A system dynamics model for the analysis of clinical laboratory productivity

• Author: Eylül Damla Gönül‐Sezer,Zeynep Ocak
• DOI: http://doi.org/10.1111/itor.12522
• Published date: 01 November 2020
• Date: 01 November 2020

Intl. Trans. in Op. Res. 27 (2020) 3144–3166

DOI: 10.1111/itor.12522

INTERNATIONAL

TRANSACTIONS

IN OPERATIONAL

RESEARCH

A system dynamics model for the analysis of clinical

laboratory productivity

Eyl¨

ul Damla G¨

on¨

ul-Sezer,∗and Zeynep Ocak

Department of Industrial and Systems Engineering, YeditepeUniversity, ˙

Istanbul 34758, Turkey

E-mail: eylul.gonul@yeditepe.edu.tr[G ¨

on¨

ul-Sezer]; zocak@yeditepe.edu.tr[Ocak]

Received 31 October 2015; receivedin revised form 22 January 2018; accepted 24 January 2018

Abstract

This study describes the initial stages of the development of a system dynamics (SD) model that will be used

to simulate the dynamics of various factors that impact the productivity of clinical laboratories. Facing a

staff capacity constraint, a clinical laboratory can undertake a number of strategies: (a) hiring new staff, (b)

working overtime, or (c) doing both. In this preliminary research, we use a dynamic model to (a) study the

impact of these strategies on laboratory productivity, including cost control, and (b) simulate the laboratory

human resource utilization process. An SD approach is used for model development because it enables

modelers to understand and discuss complex problems by illuminating the relationships among the variables

involved. We use a laboratory’s test backlogand the test turnaround time as main productivity parameters for

this preliminary study. We run the simulation for six months and study laboratory productivity performance

behavior over this time period.

Keywords:clinical laboratory; healthcare; human resources; simulation; system dynamics

1. Introduction

The main task of a clinical laboratory (CL) is to gather information for medical doctors on bio-

logical samples taken from patients. To deliver reliable and qualified service within specified time

limits, it is important to carry out the correct procedures. An organizationmust consider profitabil-

ity while maintaining service quality. As a result, healthcare managers must deliver high quality

service rapidly, and at an affordable price. They also must continuously evaluate and improve CL

performance by quantifying the satisfaction of patients and other stakeholders while constrained

by limited resources.

Additionally, to satisfy the service requirements of patients, healthcare managers must con-

sider and handle fluctuating demand. Clinical laboratories must perform their services despite any

∗Corresponding author.

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2018 The Authors.

International Transactionsin Operational Research C

2018 International Federation ofOperational Research Societies

Published by John Wiley & Sons Ltd, 9600 Garsington Road, Oxford OX4 2DQ, UK and 350 Main St, Malden, MA02148,

USA.

E. D. G¨

on¨

ul-Sezer and Z. Ocak / Intl. Trans. in Op. Res. 27 (2020) 3144–3166 3145

imbalance of supply (such as CL equipment and labor versus demand [test requests]); this imbal-

ance can give rise to problems associated with quality, productivity, and cost. Because laboratory

services represent the majority of costs in a hospital budget, improving efficiency in a healthcare

organization requires improving these processes (Burke, 2000; Kısak¨

urek, 2010). Such processes

also strongly influence customer satisfaction, which is of fundamental importance (Langlois and

Wallemacq, 2009).

Furthermore, improvements in medicine are accelerated by the invention of new technological

devices. Ongoing developmentsin information technology and electronics have resultedin improve-

ments to test techniques and the equipment used in medical processes. However, these sophisticated

tests and equipment are expensive and result in increased healthcare costs worldwide. This increase

has necessitated searching for new ways to manage budgeting problems. Especially in developing

countries, managing the cost of medical services became difficult due to the high consumption rate

of imported materials such as equipment and one-time consumables (Ninci and Ocakakon, 2004).

Thus, resource management has become a focal point in healthcare. Resource management mainly

aims to control inventory cost, equipment, and human resources issues. Inventory and equipment

costs depend on global marketing. However, firms can effectively manage human resources us-

ing various tactical strategies. This means that budgeting problems in healthcare systems can be

solved by using proper human resources management tactics. These tactics allow decision makers

to improve productivity and control their budgets.

This study aims to construct a system dynamics (SD) model to simulate a CL human resource

capacity utilization process. All dynamic simulation analyses are conducted using STELLARsoft-

ware. In this study, we conduct an introductory research analysis to study the impact of hiring new

staff and/or working overtime on laboratory productivity. We assume that the backlog of required

tests and the test turnaround time (TAT) in a laboratory are the main productivity indicators. We

define TAT as the total elapsed time required to complete a test request, from the time that the test

is received until the time the test result is reported.

The remainder of the paper is organized as follows: in Section 2, we provide a literature review

of related studies on healthcare systems. Next, Section 3 describes the methodology of the study. In

Section 4, we explain the development of the model and discuss the experimental results. Section 5

gives the conclusions.

2. Literat ure review

2.1. Resource management in healthcare

Resource management problems occur in various industrial systems, including manufacturing,

transportation, logistics, supply chain, and material handling. In addition to industrial systems,

healthcare systems also have resource management problems that must be analyzed and improved.

Toovercome the resource problems faced in healthcare systems, industrial engineering methods can

be applied, such as mathematical optimization techniques and discrete event simulation (DES), as

well as dynamic designs such as queuing models, statistical methods, and SD.

van Merode and colleagues (2004) developed a model-based framework for hospitals to control

the capacity fordeter ministic processes. Yenice (2009) developeda resource management program to

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2018 The Authors.

International Transactionsin Operational Research C

2018 International Federation of OperationalResearch Societies