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J Am Med Inform Assoc doi:10.1136/amiajnl-2012-001293
  • Research and applications

Professional and geographical network effects on healthcare information exchange growth: does proximity really matter?

  1. Gurdev Singh3,4
  1. 1Department of Management Science and Systems, School of Management, University at Buffalo, The State University of New York, Buffalo, NY 14260, USA
  2. 2School of Business, University of Connecticut, Storrs, Connecticut 06269, USA
  3. 3Department of Family Medicine, University at Buffalo, The State University of New York, Buffalo, NY 14203, USA
  4. 4UB Patient Safety Research Center, University at Buffalo, The State University of New York, Buffalo, NY 14203, USA
  1. Correspondence to Niam Yaraghi, Department of Management Science and Systems, SUNY at Buffalo, Buffalo, New York 14260, USA; niamyara{at}buffalo.edu
  • Received 28 August 2012
  • Revised 6 November 2013
  • Accepted 11 November 2013
  • Published Online First 28 November 2013

Abstract

Background and objective We postulate that professional proximity due to common patients and geographical proximity among practice locations are significant factors influencing the adoption of health information exchange (HIE) services by healthcare providers. The objective of this study is to investigate the direct and indirect network effects of these drivers on HIE diffusion.

Design Multi-dimensional scaling and clustering are first used to create different clusters of physicians based on their professional and geographical proximities. Extending the Bass diffusion model to capture direct and indirect network effects among groups, the growth of HIE among these clusters is modeled and studied. The network effects among the clusters are investigated using adoption data over a 3-year period for an HIE based in Western New York.

Measurement HIE adoption parameters—external sources of influence as well as direct and indirect network coefficients—are estimated by the extended version of the Bass diffusion model.

Results Direct network effects caused by common patients among physicians are much more influential on HIE adoption as compared with previously investigated social contagion and external factors. Professional proximity due to common patients does influence adoption decisions; geographical proximity is also influential, but its effect is more on rural than urban physicians.

Conclusions Flow of patients among different groups of physicians is a powerful factor in HIE adoption. Rather than merely following the market trend, physicians appear to be influenced by other physicians with whom they interact with and have common patients.

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