Multistate model of the patient flow process in the pediatric emergency department

• Published in: PloS one 2019-07, Vol.14 (7), p.e0219514-e0219514
• Main Authors: Liu, Anqi, Kline, David M, Brock, Guy N, Bonsu, Bema K
• Format: Article
• Language: English
• Subjects: Acuity; Age Factors; Care and treatment; Child; Child, Preschool; Children & youth; Demographic aspects; Earth Sciences; Emergency medical care; Emergency medical services; Emergency Service, Hospital - statistics & numerical data; Ethnicity; Female; Hazards; Hospital emergency services; Hospitals; Hospitals, Pediatric - statistics & numerical data; Humans; Informatics; Length of Stay - statistics & numerical data; Male; Management; Medical personnel; Medical research; Medical societies; Medicine; Medicine and Health Sciences; Minority & ethnic groups; Modelling; Patient Admission - statistics & numerical data; Patient Discharge - statistics & numerical data; Patient satisfaction; Patients; Pediatric emergencies; Pediatrics; People and Places; Physician assistants; Physicians; Proportional Hazards Models; Public health; Retrospective Studies; Sex Factors; Statistical models; Time Factors; Time of use; Transition probabilities; Trauma centers
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0219514

The main purpose of this paper was to model the process by which patients enter the ED, are seen by physicians, and discharged from the Emergency Department at Nationwide Children's Hospital, as well as identify modifiable factors that are associated with ED lengths of stay through use of multistate modeling. In this study, 75,591 patients admitted to the ED from March 1st, 2016 to February 28th, 2017 were analyzed using a multistate model of the ED process. Cox proportional hazards models with transition-specific covariates were used to model each transition in the multistate model and the Aalen-Johansen estimator was used to obtain transition probabilities and state occupation probabilities in the ED process. Acuity level, season, time of day and number of ED physicians had significant and varying associations with the six transitions in the multistate model. Race and ethnicity were significantly associated with transition to left without being seen, but not with the other transitions. Conversely, age and gender were significantly associated with registration to room and subsequent transitions in the model, though the magnitude of association was not strong. The multistate model presented in this paper decomposes the overall ED length of stay into constituent transitions for modeling covariate-specific effects on each transition. This allows physicians to understand the ED process and identify which potentially modifiable covariates would have the greatest impact on reducing the waiting times in each state in the model.