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Dr Elizabeth Ford

A photo of BSMS staff member Elizabeth Ford standing with mountains in the background on a sunny day

Dr Elizabeth Ford (MA, DPhil)

Associate Professor
E: E.M.Ford@bsms.ac.uk
T: +44 (0)1273 641974
Location: Watson Building, University of Brighton, Falmer, Brighton, BN1 9PH

Areas of expertise: Health data science; linked health and social care data; epidemiology; mental health; dementia

Research areas: Primary care and public health, dementia

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Biography

Elizabeth Ford is an Associate Professor in Health Data Science. Her research focuses on data science, data governance and public engagement. Her core interests are in analysing primary care data and linked routinely collected health data to identify areas for improvement in healthcare. She has worked on developing early detection models for mental health conditions and dementia, and has used NHS data to understand groups at risk of delayed diagnosis of long-term conditions. Elizabeth works with NHS, public health, and public stakeholders across Sussex, Kent and Surrey to support the use of routinely collected data in research, and is actively involved in the development of a sub-national secure data environment in Kent and Sussex.

She is Lead for Data Science in the NIHR Applied Research Collaboration in Kent, Surrey, and Sussex (ARC KSS). With the Data Hub team, Elizabeth works to improve capacity and capability in health data science in the KSS region. See our web profile and data resources here.

Background: After studying for an undergraduate degree in Psychology from Oxford University, Elizabeth came to the University of Sussex in 2004 to take up an MRC funded DPhil in clinical health psychology investigating the influences on women’s perceptions of childbirth and their development of post-traumatic stress disorder as a result of traumatic birth. Elizabeth held postdoctoral positions at University of Sussex, Barts and the London Medical School, and at SHORE-C in BSMS. She worked as Research Fellow in Primary Care Epidemiology in the Department of Primary Care and Public Health at BSMS until 2016, as lecturer in 2016 to 2018 and as senior lecturer from 2019-2022.

Research

Elizabeth’s current research is around supporting and optimising the use of routinely collected health and social care data for research into healthcare inequalities and improvements. She works on projects developing risk prediction and early disease detection models from routinely collected health data in mental health and dementia; and assessing the impact of social determinants, multi-morbidity and frailty on health outcomes in (for example) cancer, dementia and cardiovascular disease.

Her methodological work has focussed on using free text for assessing data quality and completeness of recording; trialling machine learning for early detection algorithms; developing Bayesian methods for reducing the impact of missingness in EHRs; and using mixed methods to inform and assess data quality in EHRs. She supports capacity building and skills development in health data science in the KSS region through involvement in DISCUS, the Sussex Integrated Dataset, East Sussex and Kent Public Health Intelligence, and the ARC KSS.

She also leads on studies to understand the public’s opinions on the use of NHS patient data and other “big” health data for research, and align data governance strategies to meet the public’s expectations and needs.

See her previous project on Early Detection of Dementia here >

See her current project on Unlocking Data for Public Health Policy and Practice here >

Elizabeth’s doctoral and post-doctoral research focussed on postnatal mental health and family relationships, and the influence of social relationships (at home and work) on common mental disorders such as anxiety and depression. In addition, she looked at how care received during diseases, such as cancer, can influence mental health.

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Teaching

Elizabeth guest lectures in epidemiology and health data science on Masters courses and runs a student selected course for medical undergraduates on understanding and evaluating artificial intelligence in healthcare. She regularly supervises PhD, Masters and undergraduate research projects and dissertations. Elizabeth is happy to hear from prospective students looking for PhD supervision.

Selected Publications

Ford, E., Pillinger, S., Stewart, R. et al. What is the patient re-identification risk from using de-identified clinical free text data for health research?. AI Ethics (2025). https://doi.org/10.1007/s43681-025-00681-0

Ford, E., Goddard, K., Smith, M., Vera, J. (2025) “I don’t see a reason why we should be hidden from view”: Views of a convenience sample of people living with HIV on sharing HIV status data in routinely collected health and care databases in England. PLoS ONE 2025 Feb 12;20(2):e0316848.https://doi.org/10.1371/journal.pone.0316848

Somathilake, G., Ford, E., Armes, J., Moschoyiannis, S., Collins, M., Francsics, P., Lemanska, A. (2024) Evaluating the quality of prostate cancer diagnosis recording in CPRD GOLD and CPRD Aurum primary care databases for observational research: A study using linked English electronic health records. Cancer Epidemiology 94:102715. https://doi.org/10.1016/j.canep.2024.102715

Reeves, D., Morgan, C., Stamate, D., Ford, E., Ashcroft, D, Kontopantelis, E., van Marwijk, H., McMillan, B. (2024) Identifying individuals at high risk for dementia in primary care: development and validation of the DemRisk risk prediction model using routinely collected patient data. PLoS ONE 19(10):e0310712. https://doi.org/10.1371/journal.pone.0310712

Rogers, I., Cooper, M., Memon, A. Forbes, L., van Marwijk, H., Ford E. (2024) The effect of comorbidities on diagnostic interval for lung cancer in England: a cohort study using electronic health record data. Br J Cancer. 131; 1147–1157 https://doi.org/10.1038/s41416-024-02824-2

Childs, S., Farmer, C., George, A, Ford, E., and Rees-Roberts, M. (2024) Data resource profile: Exploring freely accessible data describing wider determinants of health in England. International Journal of Population Data Science 6:1:2384 https://doi.org/10.23889/ijpds.v8i6.2384

Shah, A. D., Subramanian, A., Lewis, J., Dhalla, S., Ford, E., et al. (2023) Long Covid symptoms and diagnosis in primary care: a cohort study using structured and unstructured data in The Health Improvement Network primary care database PLoS ONE 18(9):e0290583.https://doi.org/10.1371/journal.pone.0290583.

Ford, E., Milne, R., & Curlewis, K. (2023). Ethical issues when using digital biomarkers and artificial intelligence for the early detection of dementia. WIREs Data Mining and Knowledge Discovery, e1492. https://doi.org/10.1002/widm.1492

Ford E, Rees-Roberts M, Stanley K, Goddard K, Giles S, Armes J., Ikhile, D., Madzvamuse, A., Spencer-Hughes, V., George, A., Farmer, C., and Cassell, J. (2023) Understanding how to build a social licence for using novel linked datasets for planning and research in Kent, Surrey and Sussex: results of deliberative focus groups. International Journal of Population Data Science 2023 5:3:13https://doi.org/10.23889/ijpds.v5i3.2114

Ford, E., Edelman, N., Somers, L. et al. (2021) Barriers and facilitators to the adoption of electronic clinical decision support systems: a qualitative interview study with UK general practitioners. BMC Med Inform Decis Mak 21, 193. https://doi.org/10.1186/s12911-021-01557-z

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