Robot‐assisted surgical care delivery at a hospital: Policies for maximizing clinical outcome benefits and minimizing costs

• Author: Kingshuk K. Sinha,Ujjal K. Mukherjee
• Date: 01 January 2020
• DOI: http://doi.org/10.1002/joom.1058
• Published date: 01 January 2020

RESEARCH ARTICLE

Robot-assisted surgical care delivery at a hospital: Policies for

maximizing clinical outcome benefits and minimizing costs

Ujjal K. Mukherjee

1

| Kingshuk K. Sinha

2

1

Business Administration, University of

Illinois, Urbana-Champaign, Illinois

2

Supply Chain and Operations Management,

University of Minnesota, Minneapolis,

Minnesota

Correspondence

Ujjal Mukherjee, University of Illinois,

Urbana-Champaign, Il.

Email: ukm@illinois.edu

Handling Editors: Lawrence Fredendall,

Anand Nair, Jeffery Smith and Anita Tucker

Abstract

Robot-assisted surgery is a major advancement in minimally invasive surgical care

delivery. It allows surgeons to gain additional dexterity, improved precision in sur-

gical tasks, enhanced three-dimensional vision of the surgical field, and superior

accessibility to the surgical field. Notwithstanding the clinical outcome benefits

(such as reduced blood loss and surgical duration, and shorter length-of-stay at a

hospital), the cost of performing robot-assisted surgery is significantly high, due to

the investments needed for robot acquisition, maintenance, and procurement of sur-

gical accessories. In this study, we address the twin objectives of a hospital where

robot-assisted surgery is performed: maximize the clinical outcome benefits and

minimize the total cost of robot-assisted surgery. The surgical robot that is the

focus of the study is a da Vinci surgical system. The surgical procedure that serves

as the context of this study is hysterectomy. We demonstrate the application of an

integrated methodological approach—combining empirical analysis involving an

on-site, prospective investigation at a hospital and retrospective analysis of archival

data from the hospital for robustness checks, analytical modeling, and discrete

event simulation—to identify and analyze actionable policies that a hospital can

implement. The specific policies we analyze are related to: (a) patient triaging for

robot-assisted surgery based on the criticality of disease condition of a patient;

(b) the optimal size of surgeon pool to facilitate the development of surgeon experi-

ence and learning; and (c) the minimum experience level of a surgeon with a robot

needed to be included in the surgeon pool for robot-assisted surgery. The key con-

tributions of this article are in demonstrating the following: First, hospital-level

policies can help to realize both the clinical outcome and cost benefits of a surgical

robot. Second, the criticality of patient condition is a significant determinant of sur-

geon learning in robot-assisted surgery. Third, application of the proposed inte-

grated methodological approach can yield nuanced and actionable insights into an

operational setting where data availability is limited and generalizability of study

insights is a concern, as is the case in this study.

KEYWORDS

cost of healthcare, hospital policy, hysterectomy, multi-method study, surgeon learning, robot-assisted

surgery

Received: 1 November 2017 Revised: 15 July 2019 Accepted: 11 August 2019

DOI: 10.1002/joom.1058

J Oper Manag. 2020;66:227–256. wileyonlinelibrary.com/journal/joom © 2019 Association for Supply Chain Management, Inc. 227

1|INTRODUCTION

Robot-assistedsurgery represents a majoradvancement in min-

imally invasive surgical care delivery. Clinical outcome bene-

fits of robot-assisted surgery vis-à-vis manual laparoscopy

include reduced surgical duration and blood loss, and shorter

length-of-stay at a hospital (Crolla, Mulder, & van der Schel-

ling, 2018). However, compared to manual laparoscopy, the

cost of performing robot-assisted surgery is significantly

higher, due to the investments needed for robot acquisition,

maintenance, and procurement of surgical accessories

(Attaluri & McLemore, 2016; Khorgami, Li, Jackson,

Howard, & Sclabas, 2018). Therefore, hospitals where robot-

assisted surgery is performed have twin objectives: to realize

the clinical outcome benefits of surgical robots, and simulta-

neously control the costs of robot-assisted surgery vis-à-vis

manual laparoscopy. Motivated by these twin objectives of

hospitals, the research question addressed in this article is:

What operationalpolicies will jointlymaximize the clinical out-

come benefits and minimize the total costs of robot-assisted

surgery vis-à-vismanual laparoscopy?

The surgical robot that is the focus of the study is the da

Vinci surgical system, manufactured by Intuitive Surgical

Inc.

1

The surgical procedure that is the context of this study

is hysterectomy. The hospital-level policies that we analyze

to address the overarching research question are related to:

(a) patient triaging for robot-assisted surgery based on the

criticality of the disease condition of a patient, (b) the opti-

mal size of surgeon pool to facilitate the development of sur-

geon experience and learning, and (c) the minimum

experience level of a surgeon with a robot needed to be

included in the surgeon pool for robot-assisted surgery. To

the best of our knowledge, this is the first study in health

care operations management that attempts to investigate

operational policies at a hospital that jointly maximize the

clinical outcome benefits and minimize the costs of robot-

assisted surgery.

1.1 |An overview of the operational policies

Operational policies that can improve clinical outcomes and

reduce costs with a new technology such as a surgical robot

are fundamental to the adoption and use of the technology at

a hospital. Toward that end, we discuss below three inter-

related operational policies.

1.1.1 |Patient triaging based on the criticality

of disease condition

Patient triaging is the process of classifying patients based

on the criticality of their disease condition (Ferrand,

Magazine, Rao, & Glass, 2018). Patient triaging has been

used to determine resource allocation in: emergency depart-

ments (Ferrand et al., 2018), surgical care delivery (Sobol &

Wunsch, 2011; Weaver, Litwin, & Martin, 1993), and gen-

eral health care delivery (Sun, Argon, & Ziya, 2017). There-

fore, it is conceivable that a patient triaging policy for

performing robot-assisted surgery informed by the criticality

of the disease condition of a patient is likely to result in

improved clinical outcomes for the patient. In the context of

the surgical procedure of hysterectomy, the relevant measure

of criticality of disease condition for a patient is the uterine

weight of the patient (Louie et al., 2018; Wasson,

Magtibay, & Magrina, 2017). Uterine weight for hysterec-

tomy patients is significantly associated with clinical out-

come measures such as surgical duration, blood loss, and

length-of-stay at a hospital (O'Hanlan, McCutcheon, &

McCutcheon, 2011). Specifically, we propose and test a

threshold based triaging policy, where patients above a

threshold value of uterine weight receive higher priority for

robot-assisted hysterectomy.

The patient triaging policy for prioritizing robot-assisted

surgery requires an understanding of the comparative effec-

tiveness of robot-assisted surgery vis-à-vis manual laparos-

copy by way of clinical outcomes for a surgical procedure

(e.g., hysterectomy). While it is widely acknowledged that,

generally, robot-assisted surgery is associated with superior

clinical outcomes vis-à-vis manual laparoscopy, how the

benefits vary across patients with varying criticality of dis-

ease condition is not addressed in the extant literature. More-

over, a triaging policy for prioritizing surgery patients based

on the criticality of their disease condition is likely to impact

not only the immediate clinical outcome benefits, but also

the improvement in clinical outcomes over time. Chiu et al.

(2015) found that as a surgeon performed robot-assisted hys-

terectomy on patients with high uterine weight, there was a

marked improvement in the clinical outcome benefits to

patients. Analysis of clinical outcome benefits of robot-

assisted surgery vis-à-vis manual laparoscopy for patients

with different criticality of disease condition can provide

actionable insights to inform the usage policy of a surgical

robot at a hospital.

1.1.2 |Optimal size of the surgeon pool for

robot-assisted surgery

The realization of the potential benefits of a surgical robot is

significantly associated with surgeon experience—that is, a

surgeon's learning-by-doing with a robot (Avondstondt,

Wallenstein, D'Adamo, & Ehsanipoor, 2017; Beane, 2019).

The effect of surgeon experience on reducing patient waiting

times, surgical duration, and post-operative length-of-stay is

documented in Venkataraman, Fredendall, Taaffe, Huynh,and

Ritchie (2018). Following Venkataraman et al. (2018), we

228 MUKHERJEE AND SINHA