Development and validation of a pragmatic natural language processing approach to identifying falls in older adults in the emergency department.

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.

Zwitterionically modified alginates mitigate cellular overgrowth for cell encapsulation.

Foreign body reaction (FBR) to implanted biomaterials and medical devices is common and can compromise the function of implants or cause complications. For example, in cell encapsulation, cellular overgrowth (CO) and fibrosis around the cellular constructs can reduce the mass transfer of oxygen, nutrients and metabolic wastes, undermining cell function and leading to transplant failure. Therefore, materials that mitigate FBR or CO will have broad applications in biomedicine. Here we report a group of zwitterionic, sulfobetaine (SB) and carboxybetaine (CB) modifications of alginates that reproducibly mitigate the CO of implanted alginate microcapsules in mice, dogs and pigs. Using the modified alginates (SB-alginates), we also demonstrate improved outcome of islet encapsulation in a chemically-induced diabetic mouse model. These zwitterion-modified alginates may contribute to the development of cell encapsulation therapies for type 1 diabetes and other hormone-deficient diseases.