doctor at the Royal London Hospital and other Barts Health sites across General and Emergency Medicine.
Dr Prashar leads on data analytics/data-strategy for the award-winning long COVID service at University College London Hospitals NHS Trust (working with the NHS England National Lead for Long COVID), where his work has contributed to the WHO international guidelines for the management of COVID-19. He is working with informatics and BI teams at Barts Health NHS Trust, where he co-leads on building and deploying artificial intelligence models predicting length of inpatient stay.
He has applied expertise in Python for data science, with advanced skills in data visualisation, geographic visualisation, complex survival analysis and time-to-event modelling, survey data analysis, regression and causal inference methods, meta-analysis, supervised and unsupervised machine learning, and foundational proficiencies in Tableau, R, SQL and AWS Cloud including Amazon EC2, S3 and Lambda.
He is currently a sub-investigator on the SCRATCH-HTN trial (NIHR202116, £1.1 million), investigating the efficacy of neuromodulation for uncontrolled hypertension. He has several years’ experience working with EHR/usual care outpatient data and has contributed significantly to WP1 of the STIMULATE-ICP long Covid trial at UCL (COV-LT2-0043, £7.0 million - at time of award, the largest long Covid grant worldwide).
Further information
LinkedIn
Research
Group members
Dr Ajay Gupta
Summary
Jai’s research at QMUL, working under Dr Ajay Gupta, focuses on the application of survival and time-to-event analyses to clinical trial datasets and using causal inference methods to infer effects of medications on long-term cardiovascular outcomes. Previously, he has co-led on projects investigating national trends in the prevalence of cardiovascular risk factors using the Health Survey for England datasets.
His work and interests in long COVID are focused on modelling prognostic factors in recovery, service design and models of care, and population health approaches (including for other post-viral syndromes). He has authored pieces on service design and public health considerations in long COVID, alongside his quantitative work at UCLH, which had uptake into the WHO international guidelines for the management of COVID-19.
At Barts Health NHS Trust, he is working on building artificial intelligence models (gradient boosting models) using Python to predict inpatient length of stay and deploying these for use by clinical and operational teams across the Trust. He is interested in delivering effective quality improvement, and is currently leading a large, multi-site quality improvement project aiming to improve effective overtime and exception reporting rates for doctors across the Trust.
He was previously appointed Honorary Research Fellow at Moorfields Eye Hospital (in Digital Health) where he worked on the evaluation of digital health interventions. He was a Junior Doctors Forum Chair at Barts Health (Whipps Cross site). He was previously President of MedTech UCL, a large multidisciplinary collaborative of students and professionals seeking to foster cross-disciplinary progress in digital health, and conceptualised and led the national UCL AI in Medicine Conference which was attended by over 400 delegates.
Teaching
Jai teaches on the MBBS programme at Barts and the London Medical School (Practical Therapeutics) and has co-directed the design and development of an extensive and well-received ‘Preparation for the Foundation Years’ teaching programme for final year medical students regionally, teaching key foundational skills in prescribing, examination and continuity of care.
He sits on the interviewing panel for Barts and the London Medical School, and has extensive experience in teaching medical students on the wards and providing career mentorship. He previously developed and implemented a national ‘Introduction to Python’ course for students and professionals.
