Predicting Length of Stay among Patients Discharged from the Emergency Department-Using an Accelerated Failure Time Model

• Published in: PloS one Vol. 12; no. 1; p. e0165756
• Main Authors: Chaou, Chung-Hsien, Chen, Hsiu-Hsi, Chang, Shu-Hui, Tang, Petrus, Pan, Shin-Liang, Yen, Amy Ming-Fang, Chiu, Te-Fa
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 20-01-2017; Public Library of Science (PLoS)
• Subjects: Acuity; Adult; Aged; Aged, 80 and over; Analysis; Computed tomography; Consultation; Crowding; Data analysis; Data processing; Diagnostic systems; Discharge; Emergency medical care; Emergency medical services; Emergency Service, Hospital - organization & administration; Emergency services; Failure analysis; Female; Health care; Hospital admission and discharge; Hospital emergency services; Hospital stays; Humans; Laboratory tests; Length of Stay; Male; Management; Mathematical models; Medical personnel; Medicine and Health Sciences; Middle Aged; Models, Organizational; Multivariate analysis; Patient Discharge; Patients; Pediatrics; People and Places; Physicians; Predictions; Preventive medicine; Research and Analysis Methods; Retrospective Studies; Trauma; Young Adult
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0165756

Emergency department (ED) crowding continues to be an important health care issue in modern countries. Among the many crucial quality indicators for monitoring the throughput process, a patient's length of stay (LOS) is considered the most important one since it is both the cause and the result of ED crowding. The aim of this study is to identify and quantify the influence of different patient-related or diagnostic activities-related factors on the ED LOS of discharged patients.
This is a retrospective electronic data analysis. All patients who were discharged from the ED of a tertiary teaching hospital in 2013 were included. A multivariate accelerated failure time model was used to analyze the influence of the collected covariates on patient LOS.
A total of 106,206 patients were included for analysis with an overall medium ED LOS of 1.46 (interquartile range = 2.03) hours. Among them, 96% were discharged by a physician, 3.5% discharged against medical advice, 0.5% left without notice, and only 0.02% left without being seen by a physician. In the multivariate analysis, increased age (>80 vs <20, time ratio (TR) = 1.408, p<0.0001), higher acuity level (triage level I vs. level V, TR = 1.343, p<0.0001), transferred patients (TR = 1.350, p<0.0001), X-rays obtained (TR = 1.181, p<0.0001), CT scans obtained (TR = 1.515, p<0.0001), laboratory tests (TR = 2.654, p<0.0001), consultation provided (TR = 1.631, p<0.0001), observation provided (TR = 8.435, p<0.0001), critical condition declared (TR = 1.205, p<0.0001), day-shift arrival (TR = 1.223, p<0.0001), and an increased ED daily census (TR = 1.057, p<0.0001) lengthened the ED LOS with various effect sizes. On the other hand, male sex (TR = 0.982, p = 0.002), weekend arrival (TR = 0.928, p<0.0001), and adult non-trauma patients (compared with pediatric non-trauma, TR = 0.687, p<0.0001) were associated with shortened ED LOS. A prediction diagram was made accordingly and compared with the actual LOS.
The influential factors on the ED LOS in discharged patients were identified and quantified in the current study. The model's predicted ED LOS may provide useful information for physicians or patients to better anticipate an individual's LOS and to help the administrative level plan its staffing policy.