Optimal Measurement Interval for Emergency Department Crowding Estimation Tools

被引:17
|
作者
Wang, Hao [1 ]
Ojha, Rohit P. [2 ,4 ]
Robinson, Richard D. [1 ]
Jackson, Bradford E. [2 ,4 ]
Shaikh, Sajid A. [3 ]
Cowden, Chad D. [1 ]
Shyamanand, Rath [3 ]
Leuck, JoAnna [1 ]
Schrader, Chet D. [1 ]
Zenarosa, Nestor R. [1 ]
机构
[1] John Peter Smith Hlth Network, Integrat Emergency Serv Phys Grp, Dept Emergency Med, Ft Worth, TX 76104 USA
[2] John Peter Smith Hlth Network, Ctr Outcomes Res, Ft Worth, TX USA
[3] John Peter Smith Hlth Network, Dept Informat Technol, Ft Worth, TX USA
[4] UNT Hlth Sci Ctr, Sch Publ Hlth, Dept Biostat & Epidemiol, Ft Worth, TX USA
关键词
OCCUPANCY; LENGTH; INDEX; STAY;
D O I
10.1016/j.annemergmed.2017.04.012
中图分类号
R4 [临床医学];
学科分类号
1002 ; 100602 ;
摘要
Study objective: Emergency department (ED) crowding is a barrier to timely care. Several crowding estimation tools have been developed to facilitate early identification of and intervention for crowding. Nevertheless, the ideal frequency is unclear for measuring ED crowding by using these tools. Short intervals may be resource intensive, whereas long ones may not be suitable for early identification. Therefore, we aim to assess whether outcomes vary by measurement interval for 4 crowding estimation tools.& para;& para;Methods: Our eligible population included all patients between July 1, 2015, and June 30, 2016, who were admitted to the JPS Health Network ED, which serves an urban population. We generated 1-, 2-, 3-, and 4-hour ED crowding scores for each patient, using 4 crowding estimation tools (National Emergency Department Overcrowding Scale [NEDOCS], Severely Overcrowded, Overcrowded, and Not Overcrowded Estimation Tool [SONET], Emergency Department Work Index [EDWIN], and ED Occupancy Rate). Our outcomes of interest included ED length of stay (minutes) and left without being seen or eloped within 4 hours. We used accelerated failure time models to estimate interval-specific time ratios and corresponding 95% confidence limits for length of stay, in which the 1-hour interval was the reference. In addition, we used binomial regression with a log link to estimate risk ratios (RRs) and corresponding confidence limit for left without being seen.& para;& para;Results: Our study population comprised 117,442 patients. The time ratios for length of stay were similar across intervals for each crowding estimation tool (time ratio = 1.37 to 1.30 for NEDOCS, 1.44 to 1.37 for SONET, 1.32 to 1.27 for EDWIN, and 1.28 to 1.23 for ED Occupancy Rate). The RRs of left without being seen differences were also similar across intervals for each tool (RR = 2.92 to 2.56 for NEDOCS, 3.61 to 3.36 for SONET, 2.65 to 2.40 for EDWIN, and 2.44 to 2.14 for ED Occupancy Rate).& para;& para;Conclusion: Our findings suggest limited variation in length of stay or left without being seen between intervals (1 to 4 hours) regardless of which of the 4 crowding estimation tools were used. Consequently, 4 hours may be a reasonable interval for assessing crowding with these tools, which could substantially reduce the burden on ED personnel by requiring less frequent assessment of crowding.
引用
收藏
页码:632 / 639
页数:8
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