Date of Award

12-2012

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Legacy Department

Industrial Engineering

Advisor

Taaffe, Kevin M

Committee Member

Fredendall , Lawrence D

Committee Member

Kurz , Mary B

Committee Member

Mayorga , Maria E

Abstract

The success of operating room management depends on all levels of decision making, from strategic to tactical and operational decisions. One key decision in systems with block booking is to assign sufficient amount of block time to surgeons and surgery groups. While the typical method of block assignment identifies the share of surgery groups from OR times based on average of past usage, this method does not count for the difference between cost of under and overtime. One of the goals of this research is to develop a decision framework for block assignment. This work is presented in chapter two. In this part first, I provide with the linear program that finds the length of block assigned to surgery groups while considering the amount of past undertime and overtime. This model then simplifieded through valid assumptions. In addition, a case study is conducted to support the usefulness of the method. The results show that 12 months of past data is sufficient amount of data to use in this method. Also, this method of block allocation out performs the existing time series method in literature.
Another key decision in an OR suite is to how manage elective and non-elective surgeries. The short and long term decisions regarding these two surgery types can change the waiting time of patients and the number of turned away surgeries. In order to accommodate elective and non-elective surgeries at lower cost to system and patients, both short and long term decisions play important roles. The long term decisions regarding the combination of rooms to choose in the system as well as the allocation to choose with the selected room combination are important decisions for OR managers. For short time decision making on the day of surgery a policy that indicate how to use share resources among the two surgery types is another important decision that OR managers need to nd an answer to. In this research I try to provide with methods and models that can guide managers in decision making process.
In this research using Markov decision processes (MDP), I introduce a model that could be used to find the optimal policy for use of operating rooms that are considered as shared resources while minimizing the overall cost of the system including waiting, turn-away and overtime. For that I focus on the system with a dedicated OR to non-elective surgeries and a flexible (shared) OR. I also model this system using simulation with Arena, by relaxing the MDP assumptions of steady state and the arrival and surgery times to find a policy that can minimize the cost of system. The simulation better reflects the real system of hospitals however it takes a long time to find a policy using taking simulation approach. In addition to that, the policy from simulation does not guarantee optimality. Moreover, the result of case study shows that relaxing MDP assumptions, simulation model finds the same policy as MDP. However, the MDP model could find an optimal policy in seconds.
Although MDP could be used to model the most common existing combinations of operating rooms, however, the optimal policy from MDP may be hard to implement. Therefore I use Markov chain to model combinations of operating rooms and define policies to be used on the day of surgery for accommodating elective and non-elective surgeries. I compare the performance of systems under defined policies by considering input parameters at different levels. I also consider several allocations under each system to find the best system and allocation. Results of this work shows that overall system with all flexible ORs has the minimum cost. However, some other systems may perform better in specific situations and scenarios. The best policy (among the studied policies) is depending on the room combinations and the chosen allocation.

Share

COinS