Elsevier

Applied Ergonomics

Volume 97, November 2021, 103517
Applied Ergonomics

Digital technologies: An exploratory study of their role in the resilience of healthcare services

https://doi.org/10.1016/j.apergo.2021.103517Get rights and content

Highlights

  • Role of digital technologies derived from Healthcare 4.0 in resilient performance.

  • Multinational survey conducted with 109 experts, in addition to four interviews.

  • Emergency rooms and intensive care units are the most likely to benefit from H4.0

  • Four H4.0 technologies had the largest impact on the resilience of these two services.

  • The benefits are mostly potential given the current limited use of H4.0 in practice.

Abstract

Descriptions of resilient performance in healthcare services usually emphasize the role of skills and knowledge of caregivers. At the same time, the human factors discipline often frames digital technologies as sources of brittleness. This paper presents an exploratory investigation of the upside of ten digital technologies derived from Healthcare 4.0 (H4.0) in terms of their perceived contribution to six healthcare services and the four abilities of resilient healthcare: monitor, anticipate, respond, and learn. This contribution was assessed through a multinational survey conducted with 109 experts. Emergency rooms (ERs) and intensive care units (ICUs) stood out as the most benefited by H4.0 technologies. That is consistent with the high complexity of those services, which demand resilient performance. Four H4.0 technologies were top ranked regarding their impacts on the resilience of those services. They are further explored in follow-up interviews with ER and ICU professionals from hospitals in emerging and developed economies to collect examples of applications in their routines.

Introduction

Healthcare organizations face three main challenges worldwide: reduction in public investments (OECD, 2019), rise in the demand for high-quality care, and pressure to improve the performance of core processes (Braithwaite et al., 2020). To cope with that, healthcare services (HSs) must balance efficiency and resilient performance, which are usually seen as conflicting states (Rosso and Saurin, 2018). Technology is viewed as a key enabler for managing such trade-off (Thimbleby, 2013). New information and communication technologies derived from the Industry 4.0 movement have been adapted to support healthcare treatments and administrative processes through digitization and interconnection of processes, services, and people (Elhoseny et al., 2018; Lasi et al., 2014). The term Healthcare 4.0 (H4.0) was coined to designate such a technology-driven approach that promotes real-time customization of patient-centered healthcare (Thuemmler and Bai, 2017; Alloghani et al., 2018; Wang et al., 2018a).

As such, H4.0 technologies might influence HSs' complex adaptive nature, which is characterized by uncertainty, diversity, and dynamics (Braithwaite et al., 2020). As part of that nature, HSs display resilient performance, which is the ability to adjust their functioning prior to, during, or following changes and disturbances to sustain required performance under both expected and unexpected conditions (Hollnagel et al., 2013). In fact, the need for resilient HSs has been made dramatically visible during the COVID-19 pandemic, which has stretched those systems' resources to their limit. This system-oriented perspective of resilience is complementary to the more traditional viewpoint adopted in clinical healthcare research and in the analysis of the workload of healthcare professionals, which focuses on the individual level, emphasizing personality traits and other personal attributes useful to cope with adversity, stress, and trauma (Cooper et al., 2020).

However, even studies that take a systems view of resilience in HSs tend to overemphasize the skills, decision-making, and actions carried out by individuals and teams (Wachs et al., 2016; Bergström et al., 2015). The role played by digital technologies is usually either overlooked or analyzed from a socio-technical viewpoint, highlighting how individuals' performance adapts to the opportunities and constraints posed by them (Nemeth et al., 2011). Since misalignments between digital technologies' design and individuals' performance are usually found in those studies (e.g., Nyssen and Blavier, 2013; Meeks et al., 2014; Nakajima et al., 2017), these technologies are more often than not portrayed as sources of brittleness.

According to Hollnagel (2017), four inter-related abilities characterize resilient systems; they are: monitor – know what to look for, focusing on what is critical or can potentially become a threat in the short term; anticipate – know what to expect, predicting threats and opportunities, probable changes, disruptions, pressures, and their consequences; respond – know what to do, reacting to disruptions and disturbances either by implementing planned responses or by adjusting the normal functioning state; and learn – know the facts associated with successes and failures to learn the right lessons from the right experience. These abilities have been used as proxies of resilient performance in healthcare settings such as emergency departments (Chuang et al., 2020) and intensive care units (Alders, 2019). In such studies, resilient performance is viewed as a property at the system level, derived from interactions between the abilities.

This study examines the potential1 role of H4.0 digital technologies as promoters of resilience abilities in HSs. Hollnagel (2017) uses the terms resilience abilities and resilience potentials indistinctly. The rationale is that resilience is an emergent property of complex systems, which cannot be measured directly. What can be measured are the factors that set the stage for resilience – these factors (e.g., the four abilities), if present, create a potential for resilience to play out (Hollnagel, 2017). Furthermore, our study does not include quantitative performance data from HSs, which could offer insight into proxies of resilient performance, such as patients' and caregivers' safety. On the other hand, considering the novelty of H4.0 digital technologies and their consequent limited dissemination so far, this study is timely as its findings might be used proactively by researchers and practitioners. Indeed, human factors knowledge must be ideally and primarily applied at the design stage of work systems (Wilson, 2014) and not only reactively after undesired performance materializes in practice.

We focused on the role played by ten H4.0 digital technologies in the resilient abilities of six HSs. While these ten technologies may be associated with a range of devices and applications, they offer a general and comprehensive framework (see section 2) that is useful for an exploratory investigation of a new research topic such as H4.0. We collected data from 109 experts from emerging and developed economies, whose responses were used as inputs in four correspondence matrices similar to the Quality Function Deployment (QFD) ‘s house of quality (Cohen, 1995). Those matrices related the ten H4.0 digital technologies (in the rows) to the six HSs (in the columns) for each resilient ability. Two sets of information from the survey were used to operationalize the matrices: impact scores of H4.0 digital technologies on each resilience ability and rankings of HSs that reflect how they are impacted by H4.0 digital technologies. The most impacted HSs within each resilience ability and their associated H4.0 digital technologies were further investigated through interviews with healthcare professionals.

Our study contributes to the state-of-the-art by shedding light on the role of H4.0 in supporting HSs through the lens of resilient healthcare. H4.0 implementation is relatively recent (e.g., Wang et al., 2018b; Tortorella et al., 2020; Tortorella et al., 2019), and its implications to resilience are still underexplored. We also contribute to practice by offering insights that may encourage more assertive investments in the digitization of HSs. For that, we point to H4.0 digital technologies most likely to promote positive impact and HSs that could most benefit from their adoption.

Section snippets

Integration of digital technologies into healthcare services

In H4.0 environments, systems are characterized by interconnected digital applications, electronics, and microstructure technologies that create more effective therapeutic models, internal and external services (Sultan, 2014; Yang et al., 2015). The use of information and communication technologies and applications in healthcare treatments positively impacts hospitals' outputs in the short term while promoting incremental changes in administrative and supporting processes in the long run (Das

Method

The proposed method is comprised of four steps: (i) questionnaire development, (ii) data collection and analysis of sample characteristics, (iii) development and analysis of correspondence matrices, and (iv) semi-structured follow-up interviews. These steps are detailed next.

Results

Table 3 shows matrix M. Each row i corresponds to an H4.0 digital technology (i=1,,10), and each column j corresponds to a HS (j=1,,6). Row i entries were obtained averaging the reciprocals of rank positions assigned by survey respondents to HSs in the columns, regarding how they are impacted by the H4.0 digital technology listed in the row. The top two ranked HSs in each row are marked in bold and indicated with superscripts. However, it is worth noting that all technologies were applicable

On the H4.0 technologies

According to the interviewees, the only technology present in the three hospitals was digital platforms for collaborative sharing of patient data and information. In fact, that corresponded to electronic medical records (EMRs), which have been a topic of human factors research, emphasizing usability and potential for human error (Zahabi et al., 2015; Holden, 2011). However, patients in none of the hospitals could upload information independently; their participation in the digital platforms was

Conclusions

This article presented an exploratory investigation of the role of H4.0 digital technologies in the resilience of healthcare services, focusing on the four abilities of resilient systems. We analyzed the results of a survey responded by 109 experts from different countries, which were complemented by interviews with 4 healthcare professionals from institutions located in developed and emerging economies. Our findings contribute to both theory and practice. To the best of our knowledge, ours is

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

References (84)

  • C.B. Rosso et al.

    The joint use of resilience engineering and lean production for work system design: a study in healthcare

    Applied Ergonomics

    (2018)
  • S. Sakr et al.

    Towards a comprehensive data analytics framework for smart healthcare services

    Big Data Research

    (2016)
  • Y.B. Salman et al.

    Icon and user interface design for emergency medical information systems: a case study

    International Journal of Medical Informatics

    (2012)
  • N. Sultan

    Cloud computing for education: a new dawn?

    International Journal of Information Management

    (2010)
  • N. Sultan

    Making use of cloud computing for healthcare provision: opportunities and challenges

    International Journal of Information Management

    (2014)
  • M.Z. Uddin

    A wearable sensor-based activity prediction system to facilitate edge computing in smart healthcare system

    Journal of Parallel and Distributed Computing

    (2019)
  • P. Wachs et al.

    Resilience skills as emergent phenomena: a study of emergency departments in Brazil and the United States

    Applied Ergonomics

    (2016)
  • Y. Wang et al.

    Big data analytics: understanding its capabilities and potential benefits for healthcare organizations

    Technological Forecasting and Social Change

    (2018)
  • Y. Wang et al.

    An integrated big data analytics-enabled transformation model: application to health care

    Information and Management

    (2018)
  • J. Wilson

    Fundamentals of systems ergonomics/human factors

    Applied Ergonomics

    (2014)
  • F. Wu et al.

    A novel mutual authentication scheme with formal proof for smart healthcare systems under global mobility networks notion

    Computers and Electrical Engineering

    (2018)
  • J.J. Yang et al.

    Emerging information technologies for enhanced healthcare

    Computers in Industry

    (2015)
  • Z. Yang et al.

    An IoT-cloud based wearable ECG monitoring system for smart healthcare

    Journal of Medical Systems

    (2016)
  • A.E. Abdelaal et al.

    A multi-camera, multi-view system for training and skill assessment for robot-assisted surgery

    International Journal of Computer Assisted Radiology and Surgery

    (2020)
  • M. Alders

    A Reflective Process for Analysing Organisational Resilience to Improve the Quality of Care

    (2019)
  • M.F. Alhamid

    Investigation of mammograms in the cloud for smart healthcare

    Multimedia Tools and Applications

    (2017)
  • M. Alloghani et al.

    Healthcare services innovations based on the state-of-the-art technology trend industry 4.0

  • M. Almulhim et al.

    A lightweight and secure authentication scheme for IoT based E-health applications

    International Journal of Computer Science and Network Security

    (2019)
  • A. Angelini et al.

    Three-dimension-printed custom-made prosthetic reconstructions: from revision surgery to oncologic reconstructions

    International Orthopaedics

    (2019)
  • J.S. Armstrong et al.

    Estimating non-response bias in mail surveys

    Journal of Marketing Research

    (1977)
  • M.A. Azzawi et al.

    A review on internet of things (IoT) in healthcare

    International Journal of Applied Engineering Research

    (2016)
  • A.K. Barrett

    Healthcare workers' communicative constitution of health information technology (HIT) resilience

    Information Technology & People

    (2021)
  • J. Braithwaite et al.

    The three numbers you need to know about healthcare: the 60-30-10 Challenge

    BMC Medicine

    (2020)
  • L. Catarinucci et al.

    An IoT-aware architecture for smart healthcare systems

    IEEE Internet of Things Journal

    (2015)
  • L. Cohen

    Quality Function Deployment: How to Make QFD Work for You

    (1995)
  • A. Colpani et al.

    3D printing for health & wealth: fabrication of custom-made medical devices through additive manufacturing

  • A.L. Cooper et al.

    Nurse resilience: a concept analysis

    International Journal of Mental Health Nursing

    (2020)
  • S. Das et al.

    The effect of information technology investments in healthcare: a longitudinal study of its lag, duration, and economic value

    IEEE Transactions on Engineering Management

    (2011)
  • H. Demirkan

    A smart healthcare systems framework

    IT Professional

    (2013)
  • R.C. Deo

    Machine learning in medicine

    Circulation

    (2015)
  • L. Dubé et al.

    Rigor in information systems positivist case research: current practices, trends and recommendations

    MIS Quarterly

    (2003)
  • G. Gargiulo et al.

    Wearable dry sensors with bluetooth connection for use in remote patient monitoring systems

    Stud. Health Technol. Inform

    (2010)
  • Cited by (22)

    • Resilience capabilities of healthcare supply chain and supportive digital technologies

      2022, Technology in Society
      Citation Excerpt :

      As such, the commonality in the aforementioned four propositions is their emphasis on the mediating role played by the resilience capabilities, stressing that, without them, DT are likely to have a modest impact on the resilience potentials. Thus, the development of these resilience capabilities must occur hand-in-hand with the implementation of DT, consistently with the view that healthcare 4.0 is socio-technical rather than a purely technical approach [69]. This socio-technical nature is also clear in contextual factors that influence the use of DT in HSC.

    • Healthcare 4.0 digital applications: An empirical study on measures, bundles and patient-centered performance

      2022, Technological Forecasting and Social Change
      Citation Excerpt :

      This study aims to identify the fundamental structure underlying H4.0 digital applications, propose a reliable and valid scale to represent it, and verify its impact on patient-centered performance indicators. For that, we propose a multi-step approach, building on indications from previous studies on the topic (e.g., Tortorella et al., 2020b; Rosa et al., 2021). The steps are described in the following sections.

    • Contributions of Healthcare 4.0 digital applications to the resilience of healthcare organizations during the COVID-19 outbreak

      2022, Technovation
      Citation Excerpt :

      Some works (e.g., Sharma et al., 2016; Aceto et al., 2018; Oueida et al., 2018; Sannino et al., 2018; Munzer et al., 2019; Tortorella et al., 2020a) have examined the impact of H4.0 on the management of healthcare operations, indicating a positive association between H4.0 digital applications' adoption and healthcare performance. Some recent surveys based on expert-opinion (Rosa et al., 2021; Tortorella et al., 2021b) suggested that those technologies are potentially beneficial to resilient performance in hospitals. However, primary data from real-world applications, interpreted in the light of resilience implications, are still scarce, which is understandable given the novelty of those applications.

    View all citing articles on Scopus
    View full text