Designing and Evaluating the Need for Patient Based Clinical Prediction Rules for Influenza Triage Telemedicine

Annika Cleven, a student at St. Olaf College, worked in the lab of Dr. Andreas Handel

Abstract Using data that was collected from a university health center where patients and clinicians were asked to report the presence of a list of respiratory-related symptoms, we analyzed the need for patient based Clinical Prediction Rules (CPRs).  We investigated inter-rater agreement between clinicians and patients on symptom reporting and found they often disagreed. A Bland-Altman analysis indicated that the disagreement on symptom reporting was enough to invalidate using patient reported symptom data on a CPR that was designed using clinician reported data. Further analysis determined that using patient versus clinician reported symptom data could lead to different risk category designations and advised care. In response, we built risk prediction models that were fit to patient reported data and they provided minimal improvement from the clinician based CPRs.  Our research indicates that clinician based CPRs cannot be effectively used with patient reported symptoms data, like in telemedicine. Our results also show that we can create a patient-based CPR, but because of the minimal improvement, a combination of at-home testing and a patient-based CPR would be the best approach to influenza triage telemedicine.