An Integrated Strategic and Tactical Master Surgery Scheduling Approach With Stochastic Resource Demand

• DOI: http://doi.org/10.1111/jbl.12105
• Published date: 01 December 2015
• Date: 01 December 2015

An Integrated Strategic and Tactical Master Surgery Scheduling

Approach With Stochastic Resource Demand

Andreas F€

ugener

University of Augsburg

This study discusses a master surgery scheduling approach that maximizes hospital revenues while considering the impact on the down-

stream resources—intensive care units and general patient wards. Demand for these resources is modeled by a stochastic patient path

model. I apply an integer programming model to provide the optimal allocation of how many (strategic planning problem) and what (tactical

planning problem) operating room (OR) blocks to assign to each medical specialty. I demonstrate further applications of the model, such as the

analysis of the value of flexible resources and the simulation of specific resource expansions. The approach is tested with real-life data from a

German university hospital and achieves significant revenue increases.

Keywords: OR in health services; resource allocation; master surgery scheduling; ward and ICU occupancy

INTRODUCTION

Since the introduction of the diagnosis related group (DRG)-

based contribution systems (Fetter and Freeman 1986), profitabil-

ity is a rising issue in hospitals. In 2012, more than 50% of

German hospitals encountered losses (Blum et al. 2013). Thus,

the importance of improving both revenues and resource usage is

evident. Many hospitals have started discussing strategic patient

mix problems to attract patient classes with high revenues and to

specialize in fields with growth potential. Much work has been

done on the management of scarce resources (Hulshof et al.

2012). The operating room (OR) department is the focus of many

studies (Cardoen et al. 2010; Guerriero and Guido 2011), as it is

the biggest cost block in most hospitals. Around 40% of costs

accrue within the OR department (Denton et al. 2007). However,

the OR department cannot be seen in isolation as patient flows

connect it to various other resources (Vanberkel et al. 2010).

Patients could be admitted to the OR either by appointment or

via the emergency department. After surgery, patients either leave

the hospital (outpatients), or are transferred to the hospital wards

(inpatients). In more severe cases, patients may be treated in an

intensive care unit (ICU) before recovering in the regular wards.

There are two basic philosophies of OR management—open

scheduling and block scheduling (Guerriero and Guido 2011). In

open scheduling, surgeries are scheduled with a first come, first

serve logic. In block scheduling, combinations of OR and time

(here denoted as “blocks,”for example, one day in a specific

OR), are reserved for medical specialties. Patterson (1996) con-

ducts an empirical study comparing these different scheduling

regimes. Hybrid solutions with a combination of block and open

scheduling are denoted as modified block scheduling systems.

Most commonly, block booking systems are applied with so-

called cyclic master surgery schedules (MSSs), where the assign-

ment of blocks to specialties is repeated in a cyclical, for exam-

ple, weekly, pattern (van Oostrum et al. 2010). I differentiate

between four decision hierarchy levels—the strategic, the tactical,

the offline operational, and the online operational level (Hulshof

et al. 2012). In OR block scheduling, examples for decision

problems at the different levels are determining the number of

slots to be assigned to the medical specialties (strategic), what

block to assign to which specialty (tactical), where and when to

schedule an elective patient (offline operational), and how to

reschedule patients in case of emergencies (online operational).

The strategic problem has a major influence on both revenue and

cost, as the number of blocks assigned to a specialty influences

the number of patients admitted to the hospital. As different

patient types not only generate different revenue, but also require

different amounts of time to recover in the ICU and the ward,

revenue maximization, and capacity utilization are clearly linked.

In this study, I create cyclical MSSs with stochastic down-

stream resource demand. The ICUs and the regular patient wards

are considered as downstream resources. The research combines

a strategic with a tactical MSS problem since the model decides

how many (strategic) and what (tactical) OR blocks to assign to

each medical specialty. The overall objective is to maximize hos-

pital revenues. Although the contribution margin (i.e., the rev-

enues reduced by variable costs) could be considered in order to

maximize hospital profits, I follow the logic of Gupta (2007) and

do not differentiate between revenues and contribution margin as

most of hospital expenses are fixed.

The remainder of this study is structured as follows. The section

on “Related literature”provides a brief review on the current litera-

ture of strategic OR scheduling. In the section “Model”I present

an approach to model the assignment of specialties to OR blocks.

A case study with a German university hospital is presented in the

section “Case study.”The last section summarizes the findings and

presents possible directions of future research.

RELATED LITERATURE

A large body of literature on OR scheduling exists. For recent

reviews on this topic, I refer to Cardoen et al. (2010) and Guer-

riero and Guido (2011). Vanberkel et al. (2011) discuss models

that combine more than one department in hospitals. In this sec-

Corresponding author:

Andreas F€

ugener, Universit€

ares Zentrum f€

ur Gesundheitswis-

senschaften am Klinikum Augsburg UNIKA-T, Universit€

at Augs-

burg, Neus€

asser Strasse 47, 86156 Augsburg, Germany; E-mail:

andreas.fuegener@unikat.uni-augsburg.de

Journal of Business Logistics, 2015, 36(4): 374–387 doi: 10.1111/jbl.12105

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