Differential Impacts of Technology-Network Structures on Cost Efficiency: Knowledge Spillovers in Healthcare

ABSTRACT

We examine how hospital cost efficiency can improve because of knowledge spillover effects, arising from the experiences of members of a healthcare system. We identify two types of functional technology networks—that we term repository-type and workflow-type networks—which are defined by the adoption patterns of different types of electronic health record (EHR) systems application functions by hospitals in a health system. Additionally, we examine how structural features of these two network types—namely, network centrality and interconnectedness—impact such knowledge spillover effects. By analyzing seven years of panel data for 1,420 U.S. hospitals across 216 health systems, and data obtained from surveys of healthcare experts, we found that greater centrality of nodes in repository-type networks enhances the influence of partners’ experiences on cost efficiency but has the opposite effect in workflow-type networks, while greater interconnectedness of workflow-type networks enhances the influence of partners’ experiences on cost efficiency. Interestingly, we found the opposite effect in repository-type networks, where greater interconnectedness diminishes the influence of partners’ experiences on cost efficiency. We discuss reasons for this surprising and counterintuitive finding. Overall, our results demonstrate that at least in healthcare settings where information technologies enable knowledge-sharing networks to be formed, benefits can accrue through knowledge spillover effects; moreover, variations across functional categories of technologies adopted in a network context can result in different technology-network structures with differential impacts on hospital cost efficiency because of their influence on knowledge spillover at both the hospital and system levels. Not only can these results explain mixed findings in the literature that has been dominated by a focus on cost efficiency outcomes dependent solely on the EHR adoption choices of individual hospitals, but they also provide the basis for guidelines for a network-based perspective of technology adoption. We discuss other possible settings to which our empirical findings can be reasonably generalized, including supply chain technologies and the Internet of Things.

KEYWORDS:

• Healthcare costs
• knowledge networks
• electronic health records
• knowledge spillovers
• knowledge transfer

Acknowledgments

The authors would like to thank Steven Nguyen for his valuable contribution to this research.

Disclosure Statement

No potential conflict of interest was reported by the authors.

Supplementary Information

Supplemental data for this article can be accessed online at https://doi.org/10.1080/07421222.2023.2229126

Notes

¹ We thank the anonymous reviewers who advised us to address these concerns and suggested the need for appropriate robustness checks.

² A detailed description of the robustness checks and the test results are provided in the online supplement.

³ Preliminary analyses revealed that the residual variances of the HLM were unequal across the years, and a serial-autocorrelation residual structure did not fit the empirical data as well. In banded-1 diagonal error structure, all year-specific variances and order-1 lagged year covariances are freely estimated; all others are kept at 0.

Additional information

Notes on contributors

Onyi Nwafor

Onyi Nwafor ([email protected]; corresponding author) is an Assistant Professor in the Department of Information Systems and Supply Chain Management at the Bryan School of Business and Economics, University of North Carolina at Greensboro. She received her Ph.D. from the University of Houston. Dr. Nwafor’s research primarily investigates how healthcare organizations can achieve coordination effectively using technology, incentives, and organizational design. She has investigated operational issues related to care coordination and technology management across a variety of settings that range from healthcare teams within hospitals to accountable care organizations. Her work has appeared in various information systems and health- services journals, such as Healthcare Management Review, International Journal of Information Management, and Social Science and Medicine.

Xiao Ma

Xiao Ma ([email protected]) is an Associate Professor of Business Analytics and Director of the M.S. in Business Analytics Program at the C. T. Bauer College of Business, University of Houston. He holds a Ph.D. in Business from the University of Wisconsin-Madison, concentrating on information systems and analytics. Dr. Ma’s research focuses on online platforms and economics of IS, disruptive IT, impacts of information disclosure on user behavior, and healthcare analytics. His earlier research has focused on online gambling behavior and intervention, participation behavior in online labor and knowledge communities, and social media analytics. His work has appeared in top journals of information systems and management, including Information Systems Research, Journal of Management Information Systems, Journal of the Association for Information Systems, Decision Sciences, and Information & Management.

Norman A. Johnson

Norman A. Johnson is the C. T. Bauer Professor of Business Analytics and Chair of the Department of Decision & Information Sciences in the C. T. Bauer College of Business at the University of Houston. He also holds a joint appointment as a professor in the Hobby School of Public Affairs at the University of Houston. His research interests include healthcare operations, uncertainty and computer-supported decision making, emotional affect in a variety of contexts, psychometric analysis, and data analytics. Norm’s research appears in leading academic journals such as MIS Quarterly, Journal of Management Information Systems, Production and Operations Management, Decision Sciences, Decision Support Systems, Healthcare Management Review, European Journal of Information Systems, and Information Systems Journal.

Rahul Singh

Rahul Singh ([email protected]) is an Associate Professor in the Information Systems and Supply Chain Management Department at the Joseph M. Bryan School of Business and Economics at the University of North Carolina at Greensboro. He holds a Ph.D. in Business Administration from Virginia Commonwealth University. Dr. Singh’s research interests include health analytics and visualization, health IT value, electronic health Records and their impact on clinical and operational efficacy, social media strategies, accessibility and usability of systems and related subjects. His research deploys a variety of methodologies, such as experimental and quasi-experimental designs and design science. His work has been published in such journals as European Journal of Information Systems; Journal of the AIS; IEEE Transactions on Systems, Man and Cybernetics; Communications of the ACM, and others.

Ravi Aron

Ravi Aron ([email protected]) is the Professor of Healthcare Strategy and Technology in the Department of Decision and Information Sciences at the Bauer College of Business, at the University of Houston. His expertise is in the areas of information technology strategy, healthcare strategy and healthcare information systems. He received his Ph.D., from the Leonard N Stern School of Business, at New York University. His research interests include machine learning and its applications, medical supply chains, and technology and innovation in emerging economies. His research has been published in various journals including Management Science, Information Systems Research, Journal of Operations Management, JMIS, and The Harvard Business Review.