Planetary Health Informatics explained
Ethnicity, Health Equity and AI

In this video, Sara and her team discuss their study on ethnicity data in NHS England, and how they will use this research to improve health equity in research and practice. Find out more about the study here
Research groups
- Planetary Health Informatics
- Big Health Data Research
- Pharmaco- and Device epidemiology
- EHDEN - European Health Data & Evidence Network
- HDRUK - Health Data Research UK
- OHDSI - Observational Health Data Sciences and Informatics
- National Geographic Society - Early Career Explorer
- OPTIMA - Tackling cancer with artificial intelligence and real-world data
- UKRI NERC - Creating Digital Environments
- Centre for Statistics in Medicine - Research Group
- ATLAS Programme - ATLAS -Enhanced Recovery for Arthroplasty Patients
- STAR Programme - STAR - Support and Treatment After Replacement
Colleges
Sara Khalid
BE, MSc (Oxon), DPhil
Associate Professor of Health Informatics and Biomedical Data Science
- Wellcome Trust Accelerator Fellow
- Group Head - Planetary Health Informatics
- Senior Research Fellow in Biomedical Data Science and Health Informatics
- Machine Learning Lead - Pharmaco-device Epidemiology Group, NDORMS
- UKRI NERC Senior Fellow in Creating Digital Environments
- National Geographic Explorer - Remote Monitoring and Machine Learning
- Former Ambassador for Women in Data Science - University of Oxford
Health Informatics, Intelligent Patient Monitoring, Planetary Health, Real-world Data Science
RESEARCH
Sara leads the Planetary Health Informatics Group at the Centre for Statistics in Medicine (Oxford) and the Machine Learning and Big Data Team of the Health Data Sciences Section in NDORMS (Oxford) which she joined in 2016. She is also affiliated with the Institute of Biomedical Engineering (Oxford) where she completed her doctoral and post-doctoral research in the Biomedical Signal Processing and Image Analysis Groups.
Her research applies artificial intelligence to international real-world health data, in order to further our understanding of disease and fills the gaps in global health, leveraging common data models and federated network analytics. She works closely with clinicians, engineers, clinical and environmental epidemiologists, conservationists, data scientists, and public and patient groups in the UK, Europe, Latin America, South Asia, and Africa to co-create models for equitable and ethical solutions for planetary health problems.
Sara completed her DPhil in Engineering Science at the IBME, University of Oxford, as a Rhodes Scholar. Prior to that she received a Distinction for her MSc in Biomedical Engineering from the University of Oxford in 2009, as a Qualcomm Scholar. In 2007 she graduated with a BE in Electronics Engineering from the National University of Sciences and Technology in Pakistan.
Teaching and Supervision
Sara teaches a number of health data science courses at NDORMS and University-wide, and is Director of the "Observational health data science: epidemiology, machine learning, and health economics" course. She is also a faculty member at the NIHR BRC course "Data analysis: statistics - designing clinical research and biostatistics", and the "Real-world epidemiology with OMOP common data model" summer school organised by the Health Data Science Section at NDORMS.
Sara supervises a number of research students including DPhil and MSc students at Oxford, as well as UK and overseas PhD students. Interested students are welcome to get in touch.
Recent publications
Mitigating health inequities with machine learning: a nationwide cohort study developing and evaluating ethnicity-specific cardiovascular risk prediction models across 19 ethnically diverse populations in 2.5 million individuals with COVID-19
Journal article
Allery F. et al, (2026), Lancet Digital Health
AlphaEarth satellite embeddings for modelling climate sensitive diseases towards global health resilience
Conference paper
Nazir U. and Khalid S., (2026)
From most vulnerable to most valuable: elevating children in the climate and health agenda.
Journal article
Gulati A. et al, (2026), Lancet Child Adolesc Health
From most vulnerable to most valuable: elevating children in the climate and health agenda
Journal article
Gulati A. et al, (2026), Lancet Child and Adolescent Health
Evaluation of plant based indoor air purification in urban environments in Pakistan
Journal article
Abubakar HM. et al, (2026), Discover Atmosphere, 4
Genetic and epidemiological evidence linking respiratory and musculoskeletal diseases: shared risk factors and intervention windows.
Journal article
Murrin O. et al, (2026), Respir Res
Critical appraisal of fairness metrics for artificial intelligence-based clinical prediction models: a scoping review.
Journal article
Matos J. et al, (2026), Lancet Digit Health
Integrating field observations and machine learning for national-scale agricultural yield classification from sentinel-2
Poster
Nazir U. and Khalid S., (2026)
Evaluating large language models for clinical note processing: local fine-tuning and internal-external validation using electronic health records from South Asia.
Journal article
Hasheminasab SA. et al, (2026), BMC Med Inform Decis Mak, 26