Clinical decision making

Female Doctor talking to mother with her child

Clinical decision making requires the assimilation of clinical evidence alongside a range of contextual factors including the preferences of individual patients, to decide on an evidence-based course of action. Our research in this area explores ways in which shared decision making and appropriate prescribing can be supported and uses a range of biostatistical methodologies to explore the performance of diagnostic tests.   

Dr Sam Finnikin

Theme lead
Dr Sam Finnikin

Senior Clinical Tutor

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Theme lead
Dr Brian Willis

MRC Clinical Scientist in Primary Care

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Aims of the research

This aim of this research theme is to explore how clinical decision making can be improved in practice to promote personalised care and optimal health outcomes. We have a particular emphasis on how clinical prediction tools and risk calculators can be incorporated into consultations to support shared decision making.

Current research groups

Shared decision making

Dr Sam Finnikin and Professor Tom Marshall have interests in clinical decision making in primary care. This includes shared decision-making, factors influencing prescribing decisions and the use of risk-scoring to guide clinical decision-making particularly in cardiovascular disease. 

Healthcare decision-making

Dr Brian Willis is a General Practitioner and MRC Clinician Scientist with a BSc in Mathematics and a PhD in Medical Statistics. His research draws on advanced statistical methodology, applied mathematics, optimisation methods and clinical practice to address clinically relevant questions. This includes producing new methodologies in evidence synthesis and individual patient data science in order to enhance healthcare decision-making


Finnikin S, Ryan R, Marshall T. Statin initiations and QRISK2 scoring in UK general practice: a THIN database study. British Journal of General Practice. 2017 Dec 1;67(665):e881-7.

Finnikin S, Hayward G, Wilson F, Lasserson D. Are referrals to hospital from out-of-hours primary care associated with National Early Warning Scores? Emergency Medicine Journal. 2020 May 1;37(5):279-85.

Willis BH, Baragilly M, Coomar D. Maximum likelihood estimation based on Newton-Raphson iteration for the bivariate random effects model in test accuracy meta-analysis. Stat Methods Med Res 2020;29(4): 1197-1211

Willis BH, Coomar D, Baragilly M.Comparison of Centor and McIsaac scores in primary care: a meta-analysis over multiple cut-off points. Br J Gen Pract 2020; 70(693):e245-e254.

Willis BH, Coomar D, Baragilly M. Tailored meta-analysis: investigating the effects of correlation between the test positive rate and prevalence. J Clin Epidemiol 2019;106:1-9

Willis BH,Riley RD.Measuring the statistical validity of summary meta-analysis and meta-regression results for use in clinical practice. Stat Med 2017;36(21): 3283-3301