Factors impacting technology adoption in hospital bed logistics

• Date: 11 February 2019
• Published date: 11 February 2019
• DOI: https://doi.org/10.1108/IJLM-02-2017-0043
• Pages: 195-230
• Author: Diana Cordes Feibert,Peter Jacobsen
• Subject Matter: Management science & operations,Logistics

Factors impacting technology

adoption in hospital bed logistics

Diana Cordes Feibert and Peter Jacobsen

Department of Management Engineering,

Technical University of Denmark, Lyngby, Denmark

Abstract

Purpose –The purpose of this paper is to refine and expand technology adoption theory for a healthcare

logistics setting by combining the technology–organization–environment framework with a business process

management (BPM) perspective. The paper identifies and ranks factors impacting the decision to implement

instances of technologies in healthcare logistics processes.

Design/methodology/approach –A multiple case study is carried out at five Danish hospitals to

investigate the bed logistics process. A combined technology adoption and BPM lens is applied to gain an

understanding of the reasoning behind technology adoption.

Findings –A set of 17 factors impacting the adoption of technologies within healthcare logistics was

identified. The impact factors perceived as most important to the adoption of technologies in healthcare

logistics processes relate to quality, employee work conditions and employee engagement.

Research limitations/implications –This paper seeks to understand how managers can use knowledge

about impact factors to improve processes through technology adoption. The findings of this study provide

insights about the factors impacting the adoption of technologies in healthcare logistics processes. Differences

in perceived importance of factors enable ranking of impact factors, and prioritization of changes to be

implemented. The study is limited to five hospitals, but is expected to be representative of public hospitals in

developed countries and applicable to similar processes.

Originality/value –The study contributes to the empirical research within the field of BPM and technology

adoption in healthcare. Furthermore, the findings of this study enable managers to make an informeddecision

about technology adoption within a healthcare logistics setting.

Keywords Europe, Case study, Information technology, Business process management,

Healthcare logistics

Paper type Case study

Introduction

Healthcare systems around the world face the challenge of rising healthcare costs.

Expectations of high-quality care together with an ageing population and more

sophisticated treatments have led to more expensive healthcare provision (OECD, 2015;

WHO, 2010). Thus, there is an increasing pressure to provide high-quality care at lower

costs. One opportunity for reducing healthcare costs is by addressing logistics expenditure

in hospitals. Logistics activities account for more than 30 percent of hospital costs, half of

which could be eliminated by applying best practices (Aptel et al., 2009; McKone-Sweet et al.,

2005; Poulin, 2003). Main and supporting logistical flows in hospitals therefore hold great

potential for cost reductions.

Hospitals are turning to manufacturing-based supply chain management (SCM) best

practices and business process management (BPM) concepts such as just-in-time ( JIT)

(Aptel and Pourjalali, 2001; Kumar, Ozdamar and Ning Zhang, 2008; Kumar, DeGroot and

Choe, 2008), lean (Hicks et al., 2015; Joosten et al., 2009; Kollberg et al., 2007), total quality

management (TQM) (Chen et al., 2004; Chow-Chua and Goh, 2000), business process

reengineering (BPR) (Bertolini et al., 2011; Elkhuizen et al., 2006; van Lent et al., 2012) and

automation (Granlund and Wiktorsson, 2013; Markin, 1994) in an effort to become more

efficient and effective. However, hospitals are often left to their own experience to decide on

a process design that suits their needs (van Lent et al., 2012). Similarly, whether to

implement a technology is up to each hospital to decide and may differ depending on the

focus areas of the hospital (Xie et al., 2016).

The International Journal of

Logistics Management

Vol. 30 No. 1, 2019

pp. 195-230

© Emerald PublishingLimited

0957-4093

DOI 10.1108/IJLM-02-2017-0043

Received 20 February 2017

Revised 29 May 2018

Accepted 25 July 2018

The current issue and full text archive of this journal is available on Emerald Insight at:

www.emeraldinsight.com/0957-4093.htm

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logistics

Introducing a new technology can significantly impact hospital costs and quality

performance (Li and Benton, 2006) and can free up time for caretakers to perform othertasks

(Bloss, 2011; Li and Benton, 2006). Technology adoption theory aims to predict under which

circumstances a technology is adopted. The technology–organization–environment (TOE)

frameworkidentifies three contextsrelevant to technology adoption,namely the technological,

organizational and environmental contexts (Baker, 2012; Tornatzky and Fleischer, 1990).

Applied in a healthcare logistics setting, the TOE framework may elucidate factors

influencing the decision to adopt technologies to improve healthcare logistics processes.

A new technology will invariably affect the process in which the technology is

implemented and the process design (Attaran, 2003; Karimi et al., 2007). Thus, technologies

can greatly improve the efficiency of processes (Voss, 1988). Conversely, processes need to

be aligned with the introduction of new technologies (Hung, 2006; Trkman, 2010). The TOE

framework enables the assessment of potential technologies to be adopted. However, the

main constructs of the TOE framework have not changed since it was developed in 1990 by

Tornatzky and Fleischer (Baker, 2012), whereas the development in technologies has

increased significantly over the past almost 30 years. Combining different theoretical

models could contribute to a better understanding of technology adoption in organizations

(Oliveira and Martins, 2011).

The TOE framework is somewhat static and fails to consider how the introduction of a

new technology ties in with and affects the process in which the technology will operate, i.e.

the dynamics of introducing a new technology. Consequently, the TOE framework does not

consider how the introduction of a technology will improve or deteriorate the process and

operations of a hospital. Supplementing the TOE framework with a process construct adds

a dynamic perspective for predicting technology adoption in a constantly changing

environment with fast paced technological advancements.

This paper seeks to identify the factors impacting the design of healthcare logistics

processes by investigating why instances of technologies have been implemented in

a hospital bed logistics process. The first research question (RQ) is formulated as follows:

RQ1. Which factors impact the decision to implement instances of technologies in

healthcare logistics processes?

Differences in process design provide different points of departure for introducing a new

technology, both in terms of the extent of changes and the level of improvement increments.

Failure to consider the current state of a process would therefore be to neglect the process

improvement dimension and the benefits gained from a technology. To identify the impact

factors in RQ1, a conceptual framework is developed based on the TOE framework

combined with a BPM perspective.

Organizations operate under different circumstances and the benefits reaped from a

technology therefore differ (Chan et al., 2001). Hence, organizations must select a technology

that best fits their specific needs depending on the context in which the hospital operates.

The factors impacting the decision to adopt a technology may therefore differ in importance

depending on the context, e.g. industry, market segment and corporate strategy. A second

RQ is therefore investigated:

RQ2. How do the identified impact factors differ in terms of importance for the decision

to adopt a technology within healthcare logistics processes?

This study aims to refine and expand technology adoption theory for a healthcare logistics

setting by applying a combined technology adoption and BPM lens to a multiple case study

of the bed logistics process in five Danish hospitals. The study therefore contributes to

technology adoption literature and healthcare logistics literature by providing a set of

factors impacting the decision to adopt technological innovations in a healthcare setting.

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Furthermore, the study illustrates how the process perspective enriches the TOE framework

and in turn how the TOE framework enhances BPM theory.

This paper is organized as follows. A literature review is provided linking technology

adoption to BPM for a healthcare logistics setting. The research method is then described,

following a presentation of the results. Finally, the paper discusses and concludes on the

results of the study.

Literature review

The following literature review covers four areas: logistics processes in healthcare,

technologies in healthcare logistics, technology adoption and TOE, and BPM in healthcare.

The literature review follows a sequence that logically links technology adoption to BPM for

a healthcare logistics setting. By providing this trail of evidence from literature, the key

elements of the study aims, RQs and objectives are covered and the link between them

established. In relation to the RQs, the first section of the literature review provides the

contextual background of the investigated processes. The second and third sections relate to

the technological aspect of the RQs, and the fourth section underpins and further justifies

the importance of the BPM dimension in explaining the adoption of technologies in a

healthcare logistics setting.

Logistics processes in healthcare

Logistics relates to the movement and transmittal of goods, services and information (Lummus

et al., 2001) and is closely related to SCM, which is reflected in th e definition of logistics

management provided by the Council of Supply Chain Managemen t Professionals (2016):

Logistics management is that part of supply chain management that plans, implements, and controls

the efficient, effective forward and reverses flow and storage of goods, services and related information

between the point of origin and the point of consumption in order to meet customers’requirements.

There has been a growing interest in the field of healthcare operations and SCM (Volland

et al., 2017), including the selection and design of the service delivery system (Dobrzykowski

et al., 2014). For example, Spens and Bask (2002) expand a SCM framework by applying the

framework to a blood transfusion supply chain and Narayana et al. (2014) investigate the

factors impacting the reverse pharmaceutical supply chain.

A process-oriented approach to SCM can improve supply chain performance (Aronsson et al.,

2011; Kumar, Ozdamar and Ning Zhang, 2008). Principles such as six sigma ( Jin et al.,2008),lean

(de Souza, 2009), JIT (Jarrett, 1998; Kumar, Ozdamar and Ning Z hang, 2008; Pan and Pokharel,

2007), TQM (Heinbuch, 1995), BPR (Chow-Chua and Goh, 2000; Elkhuizen et al.,2006;Hoet al.,

1999) and cellular operations (Parnaby and Towill, 2009) have therefore been applied to

healthcare logistics processes. However, the extent of the field continues to be limited.

The process investigated in this paper is the bed logistics process, which includes the flow

of beds and the flow of patients. A survey of Dutch hospitalsrevealed that the most prevalent

process management approaches in patient logistics are care pathways and benchmarking,

followed by BPR and lean management. However, half of the survey hospitals had not

achieved their goals(van Lent et al., 2012). This suggests a needfor more research on how to

successfully improve patient and bed logistics from a process perspective.

Beds are a scarce resource and hospitals are faced with both poor bed utilization and bed

shortages (Bekker and Koeleman, 2011; Holm et al., 2013; Schmidt et al., 2013). At the same time,

the growing demand for healthcare resources increases the pressure for better utilization of bed

capacity (Bekker and Koeleman, 2011). A number of constraints in the bed logistics process

contribute to the complexity of managing the process, e.g. single rooms, no mixed-sex rooms,

incompatibility between pathologies and contagiousness. Bed management units must solve

these issues in a context of high uncertainty as treatment outcomes are not fully predictable and

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