Artificial neural networks can predict trauma volume and acuity regardless of center size and geography: A multicenter study

Abstract

Background-Trauma has long been considered unpredictable. Artificial neural networks (ANN) have recently shown the ability to predict admission volume, acuity and operative needs at a single trauma center with very high reliability. This model has not been tested in a multicenter model with differing climate and geography. We hypothesize that an artificial neural network can accurately predict trauma admission volume, penetrating trauma admissions, and mean ISS with a high degree of reliability across multiple trauma centers.

The coronavirus disease 2019 (COVID-19) stay-at-home order’s unequal effects on trauma volume by insurance status in Southern California

“…Predicting trauma volume accurately is important for hospital staffing and resource allocation, especially during a pandemic that has considerably stressed hospital systems. 19 , 33 At the lowest point, we identified a 40% decrease in total trauma volume, which is similar to the 43% decrease described by Leichtle et al in a single center study in Virginia. 17 Interestingly, the lowest trauma volume our region experienced was 20 days after SAH orders, perhaps indicating that large-scale behavioral changes take a considerable amount of time to take effect.…”

At Ground Zero—Emergency Units in Low‐ and Middle‐Income Countries Building Resilience for Climate Change and Human Health

“…The list of consequential environmental impacts includes rising temperatures, wildfires, floods, tropical cyclones, dust storms, and droughts (IPCC, 2014;Watts et al, 2019). Infectious disease outbreaks (Sukhralia et al, 2019;Waits, Emelyanova, Oksanen, Abass, & Rautio, 2018;Wu, Lu, Zhou, Chen, & Xu, 2016), acute exacerbations of chronic diseases (Friel et al, 2011;Pruss-Ustiun et al, 2019), mental health affects (Obradovich, Migliorini, Paulus, & Rahwan, 2018;Padhy, Sarkar, Panigrahi, & Paul, 2015) and trauma caused by violence (Dennis et al, 2019;Hsiang, Burke, & Miguel, 2013;Levy, Sidel, & Patz, 2017) have all been associated with a rapidly warming planet. These impacts disproportionately affect the most socially and medically vulnerable (IPCC, 2014) and present a global challenge in the delivery of time-sensitive acute healthcare services.…”

Artificial intelligence in emergency medicine: A scoping review

“…within the ED, including daily trauma volume. [48][49][50][51][52][53] These interventions have the potential to reduce ED wait-times through improved resource allocation and policy planning. Another 2 (1.3%) studies used ML to increase the efficiency of patient identification for ED clinical trials and research, with the goal of allowing research to be more accessible and standardized in an often fast-paced environment.…”

“…A total of 18 (12.0%) of our included studies demonstrated that AI can assist organizational planning and management within the ED, including optimization of nursing staff hours, patient satisfaction, and resource planning. Six studies used ML to predict daily patient volume and flow within the ED, including daily trauma volume 48–53 . These interventions have the potential to reduce ED wait‐times through improved resource allocation and policy planning.…”