Understanding Primary Care Physicians' Work via Text Analytics on EHR Inbox Messages

OBJECTIVES: To develop a text analytics methodology to analyze in a refined manner the drivers of primary care physicians' (PCPs') electronic health record (EHR) inbox work. STUDY DESIGN: This study used 1 year (2018) of EHR inbox messages obtained from the Epic system for 184 PCPs from 18 practices. METHODS: An advanced text analytics latent Dirichlet allocation model was trained on physicians' inbox message texts to identify the different work themes managed by physicians and their relative share of workload across physicians and clinics. RESULTS: The text analytics model identified 30 different work themes rolled up into 2 categories of medical and administrative tasks. We found that 50.8% (range across physicians, 34.5%-61.9%) of the messages were concerned with medical issues and 34.1% (range, 23.0%-48.9%) focused on administrative matters. More specifically, 13.6% (range, 7.1%-22.6%) of the messages involved ambiguous diagnosis issues, 13.2% (range, 6.9%-18.8%) involved condition management issues, 6.7% (range, 1.9%-13.4%) involved identified symptoms issues, 9.5% (range, 5.2%-28.9%) involved paperwork issues, and 17.6% (range, 9.3%-27.1%) involved scheduling issues. Additionally, there was significant variability among physicians and practices. CONCLUSIONS: This study demonstrated that advanced text analytics provide a reliable data-driven methodology to understand the individual physician's EHR inbox management work with a significantly greater level of detail than previous approaches. This methodology can inform decision makers on appropriate workflow redesign to eliminate unnecessary workload on PCPs and to improve cost and quality of care, as well as staff work satisfaction.