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Research Papers

Tactical Operating Room Planning Based on System Transient Performance Control

[+] Author and Article Information
Zhigang Zeng

Department of Mechanical and
Manufacturing Engineering,
University of Calgary,
Calgary, AB T2N 1N4, Canada
e-mail: zenz@ucalgary.ca

Robert W. Brennan

Department of Mechanical and
Manufacturing Engineering,
University of Calgary,
Calgary, AB T2N 1N4, Canada
e-mail: rbrennan@ucalgary.ca

Theodor Freiheit

Department of Mechanical and
Manufacturing Engineering,
University of Calgary,
Calgary, AB T2N 1N4, Canada
e-mail: tfreihei@ucalgary.ca

Manuscript received February 24, 2018; final manuscript received April 15, 2018; published online May 9, 2018. Assoc. Editor: Shijia Zhao.

ASME J of Medical Diagnostics 1(3), 031004 (May 09, 2018) (14 pages) Paper No: JESMDT-18-1011; doi: 10.1115/1.4040055 History: Received February 24, 2018; Revised April 15, 2018

Efficient management of operating room (OR) schedules is important as the OR is the largest cost and revenue center in a hospital and can substantially impact its staffing and finances. A major problem associated with developing OR schedules for elective surgeries is the schedule disruption from uncertainty inherent in the duration of surgical services. Another problem is the cascaded impact on overall system performance of facilities and resources upstream and downstream to the OR. Using a manufacturing system analytical approach, the peri-operative process is modeled as a transfer line with three machines and two buffers by a discrete time Markov chain. Uncertain surgical and recovery duration is quantified probabilistically and incorporated in the Markov chain model with multistate geometrical machines. Model predictive control (MPC) to pace patient release into the ORs is then applied to control system transient performance. With this model and empirical studies of surgery and recovery duration, guidance can be given to OR managers on how to dynamically schedule and reschedule patients throughout an OR's day that minimizes cost for a given workload. The proposed predictive control model can also control other transient performance metrics such as OR and recovery room (RR) utilization, patient flow, and cost.

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Figures

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Fig. 1

A three-stage operating room dynamical scheduling system

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Fig. 2

Markov chain model of OR system

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Fig. 3

Machine state transition and buffer changing

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Fig. 4

Calculating transition probabilities using an absorbing state

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Fig. 5

Expected blockage of OR versus (u1 and u2)

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Fig. 6

Expected starvation of OR versus (u1 and u2)

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Fig. 7

Expected occupancy of OR versus (u1 and u2)

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Fig. 8

Expected utilization of the OR versus (u1 and u2)

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Fig. 9

Expected starvation of RR versus (u1 and u2)

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Fig. 10

Expected occupancy of RR versus (u1 and u2)

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Fig. 11

Expected utilization of RR versus (u1 and u2)

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Fig. 12

Steps of the discrete event simulation of the operating room system

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Fig. 13

Instantaneous cost under the different utilization

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Fig. 14

Estimated utilization versus simulation (n1=0)

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Fig. 15

Estimated utilization versus simulation (n1=1)

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Fig. 16

Estimated utilization versus simulation (n1=4)

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Fig. 17

Estimated utilization versus simulation (n1=6)

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Fig. 18

Estimated utilization versus simulation with ϕ1 and ϕ2 (n1=1)

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Fig. 19

Estimated utilization versus simulation with ϕ1 and ϕ2 (n1=3)

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Fig. 20

A simulation patient transfer to the OR

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Fig. 21

Probability of releasing patients to OR

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Fig. 22

Expected occupancy of OR as a percent of the total number of OR suites

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Fig. 23

Expected waiting time for an OR suite availability

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Fig. 24

Original schedule when emergency surgery is admitted at time-step 3

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Fig. 25

Revised schedule for the rest work time

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Fig. 26

Comparison of expected patient release probabilities

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Fig. 27

Comparison of expected OR occupancy as a percent of the total number of OR suites

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