Simulation-Based Evaluation of the Effects of Patient Load on Mental Workload of Healthcare Staff

    loading  Checking for direct PDF access through Ovid

Abstract

Introduction

In parts of Ohio, Veterans Affairs Medical Centers are working to handle patient load issues by sending patient overflows to the Wright-Patterson Medical Center. The Wright-Patterson Medical Center will benefit from the increase in patients; however, there are concerns that the patient quality of care may suffer. If the increase in patients results in the healthcare staff experiencing high mental workload levels, staff performance could be reduced. The objective of this research is to evaluate the influence of patient load on the mental workload of staff in an inpatient unit at the Wright-Patterson Medical Center.

Methods

This research uses discrete-event simulation to quantitatively model the mental workload of healthcare staff in an inpatient unit of the Wright-Patterson Medical Center. The model was used to find the idle time, average workload, and overload time of healthcare staff under current and future patient loads. In addition, the performance of individual tasks was evaluated.

Results

The results of this research find a linear relationship between patient load and three workload metrics (idle time, average workload, and overload time) with each worsening as patient load increases. Nurses and technicians experience the greatest negative impacts to mental workload as patient load increases with those staff members who have the most workload at the baseline condition experiencing greater increase in workload as patient load increases. In addition, the time spent in an overload state increases disproportionately with patient load increases, with overload time increases being worse for urgent tasks than for nonurgent tasks.

Conclusions

Based on this study, the researchers found that the modeled inpatient unit can safely handle the expected patient load increases. The study provides the unit with information to proactively prepare and reduce healthcare staff overloading.

Related Topics

    loading  Loading Related Articles