Toward alert triage: Scalable qualitative coding framework for analyzing alert notes from the Telehealth Intervention Program for Seniors (TIPS)

Abstract

Objective: Combined with mobile monitoring devices, telehealth generates overwhelming data, which could cause clinician burnout and overlooking critical patient status. Developing novel and efficient ways to correctly triage such data will be critical to a successful telehealth adoption. We aim to develop an automated classification framework of existing nurses' notes for each alert that will serve as a training dataset for a future alert triage system for telehealth programs. Materials and Methods: We analyzed and developed a coding framework and a regular expression-based keyword match approach based on the information of 24931 alert notes from a community-based telehealth program. We evaluated our automated alert triaging model for its scalability on a stratified sampling of 800 alert notes for precision and recall analysis. Results: We found 22717 out of 24579 alert notes (92%) belonging to at least one of the 17 codes. The evaluation of the automated alert note analysis using the regular expression-based information extraction approach resulted in an average precision of 0.86 (SD = 0.13) and recall 0.90 (SD = 0.13). Discussion: The high-performance results show the feasibility and the scalability potential of this approach in community-based, low-income older adult telehealth settings. The resulting coded alert notes can be combined with participants' health monitoring results to generate predictive models and to triage false alerts. The findings build steps toward developing an automated alert triaging model to improve the identification of alert types in remote health monitoring and telehealth systems.