Analysis of diagnostic error cases among Japanese residents using diagnosis error evaluation and research taxonomy

Harada, Taku; Miyagami, Taiju; Watari, Takashi; Hiyoshi, Tetsuya; Kunitomo, Kotaro; Tsuji, Takeshi; Shimizu, Taro

doi:10.1002/jgf2.388

Cited by 12 publications

(4 citation statements)

References 8 publications

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“…The disease recalled from the medical history information will affect the subsequent physical examination and laboratory tests to be undertaken. Previous reports have acknowledged that diagnostic errors are likely to occur at this point [6].…”

Section: Discussionmentioning

confidence: 94%

“…Most diagnostic errors likely occur during information collection and integration [5]. Japanese residents are prone to making diagnostic errors during history-taking, physical examination, and assessment [6]. Furthermore, 32% of diagnostic errors occur when integrating information from medical history, physical examination, and assessment [7].…”

Section: Introductionmentioning

confidence: 99%

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Improving diagnostic accuracy using a clinical diagnostic support system for medical students during history-taking: a randomized clinical trial

Yanagita,

Shikino,

Ishizuka

et al. 2023

BMC Med Educ

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Background A clinical diagnostic support system (CDSS) can support medical students and physicians in providing evidence-based care. In this study, we investigate diagnostic accuracy based on the history of present illness between groups of medical students using a CDSS, Google, and neither (control). Further, the degree of diagnostic accuracy of medical students using a CDSS is compared with that of residents using neither a CDSS nor Google. Methods This study is a randomized educational trial. The participants comprised 64 medical students and 13 residents who rotated in the Department of General Medicine at Chiba University Hospital from May to December 2020. The medical students were randomly divided into the CDSS group (n = 22), Google group (n = 22), and control group (n = 20). Participants were asked to provide the three most likely diagnoses for 20 cases, mainly a history of a present illness (10 common and 10 emergent diseases). Each correct diagnosis was awarded 1 point (maximum 20 points). The mean scores of the three medical student groups were compared using a one-way analysis of variance. Furthermore, the mean scores of the CDSS, Google, and residents’ (without CDSS or Google) groups were compared. Results The mean scores of the CDSS (12.0 ± 1.3) and Google (11.9 ± 1.1) groups were significantly higher than those of the control group (9.5 ± 1.7; p = 0.02 and p = 0.03, respectively). The residents’ group’s mean score (14.7 ± 1.4) was higher than the mean scores of the CDSS and Google groups (p = 0.01). Regarding common disease cases, the mean scores were 7.4 ± 0.7, 7.1 ± 0.7, and 8.2 ± 0.7 for the CDSS, Google, and residents’ groups, respectively. There were no significant differences in mean scores (p = 0.1). Conclusions Medical students who used the CDSS and Google were able to list differential diagnoses more accurately than those using neither. Furthermore, they could make the same level of differential diagnoses as residents in the context of common diseases. Trial registration This study was retrospectively registered with the University Hospital Medical Information Network Clinical Trials Registry on 24/12/2020 (unique trial number: UMIN000042831).

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Section: Discussionmentioning

confidence: 94%

Section: Introductionmentioning

confidence: 99%

Improving diagnostic accuracy using a clinical diagnostic support system for medical students during history-taking: a randomized clinical trial

Yanagita,

Shikino,

Ishizuka

et al. 2023

BMC Med Educ

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“…Diagnostic errors, defined as "the failure to (a) establish an accurate and timely explanation of the patient's health problem(s) or (b) communicate that explanation to the patient," are common worldwide patient safety issues in outpatients [1][2][3][4][5][6][7][8]. Since history-taking failure and hypothesis generation failure or failure to consider the correct diagnosis are the most important contributing factors to diagnostic errors [9][10][11][12][13], they can be major targets of intervention to reduce diagnostic errors. Indeed, previous studies reported that reminding physicians of the considerable diagnoses before they started testing hypotheses increased the number of differential diagnoses and improved diagnostic accuracy irrespective of case difficulty [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Effects of Combinational Use of Additional Differential Diagnostic Generators on the Diagnostic Accuracy of the Differential Diagnosis List Developed by an Artificial Intelligence–Driven Automated History–Taking System: Pilot Cross-Sectional Study

Harada¹,

Tomiyama²,

Sakamoto³

et al. 2023

JMIR Form Res

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Background Low diagnostic accuracy is a major concern in automated medical history–taking systems with differential diagnosis (DDx) generators. Extending the concept of collective intelligence to the field of DDx generators such that the accuracy of judgment becomes higher when accepting an integrated diagnosis list from multiple people than when accepting a diagnosis list from a single person may be a possible solution. Objective The purpose of this study is to assess whether the combined use of several DDx generators improves the diagnostic accuracy of DDx lists. Methods We used medical history data and the top 10 DDx lists (index DDx lists) generated by an artificial intelligence (AI)–driven automated medical history–taking system from 103 patients with confirmed diagnoses. Two research physicians independently created the other top 10 DDx lists (second and third DDx lists) per case by imputing key information into the other 2 DDx generators based on the medical history generated by the automated medical history–taking system without reading the index lists generated by the automated medical history–taking system. We used the McNemar test to assess the improvement in diagnostic accuracy from the index DDx lists to the three types of combined DDx lists: (1) simply combining DDx lists from the index, second, and third lists; (2) creating a new top 10 DDx list using a 1/n weighting rule; and (3) creating new lists with only shared diagnoses among DDx lists from the index, second, and third lists. We treated the data generated by 2 research physicians from the same patient as independent cases. Therefore, the number of cases included in analyses in the case using 2 additional lists was 206 (103 cases × 2 physicians’ input). Results The diagnostic accuracy of the index lists was 46% (47/103). Diagnostic accuracy was improved by simply combining the other 2 DDx lists (133/206, 65%, P<.001), whereas the other 2 combined DDx lists did not improve the diagnostic accuracy of the DDx lists (106/206, 52%, P=.05 in the collective list with the 1/n weighting rule and 29/206, 14%, P<.001 in the only shared diagnoses among the 3 DDx lists). Conclusions Simply adding each of the top 10 DDx lists from additional DDx generators increased the diagnostic accuracy of the DDx list by approximately 20%, suggesting that the combinational use of DDx generators early in the diagnostic process is beneficial.

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“…In Japan, it has been noted that efforts to improve diagnostic errors have been slow due to the influence of multiple factors, including medical education, the healthcare system, and a culture of shame [14]. In addition, although there is a recent report from Japan on the analysis of diagnostic errors by residents [15], few studies have addressed diagnostic errors in Japan. In addition, regarding emergency rooms, single-center studies have quantified the cognitive biases that are likely to occur [16], but there are no quantified studies set in Japan.…”

Section: Introductionmentioning

confidence: 99%

Cognitive biases encountered by physicians in the emergency room

2022

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Background Diagnostic errors constitute an important medical safety problem that needs improvement, and their frequency and severity are high in emergency room settings. Previous studies have suggested that diagnostic errors occur in 0.6-12% of first-time patients in the emergency room and that one or more cognitive factors are involved in 96% of these cases. This study aimed to identify the types of cognitive biases experienced by physicians in emergency rooms in Japan. Methods We conducted a questionnaire survey using Nikkei Medical Online (Internet) from January 21 to January 31, 2019. Of the 159,519 physicians registered with Nikkei Medical Online when the survey was administered, those who volunteered their most memorable diagnostic error cases in the emergency room participated in the study. EZR was used for the statistical analyses. Results A total of 387 physicians were included. The most common cognitive biases were overconfidence (22.5%), confirmation (21.2%), availability (12.4%), and anchoring (11.4%). Of the error cases, the top five most common initial diagnoses were upper gastrointestinal disease (22.7%), trauma (14.7%), cardiovascular disease (10.9%), respiratory disease (7.5%), and primary headache (6.5%). The corresponding final diagnoses for these errors were intestinal obstruction or peritonitis (27.3%), overlooked traumas (47.4%), other cardiovascular diseases (66.7%), cardiovascular disease (41.4%), and stroke (80%), respectively. Conclusions A comparison of the initial and final diagnoses of cases with diagnostic errors shows that there were more cases with diagnostic errors caused by overlooking another disease in the same organ or a disease in a closely related organ.

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Analysis of diagnostic error cases among Japanese residents using diagnosis error evaluation and research taxonomy

Cited by 12 publications

References 8 publications

Improving diagnostic accuracy using a clinical diagnostic support system for medical students during history-taking: a randomized clinical trial

Improving diagnostic accuracy using a clinical diagnostic support system for medical students during history-taking: a randomized clinical trial

Effects of Combinational Use of Additional Differential Diagnostic Generators on the Diagnostic Accuracy of the Differential Diagnosis List Developed by an Artificial Intelligence–Driven Automated History–Taking System: Pilot Cross-Sectional Study

Cognitive biases encountered by physicians in the emergency room

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