Factors motivating and affecting health information exchange usage

J Am Med Inform Assoc. 2011 Mar-Apr;18(2):143-9. doi: 10.1136/jamia.2010.004812. Epub 2011 Jan 24.


Objective: Health information exchange (HIE) is the process of electronically sharing patient-level information between providers. However, where implemented, reports indicate HIE system usage is low. The aim of this study was to determine the factors associated with different types of HIE usage.

Design: Cross-sectional analysis of clinical data from emergency room encounters included in an operational HIE effort linked to system user logs using crossed random-intercept logistic regression.

Measurements: Independent variables included factors indicative of information needs. System usage was measured as none, basic usage, or a novel pattern of usage.

Results: The system was accessed for 2.3% of all encounters (6142 out of 271,305). Novel usage patterns were more likely for more complex patients. The odds of HIE usage were lower in the face of time constraints. In contrast to expectations, system usage was lower when the patient was unfamiliar to the facility.

Limitations: Because of differences between HIE efforts and the fact that not all types of HIE usage (ie, public health) could be included in the analysis, results are limited in terms of generalizablity.

Conclusions: This study of actual HIE system usage identifies patients and circumstances in which HIE is more likely to be used and factors that are likely to discourage usage. The paper explores the implications of the findings for system redesign, information integration across exchange partners, and for meaningful usage criteria emerging from provisions of the Health Information Technology for Economic & Clinical Health Act.

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • Adolescent
  • Adult
  • Cross-Sectional Studies
  • Delivery of Health Care, Integrated
  • Emergency Service, Hospital
  • Female
  • Humans
  • Information Systems / statistics & numerical data*
  • Logistic Models
  • Male
  • Medical Record Linkage*
  • Middle Aged
  • Practice Patterns, Physicians'*
  • Texas