Purpose The data on the diagnostic contribution of general internal medicine (GIM) consultations for undiagnosed health problems from specialists are scarce. This study aims to explore the role of generalists as diagnostic medicine consultants in tertiary care settings. Patients and Methods We conducted a retrospective observational study at a Japanese university hospital. GIM consultations for diagnosis from other departments on outpatients aged ≧ 20 years from January 2018 to December 2020 were included. Data were extracted from electronic medical records. The primary outcome was new diagnosis rates. The secondary outcomes were new diagnosis rates with clinical significance and clinical outcomes at 90 days from the index visit. Results A total of 328 patients were included. The top five consulting departments were orthopedics (17.0%), cardiovascular (10.3%), otorhinolaryngology (8.8%), neurology (8.8%), and gastroenterology (7.9%). GIM identified 456 chief complaints (CCs), and the top five were fever (10.9%), abnormal laboratory results (8.3%), fatigue (5.9%), and pain (7.4%) or numbness (4.6%) in the extremities. There were 139 (104/328 patients: 31.8%) specialty consultations from GIM, and the top five departments were rheumatology (21.1%), gastroenterology (19.2%), orthopedics (9.6%), psychiatry (9.6%), and neurology (9.6%). In total, 277 new diagnoses were established in 232 patients (70.7%), and 203 patients had new diagnoses with clinical significance (61.8%). Clinical outcomes at 90 days from the time of the index visit were resolution/improvement (60.7%), unchanged/worsened (22.3%), and unknown (17.0%). Conclusion Over 70% of GIM consultations from other departments established new diagnoses with favorable outcomes in >60% of the patients.

BACKGROUND Low diagnostic accuracy is a major concern in automated medical history-taking systems with differential diagnosis generators. Extending the concept of collective intelligence to the field of differential diagnosis generators such that the accuracy of judgment becomes higher when accepting an integrated diagnosis list from multiple persons than when accepting a diagnosis list from a single person may be a possible solution. OBJECTIVE To assess whether the combined use of several differential diagnosis (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 other top 10 DDx lists (second and third DDx lists) per case by imputing key information into the other two 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: (a) simply combining DDx from the index, second, and third lists; (b) creating a new top 10 DDx list using a 1/n weighting rule; and (c) creating new lists with only shared diagnoses among DDx lists from the index, second, and third lists. We treated the data generated by two research physicians from the same patient as independent cases. Therefore, the number of cases included in analyses in the case using two additional lists was 206 (103 cases × two physicians input). RESULTS The diagnostic accuracy of the index lists was 47/103 (45.6%). Diagnostic accuracy was improved by simply combining the other two DDx lists (133/206, 64.6%, P<.001), whereas the other two combined DDx lists did not improve the diagnostic accuracy of the DDx lists (106/206, 51.5%, P=.052 in the collective list with 1/n weighting rule; and 29/206, 14.1%, P<.001 in the only shared diagnoses among the three DDx lists). CONCLUSIONS Simply adding each of the top 10 DDx 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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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.