Left without being seen in a hybrid point of service collection model emergency department

Am J Emerg Med. 2020 Mar;38(3):497-502. doi: 10.1016/j.ajem.2019.05.034. Epub 2019 May 18.

Abstract

Objective: This study identifies reasons and predictors of LWBS and examines outcomes of patients in a model that uses "point-of-service" (POS) collection for low acuity patients.

Methods: This was a matched case-control study of all patients who left without being seen from the ED of a tertiary care center in Beirut Lebanon between June 2016 and May 2017. Matching was done for the ESI score, date and time (±2 h). A descriptive analysis and a bivariate analysis were conducted comparing patients who LWBS and those who completed their medical treatment. This was followed by a Logistic regression to identify predictors of LWBS.

Results: 133 LWBS cases and 133 matched controls were enrolled in the study. Mean age for LWBS patients was (31.69 ± 15.29). The average reported wait time of LWBS patients was reported as 27.48 min (±25.09). Reasons for LWBS were; non-compensable status (66.9%), financial reasons (12.8%), long waiting times (12.8%), and others (8.3%). The majority of LWBS patients (81.2%) sought medical care after leaving the ED, and 8.3% of the LWBS patients represented to the ED after 48 h. Important predictors of LWBS included male gender, lower than undergraduate education level, waiting room time, non-compensable coverage status and fewer ED visits in the past year.

Conclusion: In an ED setting with POS collection for low acuity patients, non-compensable coverage status was the strongest predictor for LWBS. Further studies are needed to assess the outcomes of patients who LWBS in this model of care.

Keywords: Clinical outcomes; Emergency department; Insurance; Leaving without being seen; Point-of-service model; Predictors.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adolescent
  • Adult
  • Case-Control Studies
  • Emergency Service, Hospital / organization & administration*
  • Female
  • Humans
  • Insurance Coverage / statistics & numerical data
  • Lebanon
  • Male
  • Middle Aged
  • Patient Dropouts / statistics & numerical data*
  • Point-of-Care Systems / organization & administration
  • Sex Distribution
  • Surveys and Questionnaires
  • Time-to-Treatment / statistics & numerical data
  • Young Adult