Evidence map›Paper›PMID 25548316›Full record

Trial reportThe British journal of general practice : the journal of the Royal College of General Practitioners2015

Early diagnostic suggestions improve accuracy of GPs: a randomised controlled trial using computer-simulated patients.

Olga Kostopoulou, Andrea Rosen, Thomas Round, Ellen Wright, Abdel Douiri, Brendan Delaney

Abstract readRandomized Controlled Trial
In one paragraph

Trial report in The British journal of general practice : the journal of the Royal College of General Practitioners, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
50citing papers in PubMed, 4 pooled it
–field-weighted citation impact
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

50 citing papers in PubMed, 4 syntheses or guidelines pooled it.

  1. Pooled it
  2. Decision support tools to improve cancer diagnostic decision making in primary care: a systematic review.The British journal of general practice : the journal of the Royal College of General Practitioners · 2019
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  20. Human-AI collectives most accurately diagnose clinical vignettes.Proceedings of the National Academy of Sciences of the United States of America · 2025
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors.

Olga KostopoulouDepartment of Primary Care and Public Health Sciences, School of Medicine, King's College London, UK.
Andrea RosenDepartment of Primary Care and Public Health Sciences, School of Medicine, King's College London, UK.
Thomas RoundDepartment of Primary Care and Public Health Sciences, School of Medicine, King's College London, UK.
Ellen WrightDepartment of Primary Care and Public Health Sciences, School of Medicine, King's College London, UK.
Abdel DouiriDepartment of Primary Care and Public Health Sciences, School of Medicine, King's College London, UK.
Brendan DelaneyDepartment of Primary Care and Public Health Sciences, School of Medicine, King's College London, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDesigners of computerised diagnostic support systems (CDSSs) expect physicians to notice when they need advice and enter into the CDSS all information that they have gathered about the patient. The poor use of CDSSs and the tendency not to follow advice once a leading diagnosis emerges would question this expectation.

aimTo determine whether providing GPs with diagnoses to consider before they start testing hypotheses improves accuracy. DESIGN AND

settingMixed factorial design, where 297 GPs diagnosed nine patient cases, differing in difficulty, in one of three experimental conditions: control, early support, or late support.

methodData were collected over the internet. After reading some initial information about the patient and the reason for encounter, GPs requested further information for diagnosis and management. Those receiving early support were shown a list of possible diagnoses before gathering further information. In late support, GPs first gave a diagnosis and were then shown which other diagnoses they could still not discount.

resultsEarly support significantly improved diagnostic accuracy over control (odds ratio [OR] 1.31; 95% confidence interval [95%CI] = 1.03 to 1.66, P = 0.027), while late support did not (OR 1.10; 95% CI = 0.88 to 1.37). An absolute improvement of 6% with early support was obtained. There was no significant interaction with case difficulty and no effect of GP experience on accuracy. No differences in information search were detected between experimental conditions.

conclusionReminding GPs of diagnoses to consider before they start testing hypotheses can improve diagnostic accuracy irrespective of case difficulty, without lengthening information search.

Indexed as

Decision Support Systems, ClinicalDiagnosis, Computer-AssistedAbdominal PainAdultChest PainComputer SimulationDiagnosis, DifferentialDiagnostic ErrorsDyspneaEarly DiagnosisFemaleHumansMaleNeeds AssessmentQuality ImprovementUnited Kingdomclinical decision support systemsdecision makingdiagnosisdiagnostic errors

Identifiers

PMID25548316
PMCPMC4276007

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Registered trials

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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.