Use of Patient-Reported Symptom Data in Clinical Decision Rules for Predicting Influenza in a Telemedicine Setting

Abstract

Introduction: Increased use of telemedicine could potentially streamline influenza diagnosis and reduce transmission. However, telemedicine diagnoses are dependent on accurate symptom reporting by patients. If patients disagree with clinicians on symptoms, previously derived diagnostic rules may be inaccurate.

Methods: We performed a secondary data analysis of a prospective, nonrandomized cohort study at a university student health center. Patients who reported an upper respiratory complaint were required to report symptoms, and their clinician was required to report the same list of symptoms. We examined the performance of 5 previously developed clinical decision rules (CDRs) for influenza on both symptom reports. These predictions were compared against PCR diagnoses. We analyzed the agreement between symptom reports, and we built new predictive models using both sets of data.

Results: CDR performance was always lower for the patient-reported symptom data, compared with clinician-reported symptom data. CDRs often resulted in different predictions for the same individual, driven by disagreement in symptom reporting. We were able to fit new models to the patient-reported data, which performed slightly worse than previously derived CDRs. These models and models built on clinician-reported data both suffered from calibration issues.

Discussion: Patients and clinicians frequently disagree about symptom presence, which leads to reduced accuracy when CDRs built with clinician data are applied to patient-reported symptoms. Predictive models using patient-reported symptom data performed worse than models using clinician-reported data and prior results in the literature. However, the differences are minor, and developing new models with more data may be possible.

Keywords: Clinical Decision Rules; Cohort Studies; Infectious Diseases; Influenza; Prospective Studies; Respiratory Tract Diseases; Students; Telemedicine; Triage.

Figure 1:

Cohen’s kappa values for each symptom. Cohen’s kappa was used to measure agreement between clinician diagnoses and the lab test methods. Qualitative agreement categories were assigned based on previously published guidelines for clinical research.

Figure 2.

Cohen’s kappa values (with 95% bootstrap confidence intervals) for each symptom. Cohen’s kappa was used to measure agreement between clinician diagnoses and the lab test methods. Qualitative agreement categories were assigned based on previously published guidelines for clinical research.