Page 156 - The-5th-MCAIT2021-eProceeding

Issues in Surgical Scheduling Problem: Uncertainty, Capacity

Planning, Request and Demand

a
a*
b
c
Norizal Abdullah , Masri Ayob , Meng Chun Lam , Nasser R. Sabar
a Data Mining and Optimization Lab, Center for Artificial Intelligence Technology (CAIT), Faculty of Information Science and
Technology, Universiti Kebangsaan Malaysia Bangi Selangor, Malaysia
b Mixed Reality and Pervasive Computing Lab, Center for Artificial Intelligence Technology (CAIT), Faculty of Information Science and
Technology, Universiti Kebangsaan Malaysia Bangi Selangor, Malaysia
c Department of Computer Science and Information Technology, La Trobe University, Melbourne, Australia
* Email: norizal.abdullah23@gmail.com

Abstract

The surgical scheduling problem in healthcare is one of the most crucial problems that get attention from many researchers.
However, less concern is given to understand the issue in the surgical scheduling problem. The purpose of this review is to
reveal the crucial issues in a surgical scheduling problem. We organize the issues into three categories: (1) Uncertainty; (2)
Capacity Planning; and (3) Request and Demand. In the uncertainty issue, we discussed the issue that may cause uncertainty
that happens in surgical procedure, and for capacity planning issue, we concentrate on resources planning such as human
and material resources. The request and demand issue is related to the surgical case criteria and surgical team preferences.
At the end of the review, we determine the important issue that arises inside the surgical scheduling problem requires further
attention.

Keywords: Healthcare; Surgical Scheduling Problem; Hospital

1. Introduction

The surgical scheduling problem is described as the selection of procedures to be performed, the allocation
of resource time to those procedures, and the sequencing of those procedures within the time allotted (May,
Spangler, Strum, Vargas, & Management, 2011). (Belkhamsa, Jarboui, & Masmoudi, 2018) state that pre-
operative, intra-operative, and post-operative phases are stages in a surgical procedure. The surgery would
require both material and human resources to complete the procedures. Pre-operative Holding Units (PHU) beds
and nurses were required during pre-operative. Next, the intra-operative stage needs to consider the Operating
Room (OR), surgeons, anaesthetists, and nurses. Finally, services such as Incentive Care Unit (ICU) beds and
nurses are required for the post-operative phase.
There are two types of surgery: elective and emergency surgery (Silva & de Souza, 2020). Under elective
surgery, three types of surgery schedule strategy can be used either block scheduling, open scheduling and
modified block scheduling (Patterson, 1996). Although there is guidance for the surgery procedure and various
type of surgery schedule strategy to be used, there are still issues that may cause the surgical scheduling
disruption. (May, Spangler, Strum, Vargas, & Management, 2011), state that disruptions that happen in surgical
scheduling are mainly due to the uncertainty issue from the procedure duration and capacity planning issue. The
unbalanced scheduling of the OR department also often causes demand fluctuation in other departments such
as surgical wards and intensive care units (J. M. van Oostrum, Van Houdenhoven, Hurink, Hans, Wullink, &
Kazemier, 2008). (J. van Oostrum, van Houdenhoven, Wagelmans, & Kazemier, 2009) propose Master Surgical
Scheduling (MSS) to deal with this issue. The MSS implementation improved coordination among hospital
staff members and solve the issue of long-term forecasting and capacity planning. Another advantage is that it
boosts OR performance and eliminates the problem of confusion. (Silva & de Souza, 2020) proposed an
optimization method for the ambiguity problem in surgical scheduling by incorporating approximate dynamic
programming. Their method has the potential to make a big difference in practice. Resource constraints in
Surgical Department also may lead to the issue in surgical scheduling. As example, (Wang & Xu, 2017)

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Artificial Intelligence in the 4th Industrial Revolution