Complexity-Augmented Triage: A Tool for Improving Patient Safety and Operational Efficiency

Abstract

H ospital emergency departments (EDs) typically use triage systems that classify and prioritize patients almost exclusively in terms of their need for timely care. Using a combination of analytic and simulation models, we demonstrate that adding an up-front estimate of patient complexity to conventional urgency-based classification can substantially improve both patient safety (by reducing the risk of adverse events) and operational efficiency (by shortening the average length of stay). Moreover, we find that EDs with high resource (physician and/or examination room) utilization, high heterogeneity in the treatment time between simple and complex patients, and a relatively equal number of simple and complex patients benefit most from complexity-augmented triage. Finally, we find that (1) although misclassification of a complex patient as simple is slightly more harmful than vice versa, complexity-augmented triage is relatively robust to misclassification error rates as high as 25%; (2) streaming patients based on complexity information and prioritizing them based on urgency is better than doing the reverse; and (3) separating simple and complex patients via streaming facilitates the application of lean methods that can further amplify the benefit of complexity-augmented triage.

Task Selection and Workload: A Focus on Completing Easy Tasks Hurts Long-Term Performance

“…This is perhaps true for machines, but rather less so for humans who have task discretion -the ability to select their next task. Discretion is a topic that has been examined from several different dimensions in the operations literature such as routing work to different servers (Shumsky and Pinker 2003;Saghafian et al 2014;Freeman et al 2016), capacity allocation (Kim et al 2015), making a tradeoff between speed and quality (Hopp et al 2007;Anand et al 2011;Powell, Savin and Savva 2012), working in a dedicated vs. a pooled queue (Song, Tucker and Morrell 2015), and determining processing times in the face of different inventory levels (Schultz et al 1998;Schultz, Juran and Boudreau 1999).…”

Cyclostationarity: New trends and applications

“…Further applications are in the following fields: circuits [120,Section 14,136,262,361], control [29], periodic autoregressive (PAR) [67] and periodic autoregressive moving average (PARMA) [120, Section 12, 7,95] system analysis, prediction problems [109,238], linear extrapolation [90,91], finance and econometrics [120,Section 20,43,27,160], operations management [305], climatology [84,125,139,151,[188][189][190][191][192][193][194]276,308,331], ecosystem analysis [360], optics and spectroscopy [65,105,[309][310][311][312][313], astrophysics [78,100,212,213], voice speech analysis [53], and geophysics [177].…”

Healthcare operations management: a structured literature review

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