Evidence map›Paper›PMID 35084347›Full record

ArticleJMIR medical informatics2022

Incidence of Diagnostic Errors Among Unexpectedly Hospitalized Patients Using an Automated Medical History-Taking System With a Differential Diagnosis Generator: Retrospective Observational Study.

Ren Kawamura, Yukinori Harada, Shu Sugimoto, Yuichiro Nagase, Shinichi Katsukura, Taro Shimizu

Open access · goldAbstract read
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 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
6.3field-weighted citation impact, top 3% 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

11 citing papers in PubMed, 30 citations in OpenAlex.

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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

6 authors at 2 institutions in 1 country.

Ren KawamuraDepartment of Diagnostic and Generalist Medicine, Dokkyo Medical University, Mibu, Japan.ORCID https://orcid.org/0000-0002-5632-3218
Yukinori HaradaDepartment of Diagnostic and Generalist Medicine, Dokkyo Medical University, Mibu, Japan.ORCID https://orcid.org/0000-0001-6042-7397
Shu SugimotoDepartment of Internal Medicine, Nagano Chuo Hospital, Nagano, Japan.ORCID https://orcid.org/0000-0001-9428-7839
Yuichiro NagaseDepartment of Internal Medicine, Nagano Chuo Hospital, Nagano, Japan.ORCID https://orcid.org/0000-0002-1968-8365
Shinichi KatsukuraDepartment of Diagnostic and Generalist Medicine, Dokkyo Medical University, Mibu, Japan.ORCID https://orcid.org/0000-0003-2977-9436
Taro ShimizuDepartment of Diagnostic and Generalist Medicine, Dokkyo Medical University, Mibu, Japan.ORCID https://orcid.org/0000-0002-3788-487X
Dokkyo Medical University · JPNagano Municipal Hospital · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAutomated medical history-taking systems that generate differential diagnosis lists have been suggested to contribute to improved diagnostic accuracy. However, the effect of these systems on diagnostic errors in clinical practice remains unknown.

objectiveThis study aimed to assess the incidence of diagnostic errors in an outpatient department, where an artificial intelligence (AI)-driven automated medical history-taking system that generates differential diagnosis lists was implemented in clinical practice.

methodsWe conducted a retrospective observational study using data from a community hospital in Japan. We included patients aged 20 years and older who used an AI-driven, automated medical history-taking system that generates differential diagnosis lists in the outpatient department of internal medicine for whom the index visit was between July 1, 2019, and June 30, 2020, followed by unplanned hospitalization within 14 days. The primary endpoint was the incidence of diagnostic errors, which were detected using the Revised Safer Dx Instrument by at least two independent reviewers. To evaluate the effect of differential diagnosis lists from the AI system on the incidence of diagnostic errors, we compared the incidence of these errors between a group where the AI system generated the final diagnosis in the differential diagnosis list and a group where the AI system did not generate the final diagnosis in the list; the Fisher exact test was used for comparison between these groups. For cases with confirmed diagnostic errors, further review was conducted to identify the contributing factors of these errors via discussion among three reviewers, using the Safer Dx Process Breakdown Supplement as a reference.

resultsA total of 146 patients were analyzed. A final diagnosis was confirmed for 138 patients and was observed in the differential diagnosis list from the AI system for 69 patients. Diagnostic errors occurred in 16 out of 146 patients (11.0%, 95% CI 6.4%-17.2%). Although statistically insignificant, the incidence of diagnostic errors was lower in cases where the final diagnosis was included in the differential diagnosis list from the AI system than in cases where the final diagnosis was not included in the list (7.2% vs 15.9%, P=.18).

conclusionsThe incidence of diagnostic errors among patients in the outpatient department of internal medicine who used an automated medical history-taking system that generates differential diagnosis lists seemed to be lower than the previously reported incidence of diagnostic errors. This result suggests that the implementation of an automated medical history-taking system that generates differential diagnosis lists could be beneficial for diagnostic safety in the outpatient department of internal medicine.

Indexed as

artificial intelligenceautomated medical history–takingdiagnostic errorsoutpatientSafer Dx

Identifiers

PMID35084347
PMCPMC8832260
OpenAlexW4210524298

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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.