Mind the gap: the potential of alternative health information exchange

Am J Manag Care. 2019 Jan;25(1):32-38.

Abstract

Objectives: To determine the proportion of patient transitions that could be connected through 3 proprietary alternatives to open, community-based health information exchange (HIE): HIE between physicians who are part of the same integrated system, use the same electronic health record (EHR), or use an EHR that participates in an EHR vendor alliance.

Study design: Cross-sectional analysis of Medicare patient transitions and physician EHR adoption and organizational affiliation from SK&A.

Methods: We characterized the percentage of transitions that could be covered by each HIE approach and the degree of redundancy. We then assessed whether coverage opportunities differed by provider type and used multivariate linear regression to estimate the association between physician characteristics and proportion of transitions uncovered by any proprietary approach (ie, requiring an open HIE approach).

Results: Given current EHR adoption and organizational affiliations, 33% of transitions could be covered by proprietary HIE. For the average physician, open methods of HIE would still be needed for 45% of patients treated by other physicians. Physicians who did not use a market-leading EHR, were not members of a large integrated system, and shared patients with a broader network of physicians have the greatest need for open HIE.

Conclusions: Proprietary approaches to HIE do not eliminate the need for open HIE and may further disadvantage providers in small healthcare organizations using less common EHRs. Ongoing support and innovative value creation within open HIE will likely remain necessary to support HIE by independent physicians. Public efforts to promote interoperability should seek to integrate proprietary models with open HIE.

MeSH terms

  • Attitude of Health Personnel
  • Continuity of Patient Care / standards
  • Cross-Sectional Studies
  • Electronic Health Records / standards
  • Health Information Exchange / standards*
  • Humans
  • Linear Models
  • Medicare
  • Specialization
  • United States