BSMS > About BSMS > Contact us > Staff > Dr Elizabeth Ford

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 using primary care data and linked routinely collected health data, and carrying out research in mental health, dementia and 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. She is Lead for Data Science in the NIHR Applied Research Collaboration in Kent, Surrey, and Sussex (ARC KSS). She was recognised as a Future Leader in Health Data Science in 2016 by the Farr Institute of Health Informatics Research.

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. 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 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

Ford E, Stanley K, Rees-Roberts M, Giles S, Goddard K, Armes J. (2022) Understanding Public Priorities and Perceptions of the Use of Linked Healthcare Data in South East England. Stud Health Technol Inform. 2022 May 25;294:594-598. doi: 10.3233/SHTI220538. PMID: 35612158.

Porat, T., Burnell, R., Calvo R.A., Ford, E., Paudyal, P., Baxter, WL., Parush, A. (2021) ‘Vaccine Passports’ may backfire: findings from a cross-sectional study in the UK and Israel on willingness to vaccinate against Covid-19. Vaccines. 9(8) 902

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)

Ford, E., Rooney, P., Hurley, P., Oliver, S., Bremner, S., and Cassell, J. (2020) Can the use of Bayesian analysis methods correct for incompleteness in electronic health records diagnosis data? Development of a novel method using simulated and real-life clinical data. Front. Public Health 8:54. doi:10.3389/fpubh.2020.00054

Ford, E., Rooney, P., Oliver, S. et al. (2019) Identifying undetected dementia in UK primary care patients: a retrospective case-control study comparing machine-learning and standard epidemiological approaches. BMC Med Inform Decis Mak 19, 248 doi:10.1186/s12911-019-0991-9

Jones KH, Ford, E., Lea NC, Griffiths LJ, Hassan L, Squires EL, Heys SM and Nenadic G (2020) Towards the development of data governance standards for using clinical free-text data in health research. J Med Internet Res 2020; 22(6):e16760, DOI:10.2196/16760

Ford, E., Oswald, M., Hassan, L., Bozentko, K., Nenadic, G., and Cassell, J. (2020) Should free text data in electronic medical records be shared for research? A citizens’ jury study in the United Kingdom. Journal of Medical Ethics 46(6), pp.367-377. doi:10.1136/medethics-2019-105472

Click here to elements profile >