Abstract
Background Emergency department (ED) crowding and prolonged lengths of stay continue to be important medical issues. It is difficult to apply traditional methods to analyze multiple streams of the ED patient management process simultaneously. The aim of this study was to develop a statistical model to delineate the dynamic patient flow within the ED and to analyze the effects of relevant factors on different patient movement rates. Methods This study used a retrospective cohort available with electronic medical data. Important time points and relevant covariates of all patients between January and December 2013 were collected. A new five-state Markov model was constructed by an expert panel, including three intermediate states: triage, physician management, and observation room and two final states: admission and discharge. A day was further divided into four six-hour periods to evaluate dynamics of patient movement over time. Results A total of 149,468 patient records were analyzed with a median total length of stay being 2.12 (interquartile range = 6.51) hours. The patient movement rates between states were estimated, and the effects of the age group and triage level on these movements were also measured. Patients with lower acuity go home more quickly (relative rate (RR): 1.891, 95% CI: 1.881–1.900) but have to wait longer for physicians (RR: 0.962, 95% CI: 0.956–0.967) and admission beds (RR: 0.673, 95% CI: 0.666–0.679). While older patients were seen more quickly by physicians (RR: 1.134, 95% CI: 1.131–1.139), they spent more time waiting for the final state (for admission RR: 0.830, 95% CI: 0.821–0.839; for discharge RR: 0.773, 95% CI: 0.769–0.776). Comparing the differences in patient movement rates over a 24-hour day revealed that patients wait longer before seen by physicians during the evening and that they usually move from the ED to admission afternoon. Predictive dynamic illustrations show that six hours after the patients' entry, the probability of still in the ED system ranges from 28% in the evening to 38% in the morning. Conclusions The five-state model well described the dynamic ED patient flow and analyzed the effects of relevant influential factors at different states. The model can be used in similar medical settings or incorporate different important covariates to develop individually tailored approaches for the improvement of efficiency within the health professions.
Highlights
IntroductionIn a previous review article, Wiler et al introduced several modeling approaches that have been used to describe or predict Emergency department (ED) management [6]
Modern emergency medicine has undergone rapid growth over the past half a century. [1] Improvements in medical knowledge and diagnostic protocols have led to more competent emergency department (ED) systems that can manage a wide range of medical emergencies
Admission patients included those admitted to the intensive care unit, those admitted to a ward, and those who were transferred to another hospital for admission. e outcomes of measurement were the states that each patient been to and the duration they stayed in each state
Summary
In a previous review article, Wiler et al introduced several modeling approaches that have been used to describe or predict ED management [6] Some of these methods, such as regression-based methods, are useful for defining ED crowding, due to their ease of use; methods such as time series-based analysis are effective in predicting patient arrival patterns; and methods such as event-time analysis are able to analyze the influential factors affecting ED LOS. E aim of this study was to develop a statistical model to delineate the dynamic patient flow within the ED and to analyze the effects of relevant factors on different patient movement rates. E five-state model well described the dynamic ED patient flow and analyzed the effects of relevant influential factors at different states. Conclusions. e five-state model well described the dynamic ED patient flow and analyzed the effects of relevant influential factors at different states. e model can be used in similar medical settings or incorporate different important covariates to develop individually tailored approaches for the improvement of efficiency within the health professions
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