IMPROVING EMERGENCY DEPARTMENT THROUGHPUT BY ADOPTION OF AN ADMISSIONS PREDICTOR TOOL AT TRIAGE Public Deposited

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  • March 21, 2019
Creator
  • Linthicum, Benjamin
    • Affiliation: School of Nursing
Abstract
  • Emergency departments are increasingly busier and busier. An area of concern for many hospitals is how to deal with the resulting overcrowding and related throughput problems. This is because delayed throughput is seen as a measure of quality due to its association with negative patient outcomes. In this quality improvement project I sought to use an admissions predictor tool at triage to improve emergency department throughput by changing the process by which patients are identified and then processed for admissions. A new process was put into place where a patient who was predicted highly likely for admission by the predictor tool would have a bed requested for them immediately after triage but prior to further emergency department evaluation. This would allow for parallel processing of emergency department evaluation during the inpatient bed assignment process. A second goal for the project was to add to the collective evidence regarding the use of an admission predictor tool. This includes the practicality of its use as well as potential ways in which the tool could be improved upon or otherwise used beyond the early bed request process. I found the admissions process to be much more complex than initially anticipated and due to this complexity only one patient out of 281 patients screened underwent the new early bed request process. I found that in order to successfully use the new process, patients not only need to be identified for admission but their admission service and level of care also need to be identified. I found areas for improvement, of the admission predictor tool, namely the inclusion of comorbidities. I was able to find a new use for the predictor tool. By calculating an admissions probability on all patients in the emergency department, not already identified for admission, the tool was used to predict bed needs for the whole department at one time. This aggregate prediction tool can be useful in planning hospital operations to meet the bed needs of patients’ hours sooner than current methods.
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Advisor
  • Hubbell, Sara
  • Travers, Debbie
  • Mehrotra, Abhi
Degree granting institution
  • University of North Carolina at Chapel Hill
Graduation year
  • 2018
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