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Hospitals offer medical and surgical care to the sick and injured, along with accommodation while they recover. At the same time, they also provide outpatient, emergency, psychiatric, and rehabilitation services to meet various community needs. In addition to providing medical care, hospitals also act as hubs for medical research and training. Hospitals use clinical procedures and evidence-based practice standards to deliver patient care. To deliver safe and efficient care, a nurse must stay up...
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Predicting Need for Hospital Beds to Reduce Emergency Department Boarding.

Lirong Cheng1,2, Megan Tapia1,2, Kimberly Menzel1

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|September 19, 2022
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Summary

A new bed calculator tool accurately predicts the number of available staffed beds (SEDs) needed for hospital admissions, reducing human error. However, it did not significantly decrease patient boarding times.

Keywords:
Bed calculatorBoarders in the ED (BED)ED boarding timeHospital operational managementUnoccupied staffed hospital beds (SED)

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Area of Science:

  • Healthcare Management
  • Hospital Operations
  • Predictive Analytics

Background:

  • Accurate hospital bed management is crucial for preventing adverse patient outcomes.
  • Electronic medical record data can enhance the prediction of bed availability.
  • Human estimation in bed allocation introduces variability and potential errors.

Purpose of the Study:

  • To reduce variability and human error in predicting the number of staffed beds (SEDs) required for new admissions.
  • To evaluate the efficacy of a unique bed calculator prediction tool.

Main Methods:

  • Analysis of bed calculator data from a medium-sized suburban medical center.
  • Utilizing a linear regression model that aggregates key reference factors.
  • Comparing the tool's predictions against human estimation.

Main Results:

  • The bed calculator demonstrated higher accuracy in predicting the number of SEDs needed compared to human estimation.
  • No significant difference was observed in average patient boarding times before and after the intervention.
  • The tool effectively aligns hospital bed supply with demand.

Conclusions:

  • The bed calculator is effective in predicting hospital bed needs and balancing supply and demand.
  • Improving patient flow management and inter-departmental communication is essential for reducing patient boarding times.
  • Future research should focus on optimizing patient flow to further decrease boarding times.