Users' experiences of an emergency department patient admission predictive tool: A qualitative evaluation

Health Informatics J. 2016 Sep;22(3):618-32. doi: 10.1177/1460458215577993. Epub 2015 Apr 27.

Abstract

Emergency department overcrowding is an increasing issue impacting patients, staff and quality of care, resulting in poor patient and system outcomes. In order to facilitate better management of emergency department resources, a patient admission predictive tool was developed and implemented. Evaluation of the tool's accuracy and efficacy was complemented with a qualitative component that explicated the experiences of users and its impact upon their management strategies, and is the focus of this article. Semi-structured interviews were conducted with 15 pertinent users, including bed managers, after-hours managers, specialty department heads, nurse unit managers and hospital executives. Analysis realised dynamics of accuracy, facilitating communication and enabling group decision-making Users generally welcomed the enhanced potential to predict and plan following the incorporation of the patient admission predictive tool into their daily and weekly decision-making processes. They offered astute feedback with regard to their responses when faced with issues of capacity and communication. Participants reported an growing confidence in making informed decisions in a cultural context that is continually moving from reactive to proactive. This information will inform further patient admission predictive tool development specifically and implementation processes generally.

Keywords: bed management and patient flow; communication; decision-making; evaluation; implementation; predictive modelling.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Bed Occupancy / statistics & numerical data
  • Communication
  • Decision Making*
  • Emergency Service, Hospital / statistics & numerical data*
  • Humans
  • Models, Statistical
  • Patient Admission / statistics & numerical data*
  • Qualitative Research
  • Workflow*