Evidence map›Paper›PMID 41606587›Full record

Observational studyBMC medical education2026

Reducing misdiagnoses and cognitive errors using virtual patients and automated feedback in a clinical reasoning curriculum.

Jason Waechter, Anita Kusnoor, Kara Eickman, David Smith, Matt Wong, Suzanne Rogers, Chel Hee Lee

Abstract readObservational StudyMulticenter Study
In one paragraph

Observational study in BMC medical education, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

7 authors.

Jason WaechterDepartment of Critical Care Medicine, University of Calgary, Calgary, Canada. jason.waechter@ahs.ca.
Anita KusnoorDepartment of Medicine, Baylor College of Medicine, Houston, USA.
Kara EickmanDepartment of Neurology, University of North Dakota School of Medicine and Health Sciences, Grand Forks, USA.
David SmithDepartment of Medicine, Loyola Stritch School of Medicine, Chicago, USA.
Matt WongDepartment of Academic Family Medicine, University of Saskatchewan, Saskatchewan, Canada.
Suzanne RogersDepartment of Primary Care, Idaho School of Osteopathic Medicine, Boise, US.
Chel Hee LeeDepartment of Mathematics and Statistics, Department of Critical Care Medicine, University of Calgary, Calgary, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionDiagnostic errors remain prevalent across all specialties, driven largely by deficits in clinical reasoning (CR). Although CR is a core competency, most medical schools lack structured pre-clerkship CR training. Virtual patients (VPs) with automated feedback offer scalable, simulation-based training to improve diagnostic skills and reduce faculty workload. The aim of this study was to assess whether a CR curriculum using VPs with automated scoring and deliberate practice improves diagnostic accuracy and CR.

methodsWe conducted a multi-site observational study across five North American medical schools. First- and second-year students completed up to 20 diagnostic VP cases on TeachingMedicine.com, each with automated scoring to inform individualized feedback. We analyzed 1.55 million datapoints from 12,400 cases completed by 1,066 students to assess differences in CR performance between correctly and incorrectly diagnosed cases, associations between CR components and diagnostic accuracy, and learning gains over time.

resultsMisdiagnoses occurred in 20.1% of cases. Correct diagnoses were associated with higher diagnostic justification (DxJ) scores (+ 50%), better test ordering (+ 51%), and fewer cognitive errors (–89%). Multivariate analysis identified DxJ and cognitive errors as the strongest predictors of diagnostic accuracy. With repeated practice, students improved DxJ by 72%, test ordering by 40%, and reduced misdiagnoses threefold and cognitive errors by half, with no plateau observed after 20 cases. By end of pre-clerkship, first-year students who completed 20 cases outperformed second-year students who completed 10 in all CR metrics. All results were statistically significant with p < 0.0001.

conclusionsThis curriculum shows that CR skills are highly trainable through deliberate practice. Improved DxJ and reduced cognitive errors are strongly associated with lower misdiagnosis rates. In contrast to a common misperception, training CR diagnostic skills is successful when started in the beginning of 1st year medical school prior to students’ acquisition of significant medical knowledge.

Indexed as

Clinical CompetenceClinical ReasoningDiagnostic ErrorsEducation, Medical, UndergraduatePatient SimulationSimulation TrainingCurriculumFeedbackHumansStudents, MedicalClinical reasoningCognitive errorsCurriculumDeliberate practiceMisdiagnosisPre-clerkship

Identifiers

PMID41606587
PMCPMC12983517

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.