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. 2022 Dec 20;10(12):e41200.
doi: 10.2196/41200.

Identifying Patterns of Clinical Interest in Clinicians' Treatment Preferences: Hypothesis-free Data Science Approach to Prioritizing Prescribing Outliers for Clinical Review

Brian MacKenna #  1 , Helen J Curtis #  1 , Lisa E M Hopcroft  1 , Alex J Walker  1 , Richard Croker  1 , Orla Macdonald  1 , Stephen J W Evans  2 , Peter Inglesby  1 , David Evans  1 , Jessica Morley  1 , Sebastian C J Bacon  1 , Ben Goldacre  1
Affiliations

Identifying Patterns of Clinical Interest in Clinicians' Treatment Preferences: Hypothesis-free Data Science Approach to Prioritizing Prescribing Outliers for Clinical Review

Brian MacKenna et al. JMIR Med Inform. .

Abstract

Background: Data analysis is used to identify signals suggestive of variation in treatment choice or clinical outcome. Analyses to date have generally focused on a hypothesis-driven approach.

Objective: This study aimed to develop a hypothesis-free approach to identify unusual prescribing behavior in primary care data. We aimed to apply this methodology to a national data set in a cross-sectional study to identify chemicals with significant variation in use across Clinical Commissioning Groups (CCGs) for further clinical review, thereby demonstrating proof of concept for prioritization approaches.

Methods: Here we report a new data-driven approach to identify unusual prescribing behaviour in primary care data. This approach first applies a set of filtering steps to identify chemicals with prescribing rate distributions likely to contain outliers, then applies two ranking approaches to identify the most extreme outliers amongst those candidates. This methodology has been applied to three months of national prescribing data (June-August 2017).

Results: Our methodology provides rankings for all chemicals by administrative region. We provide illustrative results for 2 antipsychotic drugs of particular clinical interest: promazine hydrochloride and pericyazine, which rank highly by outlier metrics. Specifically, our method identifies that, while promazine hydrochloride and pericyazine are barely used by most clinicians (with national prescribing rates of 11.1 and 6.2 per 1000 antipsychotic prescriptions, respectively), they make up a substantial proportion of antipsychotic prescribing in 2 small geographic regions in England during the study period (with maximum regional prescribing rates of 298.7 and 241.1 per 1000 antipsychotic prescriptions, respectively).

Conclusions: Our hypothesis-free approach is able to identify candidates for audit and review in clinical practice. To illustrate this, we provide 2 examples of 2 very unusual antipsychotics used disproportionately in 2 small geographic areas of England.

Keywords: NHS England; antipsychotics; clinical audit; data science; pericyazine; prescribing; promazine hydrochloride.

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Conflict of interest statement

Conflicts of Interest: All authors have completed the ICMJE (International Committee of Medical Journal Editors) uniform disclosure form at www.icmje.org/coi_disclosure.pdf and declare the following: BG has received research funding from the Laura and John Arnold Foundation, the NHS National Institute for Health Research (NIHR), the NIHR School of Primary Care Research, the NIHR Oxford Biomedical Research Centre, the Mohn-Westlake Foundation, NIHR Applied Research Collaboration Oxford and Thames Valley, Wellcome Trust, the Good Thinking Foundation, Health Data Research UK, the Health Foundation, the World Health Organization, UKRI, Asthma UK, the British Lung Foundation, and the Longitudinal Health and Wellbeing strand of the National Core Studies program; he also receives personal income from speaking and writing for lay audiences on the misuse of science. BMK and OM work for the NHS and are seconded to the Bennett Institute for Applied Data Science. All other University of Oxford authors are employed on BG’s grants.

Figures

Figure 1
Figure 1
Prioritizing 204 candidate chemicals using ranking by 2 outlier metrics. The top 5 chemicals by either the high:med centile ratio or kurtosis are highlighted in orange; all other chemicals are shown in gray. Each metric is summarized as a histogram of chemical counts along the corresponding axis. Pericyazine and promazine hydrochloride (our chemicals of interest) are highlighted in bold.
Figure 2
Figure 2
Total number of prescriptions for (A) pericyazine (B) promazine hydrochloride per 1000 antipsychotic prescriptions for all CCGs in England in June-August 2017. The colour scale in each plot indicates the number of prescriptions per 1000 antipsychotic prescriptions in the corresponding geographic region.
See this image and copyright information in PMC

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