Evidence map›Paper›PMID 36538350›Full record

ArticleJMIR medical informatics2022

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

Brian MacKenna, Helen J Curtis, Lisa E M Hopcroft, Alex J Walker, Richard Croker, Orla Macdonald, Stephen J W Evans, Peter Inglesby, David Evans, Jessica Morley and 2 more

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in JMIR medical informatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.3field-weighted citation impact, top 24% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 1 citations in OpenAlex.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors at 2 institutions in 1 country.

Brian MacKenna *Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom.ORCID https://orcid.org/0000-0002-3786-9063
Helen J Curtis *Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom.ORCID https://orcid.org/0000-0003-3429-9576
Lisa E M HopcroftBennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom.ORCID https://orcid.org/0000-0002-7022-1322
Alex J WalkerBennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom.ORCID https://orcid.org/0000-0003-4932-6135
Richard CrokerBennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom.ORCID https://orcid.org/0000-0002-8114-9186
Orla MacdonaldBennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom.ORCID https://orcid.org/0000-0003-2774-0001
Stephen J W EvansDepartment of Medical Statistics, London School of Hygiene and Tropical Medicine, London, United Kingdom.ORCID https://orcid.org/0000-0002-1474-2596
Peter InglesbyBennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom.ORCID https://orcid.org/0000-0002-7784-1719
David EvansBennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom.ORCID https://orcid.org/0000-0002-1100-079X
Jessica MorleyBennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom.ORCID https://orcid.org/0000-0001-5221-4770
Sebastian C J BaconBennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom.ORCID https://orcid.org/0000-0002-6354-3454
Ben GoldacreBennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom.ORCID https://orcid.org/0000-0002-5127-4728
University of Oxford · GBLondon School of Hygiene & Tropical Medicine · GB

Funding

Wellcome Trust
6 · The paper itself

Abstract

backgroundData 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.

objectiveThis 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.

methodsHere 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).

resultsOur 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).

conclusionsOur 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.

Indexed as

antipsychoticsclinical auditdata scienceNHS Englandpericyazineprescribingpromazine hydrochloride

Identifiers

PMID36538350
PMCPMC9812268
OpenAlexW4312065018

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.