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Multistate model of the patient flow process in the pediatric emergency department
Anqi Liu1,2,3, David M Kline1,2,3, Guy N Brock1,2,3,4,5
1Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, United States of America.
Insights
This study modeled emergency department (ED) patient flow to identify factors influencing wait times. Acuity, season, time of day, and physician staffing significantly impact ED length of stay.
Area of Science:
- Health Services Research
- Biostatistics
- Emergency Medicine
Background:
- Emergency departments (EDs) face challenges managing patient flow and lengthy wait times.
- Understanding the ED process is crucial for optimizing patient care and resource allocation.
Purpose of the Study:
- To model the patient journey through the ED, from arrival to discharge.
- To identify modifiable factors associated with ED length of stay using multistate modeling.
Main Methods:
- Analysis of 75,591 patient visits to Nationwide Children's Hospital ED from March 2016 to February 2017.
- Application of a multistate model with Cox proportional hazards models for transition-specific covariates.
- Utilized Aalen-Johansen estimator for transition and state occupation probabilities.
Main Results:
- Patient acuity, season, time of day, and physician numbers significantly affected ED transitions.
- Race/ethnicity impacted leaving without being seen; age/gender influenced early transitions.
- Covariate effects varied across different stages of the ED process.
Conclusions:
- The multistate model effectively breaks down ED length of stay into distinct transitions.
- Identifies specific, potentially modifiable factors to reduce waiting times at various ED stages.
- Provides actionable insights for improving ED operational efficiency and patient throughput.
Objectives:
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.
Methods:
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.
Results:
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.
Conclusions:
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.
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