Accuracy of diagnoses predicted from a simple patient questionnaire stratified by the duration of general ambulatory training: an observational study

Abstract

Purpose

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.

Participants and 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) (χ²=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.

Conclusion

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.

Keywords:

• clinical reasoning
• diagnostic accuracy
• diagnostic reasoning
• general medicine
• Herfindahl index
• predict disease

Supplementary material

Patient questionnaire used for predictive diagnosis

Please answer the following questions to the best of your knowledge, as this information will be used in diagnosis and treatment.

Please answer in regard to the symptoms that brought you here today.

1. Please describe your symptoms (or illness).

2. When did you start to experience these symptoms?

3. Have you received treatment for these symptoms? (Please write down any over-the-counter medications you take for these.)

Hospital/clinic name

Please answer in regard to any previously experienced diseases.

• Are you undergoing treatment at present?    Yes/No

• If yes, please write the name of the disease.

1.———— 2.———— 3.————

Have you ever been seriously ill?   Yes/No

If yes, please write the name of the disease and the time period.

1.———— 2.———— 3.————

Please answer in regard to allergies to medicine.

• Have you ever had an allergic reaction to medicine?    Yes/No

If you answered yes, what was the name of the medication and what were your symptoms?

Please answer in regard to your lifestyle habits.

• Smoking    (not at all/smoke ———— cigarettes a day)

• Drinking alcohol    (not at all/drink alcohol)

Please answer if you are female.

• Is there a chance that you are pregnant, or are you currently breastfeeding?    Yes/No

Disclosure

TU and MO were funded by a grant from the Chiba Prefectural Government (“The course of contribution of rotated collaboration systems for local health care”). The funder had no role in the design or analysis of the original study or in the data analysis. The other authors have no conflicts of interest to declare.