Date of Award
Master of Science in Systems Engineering
Department of Systems Engineering and Management
Christina F. Rusnock, PhD.
Many process improvement tools have been applied to the healthcare industry to improve safety and efficiency. However, nearly all of these tools have neglected to explicitly quantify mental workload of healthcare providers despite the consensus that it is related to human performance. This research uses the Improved Performance Research Integration Tool (IMPRINT), a discrete-event simulation (DES), to quantify mental workload. Specifically, this research examines staff members in an inpatient unit at the Wright-Patterson Medical Center to detect workload differences between staff, identify trends which lead to high workload demands, evaluate the influence of patient load on mental workload, and test a workload-leveling process improvement. Results from this study indicate workload differences between staff types and finds that task urgency and complexity play a role in the overloading of tasks. The relationship between predicted mental workload and increased patient load is mostly linear; however, the slopes are different between staff types, indicating that staff types are predicted to be affected unequally by increases in patient demand. Lastly, the task sharing process improvement provides mixed results; idle time and average workload become more balanced, but overload time becomes more unbalanced. Overall, this study demonstrates the usefulness of IMPRINT at evaluating medical systems.
DTIC Accession Number
Maxheimer, Erich W., "Analysis of Inpatient Hospital Staff Mental Workload by Means of Discrete-event Simulation" (2016). Theses and Dissertations. 404.