Objectives: The objectives were to evaluate three models that use information gathered during triage to predict, in real time, the number of emergency department (ED) patients who subsequently will be admitted to a hospital inpatient unit (IU) and to introduce a new methodology for implementing these predictions in the hospital setting. Methods: Three simple methods were compared for predicting hospital admission at ED triage: expert opinion, naïve Bayes conditional probability, and a generalized linear regression model with a logit link function (logit-linear). Two months of data were gathered from the Boston VA Healthcare System's 13-bed ED, which receives approximately 1,100 patients per month. Triage nurses were asked to estimate the likelihood that each of 767 triaged patients from that 2-month period would be admitted after their ED treatment, by placing them into one of six categories ranging from low to high likelihood. Logit-linear regression and naïve Bayes models also were developed using retrospective data and used to estimate admission probabilities for each patient who entered the ED within a 2-month time frame, during triage hours (1,160 patients). Predictors considered included patient age, primary complaint, provider, designation (ED or fast track), arrival mode, and urgency level (emergency severity index assigned at triage). Results: Of the three methods considered, logit-linear regression performed the best in predicting total bed need, with a receiver operating characteristic (ROC) area under the curve (AUC) of 0.887, an R 2 of 0.58, an average estimation error of 0.19 beds per day, and on average roughly 3.5 hours before peak demand occurred. Significant predictors were patient age, primary complaint, bed type designation, and arrival mode (p < 0.0001 for all factors). The naïve Bayesian model had similar positive predictive value, with an AUC of 0.841 and an R 2 of 0.58, but with average difference in total bed need of approximately 2.08 per day. Triage nurse expert opinion also had some predictive capability, with an R 2 of 0.52 and an average difference in total bed need of 1.87 per day. Conclusions: Simple probability models can reasonably predict ED-to-IU patient volumes based on basic data gathered at triage. This predictive information could be used for improved real-time bed management , patient flow, and discharge processes. Both statistical models were reasonably accurate, using only a minimal number of readily available independent variables.
Our definitions of cumulative and continuous polypharmacy serve to set upper and lower bounds for the estimate of polypharmacy. Our method for simultaneous polypharmacy gives numbers that diverge in some respects, but it is better at showing transient changes in medications. The methods are complementary and allow exploration of various aspects of medication use, such as cumulative medication exposure over time, the influence of chronic medical problems, and the causes of rapid changes in medications.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.