Studying Provider and Team Behaviors via EHR Audit Logs 

The digitization of health records and the metadata captured by these systems create a novel and exciting opportunity to rapidly advance our understanding of the diagnostic process and diagnostic excellence. Specifically, the ability to truly understand how frontline clinicians interact with clinical information when making diagnostic decisions, and how these interactions impact diagnostic quality, may be greatly improved by analysis of a largely-unused, highly-granular level of electronic health record (EHR) data (referred to as “audit log” data) that are captured primarily for administrative compliance purposes today. This grant to the University of California, San Francisco Office of Sponsored Research (UCSF) will leverage the use of audit log data to capture contextual dimensions of diagnostic assessment (including clinical decision-making, clinical team structure and clinical processes) from three health systems (UCSF, Stanford and Kaiser Permanente Northern California). By identifying key contextual factors that contribute to diagnostic performance, this work may ultimately improve the efficiency of diagnostic processes and reduce errors.

The University of California Behavioral Economics and Access Log Research Collaborative (UCBEAR) is a joint venture between CLIIR and faculty in the Haas School of Business and the Department of Economics at UC Berkeley.  The goal of this collaborative is to develop a rich dataset describing EHR users at UCSF, their day to day actions and the outcomes of their actions.  These data will help researchers create a body of work that addresses questions about the impact of clinical training on note writing efficiency, the extent of cognitive biases in diagnostic processes and other explorations at the intersection of behavioral economics, medicine and clinical informatics.