Short-stay admissions at an inner city hospital: a cross-sectional analysis

Emerg Med J. 2018 Apr;35(4):238-246. doi: 10.1136/emermed-2016-205803. Epub 2018 Jan 5.

Abstract

Objective: To investigate factors predictive of short hospital admissions and appropriate placement to inpatient versus clinical decision units (CDUs).

Method: This is a retrospective analysis of attendance and discharge data from an inner-city ED in England for December 2013. The primary outcome was admission for less than 48 hours either to an inpatient unit or CDU. Variables included: age, gender, ethnicity, deprivation score, arrival date and time, arrival method, admission outcome and discharge diagnosis. Analysis was performed by cross-tabulation followed by binary logistic regression in three models using the outcome measures above and seeking to identify factors associated with short-stay admission.

Results: There were 2119 (24%) admissions during the study period and 458 were admitted for less than 24 hours. Those who were admitted in the middle of the week or with ambulatory care sensitive conditions (ACSCs) were significantly more likely to experience short-stays. Older patients and those who arrived by ambulance were significantly more likely to have a longer hospital stay. There was no association of length of inpatient stay with being admitted in the last 10 min of a 4 hours ED stay.

Conclusion: Only a few factors were independently predictive of short stays. Patients with ACSCs were more likely to have short stays, regardless of whether they were admitted to CDU or an inpatient ward. This may be a group of patients that could be targeted for dedicated outpatient management pathways or CDU if they need admission.

Keywords: admission avoidance; emergency care systems; emergency department.

Publication types

  • Observational Study

MeSH terms

  • Adolescent
  • Adult
  • Aged
  • Aged, 80 and over
  • Cross-Sectional Studies
  • Decision Support Techniques
  • England
  • Female
  • Hospitalization / statistics & numerical data*
  • Hospitals, Urban / organization & administration
  • Hospitals, Urban / statistics & numerical data
  • Humans
  • Length of Stay / statistics & numerical data*
  • Logistic Models
  • Male
  • Middle Aged
  • Retrospective Studies
  • State Medicine / organization & administration
  • State Medicine / statistics & numerical data
  • Time Factors*