Original Contribution Detecting Unplanned Care From Clinician Notes in Electronic Health Records

Purpose: Reduction in unplanned episodes of care, such as emergency department visits and unplanned hospitalizations, are important quality outcomemeasures. However, many events are only documented in free-text clinician notes and are labor intensive to detect by manual medical record review. Methods: We studied 308,096 free-text machine-readable documents linked to individual entries in our electronic health records, representing care for patients with breast, GI, or tho-racic cancer, whose treatment was initiated at one academic medical center, Stanford Health Care (SHC). Using a clinical text-mining tool, we detected unplanned episodes documented in clinician notes (for non-SHC visits) or in coded encounter data for SHC-delivered care and the most frequent symptoms docu-mented in emergency department (ED) notes. Results: Combined reporting increased the identification of pa-tients with one or more unplanned care visits by 32 % (15 % using coded data; 20 % using all the data) among patients with 3 months of follow-up and by 21 % (23 % using coded data; 28% using all the data) among those with 1 year of follow-up. Based on the textual analysis of SHC ED notes, pain (75%), followed by nausea (54%), vomiting (47%), infection (36%), fever (28%), and anemia (27%), were the most frequent symptoms mentioned. Pain, nausea, and vomiting co-occur in 35 % of all ED encounter notes. Conclusion: The text-mining methods we describe can be ap-plied to automatically review free-text clinician notes to detect unplanned episodes of care mentioned in these notes. These methods have broad application for quality improvement efforts in which events of interest occur outside of a network that allows for patient data sharing