title: Accuracy of diagnoses predicted from a simple patient questionnaire stratified by the duration of general ambulatory training: an observational study.
abstract: OBJECTIVE
  To compare the diagnostic accuracy of diseases predicted from patient responses to a simple questionnaire completed prior to examination by doctors with different levels of ambulatory training in general medicine.
  
  
  METHODS
  Before patient examination, five trained physicians, four short-term-trained residents, and four untrained residents examined patient responses to a simple questionnaire and then indicated, in rank order according to their subjective confidence level, the diseases they predicted. Final diagnosis was subsequently determined from hospital records by mentor physicians 3 months after the first patient visit. Predicted diseases and final diagnoses were codified using the International Classification of Diseases version 10. A "correct" diagnosis was one where the predicted disease matched the final diagnosis code.
  
  
  RESULTS
  A total of 148 patient questionnaires were evaluated. The Herfindahl index was 0.024, indicating a high degree of diversity in final diagnoses. The proportion of correct diagnoses was high in the trained group (96 of 148, 65%; residual analysis, 4.4) and low in the untrained group (56 of 148, 38%; residual analysis, -3.6) (χ (2)=22.27, P<0.001). In cases of correct diagnosis, the cumulative number of correct diagnoses showed almost no improvement, even when doctors in the three groups predicted ≥4 diseases.
  
  
  CONCLUSIONS
  Doctors who completed ambulatory training in general medicine while treating a diverse range of diseases accurately predicted diagnosis in 65% of cases from limited written information provided by a simple patient questionnaire, which proved useful for diagnosis. The study also suggests that up to three differential diagnoses are appropriate for diagnostic prediction, while ≥4 differential diagnoses barely improved the diagnostic accuracy, regardless of doctors' competence in general medicine. If doctors can become able to predict the final diagnosis from limited information, the correct diagnostic outcome may improve and save further consultation hours.
authors.affiliation: Department of General Medicine, Chiba University Hospital, Chiba, Japan ; Division of Rotated Collaboration Systems for Local Healthcare, Graduate School of Medicine, Chiba University, Chiba, Japan.
authors.forename: Takanori
authors.initials: T
authors.lastname: Uehara
authors.affiliation: Department of General Medicine, Chiba University Hospital, Chiba, Japan.
authors.forename: Masatomi
authors.initials: M
authors.lastname: Ikusaka
authors.affiliation: Department of General Medicine, Chiba University Hospital, Chiba, Japan.
authors.forename: Yoshiyuki
authors.initials: Y
authors.lastname: Ohira
authors.affiliation: Department of General Medicine, Chiba University Hospital, Chiba, Japan ; Division of Rotated Collaboration Systems for Local Healthcare, Graduate School of Medicine, Chiba University, Chiba, Japan.
authors.forename: Mitsuyasu
authors.initials: M
authors.lastname: Ohta
authors.affiliation: Department of General Medicine, Chiba University Hospital, Chiba, Japan.
authors.forename: Kazutaka
authors.initials: K
authors.lastname: Noda
authors.affiliation: Department of General Medicine, Chiba University Hospital, Chiba, Japan.
authors.forename: Tomoko
authors.initials: T
authors.lastname: Tsukamoto
authors.affiliation: Department of General Medicine, Chiba University Hospital, Chiba, Japan.
authors.forename: Toshihiko
authors.initials: T
authors.lastname: Takada
authors.affiliation: Department of General Medicine, Chiba University Hospital, Chiba, Japan.
authors.forename: Masahito
authors.initials: M
authors.lastname: Miyahara
keywords: Herfindahl index; clinical reasoning; diagnostic accuracy; diagnostic reasoning; general medicine; predict disease