Applying Electronic Medical Records in Health Care: Physicians' Perspective

Appl Clin Inform. 2016 May 11;7(2):341-54. doi: 10.4338/ACI-2015-11-RA-0165. eCollection 2016.

Abstract

Background: In order to fulfill comprehensive interoperability and recognize the electronic medical records (EMRs') benefits, physicians' attitudes toward using and applying EMR must be recognized.

Objectives: The purpose of this study was to present an integrated model of applying EMRs by physicians.

Methods: This was a cross sectional study in which a sample of 330 physicians working in hospitals affiliated to the Tehran University of medical sciences (TUMS) was selected. Physicians' attitudes toward using and accepting EMR in health care have been analyzed by an integrated model of two classical theories i.e. technology acceptance model (TAM) and diffusion of innovation (DOI). The model was tested using an empirical survey. The final model was tested by structural equation modeling (SEM) and represented by Analysis of Moment Structures (AMOS).

Results: The results suggest that the hybrid model explains about 43 percent of the variance of using and accepting of EMRs (R2=0.43). The findings also evidenced that Perceived Usefulness (PU), Perceived Ease of Use (PEOU), Relative Advantage, Compatibility, Complicatedness and Trainability have direct and significant effect on physicians' attitudes toward using and accepting EMRs. But concerning observeability, significant path coefficient was not reported.

Conclusions: The integrated model supplies purposeful intuition for elucidates and anticipates of physicians' behaviors in EMRs adoption. The study identified six relevant factors that affect using and applying EMRs that should be subsequently the major concern of health organizations and health policy makers.

Keywords: Electronic medical record (EMR); diffusion of innovation theory (DOI); physician; structural equation modeling (SEM); technology acceptance model (TAM).

MeSH terms

  • Adult
  • Delivery of Health Care*
  • Electronic Health Records / statistics & numerical data*
  • Female
  • Health Knowledge, Attitudes, Practice
  • Humans
  • Male
  • Middle Aged
  • Physicians / psychology*