A standard-based taxonomy of features that affect user response to clinical decision support alerts

Abstract

Objective: The objective of this work is to develop a standard-based taxonomy of features that might affect user response to alerts using evidence from literature and public alert logic repositories. Methods: We developed a taxonomy of features using multiple sources: (1) the Agency for Healthcare Research and Quality (AHRQ) CDS Connect Repository, (2) alert logic from commercial electronic health record (EHR) customers, and (3) published literature. Three categories (patient, provider, environment/context) were used a priori to develop the taxonomy. The final taxonomy was mapped to the Fast Healthcare Interoperability Resources (FHIR) standard for development of standardized CDS services. Results: Aggregating potential features extracted from three data sources, we identified 95 unique features, which we then mapped to the FHIR standard, encompassing 24 FHIR resources. The common features differed depending on the knowledge source. In the AHRQ public alert repository, frequently occurring features were observations in flowsheets, procedures, diagnoses, medications, and patient age. On the other hand, the commercial EHR customers primarily presented features such as diagnosis type, patient age, diagnosis grouper, diagnosis, medication value set. Literature-based insights revealed that provider type, medication, patient age, alert severity, and medication dose were the most common features. Conclusion: This study demonstrated a standard-based taxonomy of features that could impact user responses to CDS alerts, bridging insights from academic studies and industry practices. The taxonomy stands as a foundational tool, guiding the CDS development, implementation, and evaluation, with the overarching goal of improving user acceptance and healthcare quality.