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Abstract Background Falls among older adults are both a common reason for presentation to the emergency department, and a major source of morbidity and mortality. It is critical to identify fall patients quickly and reliably during, and immediately after, emergency department encounters in order to deliver appropriate care and referrals. Unfortunately, falls are difficult to identify without manual chart review, a time intensive process infeasible for many applications including surveillance and quality reporting. Here we describe a pragmatic NLP approach to automating fall identification. Methods In this single center retrospective review, 500 emergency department provider notes from older adult patients (age 65 and older) were randomly selected for analysis. A simple, rules-based NLP algorithm for fall identification was developed and evaluated on a development set of 1084 notes, then compared with identification by consensus of trained abstractors blinded to NLP results. Results The NLP pipeline demonstrated a recall (sensitivity) of 95.8%, specificity of 97.4%, precision of 92.0%, and F1 score of 0.939 for identifying fall events within emergency physician visit notes, as compared to gold standard manual abstraction by human coders. Conclusions Our pragmatic NLP algorithm was able to identify falls in ED notes with excellent precision and recall, comparable to that of more labor-intensive manual abstraction. This finding offers promise not just for improving research methods, but as a potential for identifying patients for targeted interventions, quality measure development and epidemiologic surveillance.

Abstract To improve cancer care in Indiana, a telementoring program using the Extension for Community Healthcare Outcomes (ECHO) model was introduced in September 2019 to promote best-practice cancer prevention, screening, and survivorship care by primary care providers (PCPs). The aim of this study was to evaluate the program’s educational outcomes in its pilot year, using Moore’s Evaluation Framework for Continuing Medical Education and focusing on the program’s impact on participants’ knowledge, confidence, and professional practice. We collected data in 22 semi-structured interviews (13 PCPs and 9 non-PCPs) and 30 anonymous one-time surveys (14 PCPs and 16 non-PCPs) from the program participants (hub and spoke site members), as well as from members of the target audience who did not participate. In the first year, average attendance at each session was 2.5 PCPs and 12 non-PCP professionals. In spite of a relatively low PCP participation, the program received very positive satisfaction scores, and participants reported improvements in knowledge, confidence, and practice. Both program participants and target audience respondents particularly valued three features of the program: its conversational format, the real-life experiences gained, and the support received from a professional interdisciplinary community. PCPs reported preferring case discussions over didactics. Our results suggest that the Cancer ECHO program has benefits over other PCP-targetted cancer control interventions and could be an effective educational means of improving cancer control capacity among PCPs and others. Further study is warranted to explain the discrepancies among study participants’ perceptions of the program’s strengths and the relatively low PCP participation before undertaking a full-scale effectiveness study.