Marc H Gorelick1, Kenneth Yen, Hyun J Yun. 1. Section of Emergency Medicine, Department of Pediatrics, Medical College of Wisconsin, Milwaukee, USA. mgorelic@mcw.edu <mgorelic@mcw.edu>
Abstract
STUDY OBJECTIVE: Length of stay is a measure of efficiency of delivery of care and is an important determinant of patient satisfaction in a pediatric emergency department (ED). Although length of stay is affected by many unmodifiable factors, changes in care processes may lead to improvements in throughput. Evaluating the success of such changes, however, requires an understanding of the effect of other determinants. We determine the factors associated with ED length of stay and estimate the effect of an in-room registration process on throughput time. METHODS: This was a before-and-after study at an academic pediatric ED with annual census of 45,000 visits. The study period was January 2, 2000, through December 31, 2003. In June 2003, an in-room registration process was initiated so that patients were placed directly into a room after triage, and the registration process was completed after physician evaluation. Weekly data were obtained on average length of stay and the following possible confounders: average daily ED census, hospital admissions from the ED, hospital occupancy rate, hours of physician staffing, and month of the year. An autoregressive integrated moving-average time series model was used to determine the effect of each variable on length of stay. RESULTS: Data were obtained for 209 weekly intervals during the study period; average length of stay ranged from 2.2 to 3.8 hours. All factors were highly associated with length of stay except for the number of hours of physician staffing (P =.71). The presence of in-room registration led to an estimated average decrease in length of stay of 15.0 minutes (95% confidence interval 6.3 to 33.4 minutes), or 9.3%. CONCLUSION: In-room registration leads to a statistically significant and practically meaningful improvement in total length of stay in the pediatric ED. Throughput time can be largely explained by a limited number of factors; our predictive model may facilitate the evaluation of other interventions.
STUDY OBJECTIVE: Length of stay is a measure of efficiency of delivery of care and is an important determinant of patient satisfaction in a pediatric emergency department (ED). Although length of stay is affected by many unmodifiable factors, changes in care processes may lead to improvements in throughput. Evaluating the success of such changes, however, requires an understanding of the effect of other determinants. We determine the factors associated with ED length of stay and estimate the effect of an in-room registration process on throughput time. METHODS: This was a before-and-after study at an academic pediatric ED with annual census of 45,000 visits. The study period was January 2, 2000, through December 31, 2003. In June 2003, an in-room registration process was initiated so that patients were placed directly into a room after triage, and the registration process was completed after physician evaluation. Weekly data were obtained on average length of stay and the following possible confounders: average daily ED census, hospital admissions from the ED, hospital occupancy rate, hours of physician staffing, and month of the year. An autoregressive integrated moving-average time series model was used to determine the effect of each variable on length of stay. RESULTS: Data were obtained for 209 weekly intervals during the study period; average length of stay ranged from 2.2 to 3.8 hours. All factors were highly associated with length of stay except for the number of hours of physician staffing (P =.71). The presence of in-room registration led to an estimated average decrease in length of stay of 15.0 minutes (95% confidence interval 6.3 to 33.4 minutes), or 9.3%. CONCLUSION: In-room registration leads to a statistically significant and practically meaningful improvement in total length of stay in the pediatric ED. Throughput time can be largely explained by a limited number of factors; our predictive model may facilitate the evaluation of other interventions.
Authors: Daniel A Handel; Adit A Ginde; Ali S Raja; John Rogers; Ashley F Sullivan; Janice A Espinola; Carlos A Camargo Journal: Int J Emerg Med Date: 2010-08-21
Authors: Juliane Lauks; Blaz Mramor; Klaus Baumgartl; Heinrich Maier; Christian H Nickel; Roland Bingisser Journal: PLoS One Date: 2016-04-22 Impact factor: 3.240