Who is sleeping in our beds? Factors predicting the ED boarding of admitted patients for more than 2 hours

J Emerg Nurs. 2011 May;37(3):225-30. doi: 10.1016/j.jen.2010.02.020. Epub 2010 Mar 31.

Abstract

Introduction: Although the provision of inpatient care is not typically associated with emergency nursing, it is the new reality in many departments. Given the number of admitted patients boarded in the emergency department for part or all of their hospital stay, it is important to know who these patients are. The purpose of this analysis was to determine whether the occurrence of ED boarding could be predicted by factors specific to the type and timing of the ED visit or whether patient characteristics also affected these decisions.

Methods: A retrospective review of administrative data for a 1-year period was conducted. Chi-square and logistic regression analyses were used to determine whether the likelihood of being boarded for more than 2 hours could be predicted by factors specific to the type of visit (ie, triage level and admission type) and timing of the visit (ie, time of day and day of week) or whether patient characteristics (ie, sex and age group) also played a role.

Results: Slightly more than half of patients remained in the emergency department for more than 2 hours following receipt of an admission order. Results suggest the likelihood of boarding was highest for those who were medical admissions and admitted on a weekday or during the night shift. Even after accounting for these factors, patient characteristics improved the ability to predict ED boarding. Female patients and those 65 years of age or older were more likely to be boarded.

Conclusions: Findings suggest that in addition to their usual responsibilities, emergency nurses are providing care to a group of inpatients who tend to have high medical and nursing care needs.

MeSH terms

  • Adolescent
  • Adult
  • Aged
  • Aged, 80 and over
  • Bed Occupancy
  • Chi-Square Distribution
  • Child
  • Child, Preschool
  • Emergency Service, Hospital / organization & administration*
  • Female
  • Humans
  • Infant
  • Infant, Newborn
  • Logistic Models
  • Male
  • Middle Aged
  • New Brunswick
  • Nursing Administration Research
  • Patient Admission / statistics & numerical data
  • Patient Transfer / statistics & numerical data
  • Retrospective Studies
  • Young Adult