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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)

Reader in Health Data Science
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

BACKGROUND IMAGE FOR PANEL

Biography

Elizabeth Ford is Reader 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.

Elizabeth has been on the on the editorial board for the International Journal of Population Data Science since its inception. She has served as a funding panel member for the MRC, ESRC and NIHR. She was a “Farr Future Leader” in health data science in the Farr Institute of Health Informatics Research (2016-2018) and was public engagement and governance lead for the EPSRC-funded Healthcare Text Analystics Network (2016-2020).

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.

BACKGROUND IMAGE FOR PANEL

Teaching

Elizabeth has overview of the undergraduate curriculum in research methods, epidemiology and healthcare evaluation and improvement, and guest lectures on epidemiology and health data science on Masters courses.

She regularly supervises individual research projects for fourth year medical students as well as masters and PhD students.

Selected Publications

Webb, R, Ford, E., Easter, A., Shakespeare, J, Holly, J., Ayers, S. (2023) The MATRIx Models – Conceptual frameworks of barriers and facilitators to perinatal mental health care. BJPsych Open (in press)

Fitzpatrick NK, Dobson R, Roberts A, Jones K, Shah A, Nenadic G, Ford E., (2023) Understanding stakeholder views around the creation of a consented donated databank of clinical free text to develop and train natural language processing models for research. JMIR Medical Informatics. (in press)

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, Tyler, R., Johnston, N., Spencer-Hughes, V., Evans, G., Elsom, J., Madzvamuse, A., Clay, J., Gilchrist, K., Rees-Roberts M (2023) Challenges Encountered and Lessons Learned when Using a Novel Anonymised Linked Dataset of Health and Social Care Records for Public Health Intelligence: The Sussex Integrated Dataset. Information 14, 106. https://doi.org/10.3390/info14020106

Shah, A. D., Subramanian, A., Lewis, J., Dhalla, S., Ford,E., Haroon, S. 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 databasemedRxiv 2023 2023.01.06.23284202

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 5:3:13

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

Webb R., Uddin, N., Ford, E., et al (2021). Barriers and Facilitators to Implementing Perinatal Mental Health Care in Health and Social Care Settings: A Systematic Review. The Lancet Psychiatry https://doi.org/10.1016/S2215-0366(20)30467-3.

Ford E., Sheppard, J., Oliver, S., Rooney, P., Banerjee, S., Cassell. J. (2021) Automated detection of patients with dementia whose symptoms have been identified in primary care but have no formal diagnosis: a retrospective case–control study using electronic primary care records. BMJ Open 2021;11:e039248. doi:10.1136/bmjopen-2020-039248

Ford E, Starlinger J, Rooney P et al. (2020) Could dementia be detected from UK primary care patients’ records by simple automated methods earlier than by the treating physician? A retrospective case-control study. Wellcome Open Res 2020, 5:120 (https://doi.org/10.12688/wellcomeopenres.15903.1)

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