Assessing time to treatment and patient inflow in a Danish emergency department: a cohort study using data from electronic emergency screen boards

BMC Res Notes. 2014 Oct 6;7:690. doi: 10.1186/1756-0500-7-690.

Abstract

Background: The purpose of this study was to assess and describe the patient inflow during a 1-month period in a Danish emergency department and to evaluate if the intended times to treatment (TTT) related to category of triage were met.

Methods: Data from electronic emergency screen boards were extracted from the 1st to the 30th of April 2013. 2000 patients were enrolled of which 1011 were eligible for inclusion in the study of TTT. Patient inflow was described according to hours of the day and days of the week. Patients were divided into groups of triage and TTT was assessed in the different groups. Adjusted odds ratios of not being seen on time were calculated between triage groups and time of the day/week.

Results: The pattern of inflow differed between weekdays and weekends. On weekdays it peaked around midday and on weekends it peaked during the late afternoon/evening. The distributions of the different triage categories between days were similar. Monday had the most patient contacts while Saturday showed the least. Category II (orange) patients were the most prone to exceed the intended TTT. The risk of not being seen on time when compared to daytime, was on evenings OR 2.3 [1.1;4.9] and on nights OR 2.0 [1.2;3.9]. On weekends the odds ratio was OR 1.9 [0.8;4.7] compared to weekdays.

Conclusion: The results demonstrated varying patterns of patient inflow between weekdays and weekends. There was a significantly increased risk of being attended late when arriving on evenings and nights. Likewise higher acuity was associated with exceeded TTT.

Publication types

  • Comparative Study
  • Research Support, Non-U.S. Gov't

MeSH terms

  • After-Hours Care
  • Chi-Square Distribution
  • Cohort Studies
  • Delivery of Health Care
  • Denmark
  • Electronics / instrumentation*
  • Emergency Service, Hospital* / trends
  • Equipment Design
  • Humans
  • Length of Stay
  • Logistic Models
  • Odds Ratio
  • Time Factors
  • Time-to-Treatment* / trends
  • Triage* / trends
  • Workflow*