Literature DB >> 31128935

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

Eveline Hitti1, Dima Hadid2, Hani Tamim3, Moustafa Al Hariri4, Mazen El Sayed5.   

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.
Copyright © 2019 Elsevier Inc. All rights reserved.

Entities:  

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

Mesh:

Year:  2019        PMID: 31128935     DOI: 10.1016/j.ajem.2019.05.034

Source DB:  PubMed          Journal:  Am J Emerg Med        ISSN: 0735-6757            Impact factor:   2.469


  2 in total

1.  Patients leaving without being seen from the emergency department: A prediction model using machine learning on a nationwide database.

Authors:  Mack Sheraton; Christopher Gooch; Rahul Kashyap
Journal:  J Am Coll Emerg Physicians Open       Date:  2020-09-28

Review 2.  Methodological Approaches to Support Process Improvement in Emergency Departments: A Systematic Review.

Authors:  Miguel Angel Ortíz-Barrios; Juan-José Alfaro-Saíz
Journal:  Int J Environ Res Public Health       Date:  2020-04-13       Impact factor: 3.390

  2 in total

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