Dr Mome Mukherjee

I am a health data scientist and health services researcher working at the intersection of routinely collected health data, health systems, and long-term conditions. My work focuses on using population-scale electronic health records (EHR) and administrative data to improve understanding of disease burden, healthcare use, outcomes and inequalities, and to support evidence generation that is relevant to clinical practice, policy and health system improvement. These work are informed by my prior experience working within the NHS, including roles in NHS Lothian Research Safe Haven and Public Health Scotland, where I worked on routinely collected data and research data infrastructures.

Much of my research to date has used asthma as an exemplar to explore how routinely collected NHS and administrative data can be analysed and interpreted to inform service delivery and quality improvement. My PhD by Research Publications (2025) at the University of Edinburgh on learning health systems for asthma in the NHS was based on eight papers for which I was the lead author. Across this body of work, I defined the research questions, designed the studies, led data specification and harmonisation across datasets, undertook analyses, and interpreted findings as the main researcher, working with Prof Sir Aziz Sheikh and multidisciplinary colleagues.

I conceived and led the first evaluation of primary care clinical coding for asthma and allergies, and brought together, for the first time, routinely collected NHS and administrative data across all four UK nations to generate comparable estimates of asthma epidemiology, healthcare utilisation, outcomes and costs. I led a study examining potentially modifiable risk factors, that might help explain reductions in asthma hospitalisations during the COVID-19 pandemic. I conceived and led the first national study of children admitted to paediatric intensive care with asthma.

Alongside quantitative and systematic review work, I designed and led a qualitative study examining barriers and facilitators to accessing and using near-real-time healthcare data, drawing on interviews with stakeholders across the Scottish health data ecosystem. More recently, I have led work translating epidemiological findings into applied outputs, specifically the development of a near-real-time asthma dashboard to support quality improvement in primary care.

An emerging focus of my work is on how routine data can better support learning within health systems. I am interested in the steps needed to move from descriptive and explanatory analyses towards more timely, actionable evidence that can inform clinical practice and policy, while being mindful of data quality, equity and real-world constraints. I am currently working on developing risk prediction models for people with and without respiratory disease and for GP practices to support prevention and planning for winter pressures in the NHS.

Looking ahead, I aim to develop transferable methods for using UK-wide routine data to support learning health systems for long-term conditions, multimorbidity, reducing unwarranted variation in care, and prevention.

I am keen to collaborate with clinicians, researchers, policymakers and data scientists who are interested in using EHR and other routine data to address clinically and policy-relevant questions. I particularly welcome collaborations at the study design stage, where routinely collected data may offer value, as well as partnerships involving applied analytics, mixed-methods research, grant development and the delivery of population-based studies.