Comparison of Diagnostic and Triage Accuracy of Ada Health and WebMD Symptom Checkers, ChatGPT, and Physicians for Patients in an Emergency Department: Clinical Data Analysis Study.

BACKGROUND: Diagnosis is a core component of effective health care, but misdiagnosis is common and can put patients at risk. Diagnostic decision support systems can play a role in improving diagnosis by physicians and other health care workers. Symptom checkers (SCs) have been designed to improve diagnosis and triage (ie, which level of care to seek) by patients. OBJECTIVE: The aim of this study was to evaluate the performance of the new large language model ChatGPT (versions 3.5 and 4.0), the widely used WebMD SC, and an SC developed by Ada Health in the diagnosis and triage of patients with urgent or emergent clinical problems compared with the final emergency department (ED) diagnoses and physician reviews. METHODS: We used previously collected, deidentified, self-report data from 40 patients presenting to an ED for care who used the Ada SC to record their symptoms prior to seeing the ED physician. Deidentified data were entered into ChatGPT versions 3.5 and 4.0 and WebMD by a research assistant blinded to diagnoses and triage. Diagnoses from all 4 systems were compared with the previously abstracted final diagnoses in the ED as well as with diagnoses and triage recommendations from three independent board-certified ED physicians who had blindly reviewed the self-report clinical data from Ada. Diagnostic accuracy was calculated as the proportion of the diagnoses from ChatGPT, Ada SC, WebMD SC, and the independent physicians that matched at least one ED diagnosis (stratified as top 1 or top 3). Triage accuracy was calculated as the number of recommendations from ChatGPT, WebMD, or Ada that agreed with at least 2 of the independent physicians or were rated "unsafe" or "too cautious." RESULTS: Overall, 30 and 37 cases had sufficient data for diagnostic and triage analysis, respectively. The rate of top-1 diagnosis matches for Ada, ChatGPT 3.5, ChatGPT 4.0, and WebMD was 9 (30%), 12 (40%), 10 (33%), and 12 (40%), respectively, with a mean rate of 47% for the physicians. The rate of top-3 diagnostic matches for Ada, ChatGPT 3.5, ChatGPT 4.0, and WebMD was 19 (63%), 19 (63%), 15 (50%), and 17 (57%), respectively, with a mean rate of 69% for physicians. The distribution of triage results for Ada was 62% (n=23) agree, 14% unsafe (n=5), and 24% (n=9) too cautious; that for ChatGPT 3.5 was 59% (n=22) agree, 41% (n=15) unsafe, and 0% (n=0) too cautious; that for ChatGPT 4.0 was 76% (n=28) agree, 22% (n=8) unsafe, and 3% (n=1) too cautious; and that for WebMD was 70% (n=26) agree, 19% (n=7) unsafe, and 11% (n=4) too cautious. The unsafe triage rate for ChatGPT 3.5 (41%) was significantly higher (P=.009) than that of Ada (14%). CONCLUSIONS: ChatGPT 3.5 had high diagnostic accuracy but a high unsafe triage rate. ChatGPT 4.0 had the poorest diagnostic accuracy, but a lower unsafe triage rate and the highest triage agreement with the physicians. The Ada and WebMD SCs performed better overall than ChatGPT. Unsupervised patient use of ChatGPT for diagnosis and triage is not recommended without improvements to triage accuracy and extensive clinical evaluation.

Evaluation of Diagnostic and Triage Accuracy and Usability of a Symptom Checker in an Emergency Department: Observational Study.

BACKGROUND: Symptom checkers are clinical decision support apps for patients, used by tens of millions of people annually. They are designed to provide diagnostic and triage advice and assist users in seeking the appropriate level of care. Little evidence is available regarding their diagnostic and triage accuracy with direct use by patients for urgent conditions. OBJECTIVE: The aim of this study is to determine the diagnostic and triage accuracy and usability of a symptom checker in use by patients presenting to an emergency department (ED). METHODS: We recruited a convenience sample of English-speaking patients presenting for care in an urban ED. Each consenting patient used a leading symptom checker from Ada Health before the ED evaluation. Diagnostic accuracy was evaluated by comparing the symptom checker's diagnoses and those of 3 independent emergency physicians viewing the patient-entered symptom data, with the final diagnoses from the ED evaluation. The Ada diagnoses and triage were also critiqued by the independent physicians. The patients completed a usability survey based on the Technology Acceptance Model. RESULTS: A total of 40 (80%) of the 50 participants approached completed the symptom checker assessment and usability survey. Their mean age was 39.3 (SD 15.9; range 18-76) years, and they were 65% (26/40) female, 68% (27/40) White, 48% (19/40) Hispanic or Latino, and 13% (5/40) Black or African American. Some cases had missing data or a lack of a clear ED diagnosis; 75% (30/40) were included in the analysis of diagnosis, and 93% (37/40) for triage. The sensitivity for at least one of the final ED diagnoses by Ada (based on its top 5 diagnoses) was 70% (95% CI 54%-86%), close to the mean sensitivity for the 3 physicians (on their top 3 diagnoses) of 68.9%. The physicians rated the Ada triage decisions as 62% (23/37) fully agree and 24% (9/37) safe but too cautious. It was rated as unsafe and too risky in 22% (8/37) of cases by at least one physician, in 14% (5/37) of cases by at least two physicians, and in 5% (2/37) of cases by all 3 physicians. Usability was rated highly; participants agreed or strongly agreed with the 7 Technology Acceptance Model usability questions with a mean score of 84.6%, although "satisfaction" and "enjoyment" were rated low. CONCLUSIONS: This study provides preliminary evidence that a symptom checker can provide acceptable usability and diagnostic accuracy for patients with various urgent conditions. A total of 14% (5/37) of symptom checker triage recommendations were deemed unsafe and too risky by at least two physicians based on the symptoms recorded, similar to the results of studies on telephone and nurse triage. Larger studies are needed of diagnosis and triage performance with direct patient use in different clinical environments.