• David Chartash, Mona Sharifi, Beth Emerson, Robert Frank, Elizabeth M Schoenfeld, Jason Tanner, Cynthia Brandt, and Richard A Taylor.
• Center for Medical Informatics, Yale University School of Medicine, New Haven, CT.
• Ann Emerg Med. 2021 Nov 1; 78 (5): 637-649.

Study ObjectiveWhile patient-centered communication and shared decisionmaking are increasingly recognized as vital aspects of clinical practice, little is known about their characteristics in real-world emergency department (ED) settings. We constructed a natural language processing tool to identify patient-centered communication as documented in ED notes and to describe visit-level, site-level, and temporal patterns within a large health system.MethodsThis was a 2-part study involving (1) the development and validation of an natural language processing tool using regular expressions to identify shared decisionmaking and (2) a retrospective analysis using mixed effects logistic regression and trend analysis of shared decisionmaking and general patient discussion using the natural language processing tool to assess ED physician and advanced practice provider notes from 2013 to 2020.ResultsCompared to chart review of 600 ED notes, the accuracy rates of the natural language processing tool for identification of shared decisionmaking and general patient discussion were 96.7% (95% CI 94.9% to 97.9%) and 88.9% (95% confidence interval [CI] 86.1% to 91.3%), respectively. The natural language processing tool identified shared decisionmaking in 58,246 (2.2%) and general patient discussion in 590,933 (22%) notes. From 2013 to 2020, natural language processing-detected shared decisionmaking increased 300% and general patient discussion increased 50%. We observed higher odds of shared decisionmaking documentation among physicians versus advanced practice providers (odds ratio [OR] 1.14, 95% CI 1.07 to 1.23) and among female versus male patients (OR 1.13, 95% CI 1.11 to 1.15). Black patients had lower odds of shared decisionmaking (OR 0.8, 95% CI 0.84 to 0.88) compared with White patients. Shared decisionmaking and general patient discussion were also associated with higher levels of triage and commercial insurance status.ConclusionIn this study, we developed and validated an natural language processing tool using regular expressions to extract shared decisionmaking from ED notes and found multiple potential factors contributing to variation, including social, demographic, temporal, and presentation characteristics.Copyright © 2021 American College of Emergency Physicians. Published by Elsevier Inc. All rights reserved.

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