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Effects of emergency department expansion on emergency department patient flow.

ABSTRACT: Emergency department (ED) crowding is an increasing problem associated with adverse patient outcomes. ED expansion is one method advocated to reduce ED crowding. The objective of this analysis was to determine the effect of ED expansion on measures of ED crowding.This was a retrospective study using administrative data from two 11-month periods before and after the expansion of an ED from 33 to 53 adult beds in an academic medical center. ED volume, staffing, and hospital admission and occupancy data were obtained either from the electronic health record (EHR) or from administrative records. The primary outcome was the rate of patients who left without being treated (LWBT), and the secondary outcome was total ED boarding time for admitted patients. A multivariable robust linear regression model was used to determine whether ED expansion was associated with the outcome measures.The mean (±SD) daily adult volume was 128 (±14) patients before expansion and 145 (±17) patients after. The percentage of patients who LWBT was unchanged: 9.0% before expansion versus 8.3% after expansion (difference = 0.6%, 95% confidence interval [CI] = -0.16% to 1.4%). Total ED boarding time increased from 160 to 180 hours/day (difference = 20 hours, 95% CI = 8 to 32 hours). After daily ED volume, low-acuity area volume, daily wait time, daily boarding hours, and nurse staffing were adjusted for, the percentage of patients who LWBT was not independently associated with ED expansion (p = 0.053). After ED admissions, ED intensive care unit (ICU) admissions, elective surgical admissions, hospital occupancy rate, ICU occupancy rate, and number of operational ICU beds were adjusted for, the increase in ED boarding hours was independently associated with the ED expansion (p = 0.005).An increase in ED bed capacity was associated with no significant change in the percentage of patients who LWBT, but had an unintended consequence of an increase in ED boarding hours. ED expansion alone does not appear to be an adequate solution to ED crowding.

SUBMITTER: Mumma BE

PROVIDER: S-EPMC4046120 | biostudies-literature | 2014 May

REPOSITORIES: biostudies-literature

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Effects of emergency department expansion on emergency department patient flow.

Mumma Bryn E BE McCue James Y JY Li Chin-Shang CS Holmes James F JF

Academic emergency medicine : official journal of the Society for Academic Emergency Medicine 20140501 5

<h4>Objectives</h4>Emergency department (ED) crowding is an increasing problem associated with adverse patient outcomes. ED expansion is one method advocated to reduce ED crowding. The objective of this analysis was to determine the effect of ED expansion on measures of ED crowding.<h4>Methods</h4>This was a retrospective study using administrative data from two 11-month periods before and after the expansion of an ED from 33 to 53 adult beds in an academic medical center. ED volume, staffing, a ...[more]

PMID: 24842500

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Project description:OBJECTIVE:The objective of this study is to assess the impact of a multimodal intervention on emergency department (ED) crowding and patient flow in a Dutch level 1 trauma center. METHODS:In this cross-sectional study, we compare ED crowding and patient flow between a 9-month pre-intervention period and a 9-month intervention period, during peak hours and overall (24/7). The multimodal intervention included (1) adding an emergency nurse practitioner (ENP) and (2) five medical specialists during peak hours to the 24/7 available emergency physicians (EPs), (3) a Lean programme to improve radiology turnaround times, and (4) extending the admission offices' openings hours. Crowding is measured with the modified National ED OverCrowding Score (mNEDOCS). Furthermore, radiology turnaround times, patients' length of stay (LOS), proportion of patients leaving without being seen (LWBS) by a medical provider, and unscheduled representations are assessed. RESULTS:The number of ED visits were grossly similar in the two periods during peak hours (15,558 ED visits in the pre-intervention period and 15,550 in the intervention period) and overall (31,891 ED visits in the pre-intervention period vs. 32,121 in the intervention period). During peak hours, ED crowding fell from 18.6% (pre-intervention period) to 3.5% (intervention period), radiology turnaround times decreased from an average of 91?min (interquartile range 45-256?min) to 50?min (IQR 30-106?min., p <?0.001) and LOS reduced with 13?min per patient from 167 to 154?min (p <?0.001). For surgery, neurology and cardiology patients, LOS reduced significantly (with 17?min, 25?min, and 8?min. respectively), while not changing for internal medicine patients. Overall, crowding, radiology turnaround times and LOS also decreased. Less patients LWBS in the intervention period (270 patients vs. 348 patients, p <?0.001) and less patients represented unscheduled within 1 week after the initial ED visit: 864 (2.7%) in the pre-intervention period vs. 645 (2.0%) patients in the intervention period, p <?0.001. CONCLUSIONS:In this hospital, a multimodal intervention successfully reduces crowding, radiology turnaround times, patients' LOS, number of patients LWBS and the number of unscheduled return visits, suggesting improved ED processes. Further research is required on total costs of care and long-term effects.

Project description:BackgroundEmergency 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.MethodsThis 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.ResultsA 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.ConclusionsThe 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.

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Effects of emergency department expansion on emergency department patient flow.

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Effects of emergency department expansion on emergency department patient flow.

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