Background: Patients who present to emergency departments (EDs) for evaluation but are noted to have left without being seen (LWBS) are potentially at great risk. Governmental agencies, such as the Centers for Medicare and Medicaid, as well as hospitals and health organizations, are examining the factors which drive LWBS, including accurately quantifying patient tolerance to wait times and targeting interventions to improve patient tolerance to waiting.

Objective: Compare traditional methods of estimating time to LWBS with an objective method using a real-time location tracking system (RTLS); examine temporal factors associated with greater LWBS rates.

Methods: This is a retrospective cohort study of all ED visits to a large, suburban, quaternary care hospital in one calendar year. LWBS was calculated as patient registration to nurse recognition and documentation of patient abandonment (traditional method) vs registration to last onsite RTLS timestamp (study method). Descriptives of patterns of patient abandonment rates and patient demographic data were also included.

Results: Our study shows that traditional methods of measuring LWBS times significantly overestimate actual patient tolerance to waiting times (median 70, mean 92 min). Patients triaged to resource intensive categories (Emergency Severity Index (ESI) 2, 3) wait longer than patients triaged to less resource intensive categories (ESI 4, 5).

Conclusion: Compared to traditional methods, RTLS is an efficient and accurate way to measure LWBS rates and helps set the stage for assessing the efficacy of interventions to reduce LWBS and reduce the gap between those seeking evaluation at emergency departments and those ultimately receiving it.

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.ajem.2019.06.025DOI Listing

Publication Analysis

Top Keywords

patient tolerance
12
traditional methods
12
real-time location
8
emergency departments
8
lwbs
8
patient abandonment
8
patients triaged
8
triaged resource
8
resource intensive
8
intensive categories
8

Similar Publications

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!