Jacqueline Dworaczyk, a student at Arizona State University, worked in the lab of Dr. Andreas Handel.

Telemedicine has become increasingly popular during the age of Covid-19. During a public health crisis, telemedicine could be used as a tool to triage patients and prevent burden on the health care system. In an exploratory data analysis, we investigated whether a symptom questionnaire could be used to predict influenza diagnosis. A symptom questionnaire containing 19 upper respiratory symptoms was administered to patients and clinicians (n = 2475 patients) at University of Georgia’s Health Center during the 2016-2017 flu season. Five clinical decision rules were applied to the symptoms reported by the patients and clinicians. The clinical decision rules’ performance when predicting influenza diagnosis was assessed using AUC, F1, MCC, sensitivity and specificity. A 7-11% drop in AUC was observed across all clinical decision rules when using the patient-reported symptoms as opposed to the clinician-reported symptoms.

In a sensitivity analysis, we evaluated the clinical decision rules’ ability to predict true, lab-confirmed influenza in a subset of our population who received PCR testing. While clinician-reported symptoms still performed better than patient-reported symptoms, the difference in AUCs when predicting PCR was significantly smaller. These differences in performance may be partially explained by a lack of agreement between patients and clinicians on the presence of signs and symptoms. Agreement between the patients and clinician’s questionnaire responses (n = 2475) was quantified using Cohen’s Kappa statistic and found that at best, patient’s and clinician’s had moderate agreement on three of the nineteen symptoms assessed.  Overall, it was found that using a patient symptom questionnaire to predict physician diagnosis led to a reduction in accuracy. Further studies need to be done to assess the clinical relevance of this reduction.