Date on Master's Thesis/Doctoral Dissertation
Industrial Engineering, PhD
Committee Co-Chair (if applicable)
performance; organization; complexity; simulation
This dissertation provides a method for evaluating the difference in performance after an organization makes a change while considering the stochastic nature in which it operates. A procedure that uses simulation to estimate outcomes by adjusting controllable parameters and leaving uncontrolled parameters unadjusted is proposed. As healthcare organizations are considered as highly complex systems, a case study involving a scheduling tactic change in the mother-baby service line of a hospital is used to demonstrate application of this procedure. The goal in the case study was to reduce delays in transitioning care of mother patients from the labor and delivery unit to the postpartum care unit. The Holds Rate metric measured delays as the number of mothers deemed to be unintentionally delayed from transferring to the postpartum care unit to the total number of deliveries. While the scheduling tactic change did not yield the anticipated result, the proposed procedure was used to show that performance would have been worse had the change not been made. Hospital leadership chose to keep the solution and target performance was later surpassed. Ultimately, hospital leaders heralded the project as a great success. The proposed procedure was applied with two different simulation methods. A Monte Carlo simulation model was used to measure Holds Rate and a discrete-event simulation model to measure the average delay time experienced by patients waiting to be placed in a postpartum bed following delivery. The results of the procedure with both models led to the same conclusion that the scheduling tactic change indeed reduced delays in the transitions of care between the two hospital units. The case study demonstrated the validity and applicability of the proposed procedure and organizations may benefit from its use as leaders may be more prone to act since analysis with the procedure isolates the effects of uncontrolled parameters. Isolating these effects to better understand those of controlled parameters can promote an organization’s sustainability by advancing knowledge of cause-and-effect relationships. Future research with this topic can include application with other simulation methods, investigating the impacts of technology advancements, and considering a method of analysis using Bayesian inference.
Harrington, William C. Jr., "Performance analysis of organizations as complex systems." (2017). Electronic Theses and Dissertations. Paper 2725.