Date of Award
Doctor of Philosophy in Information Systems (DISS)
College of Engineering and Computing
The benefits of using electronic medical records (EMRs) have been well documented; however, despite numerous financial benefits and cost reductions being offered by the federal government, some healthcare professionals have been reluctant to implement EMR systems. In fact, prior research provides evidence of failed EMR implementations due to resistance on the part of physicians, nurses, and clinical administrators. In 2010, only 25% of office-based physicians have basic EMR systems and only 10% have fully functional systems. One of the hindrances believed to be responsible for the slow implementation rates of EMR systems is resistance from healthcare professionals not truly convinced that the system could be of substantive use to them.
This study used quantitative methods to measure the relationships between six constructs, namely computer self-efficacy (CSE), perceived complexity (PC), attitude toward EMR (ATE), peer pressure (PP), anxiety (AXY), and resistance to use of technology (RES), are predominantly found in the literature with mixed results. Moreover, they may play a significant role in exposing the source of resistance that exists amongst American healthcare professionals when using Electronic Medical Records (EMR) Systems. This study also measured four covariates: age, role in healthcare, years in healthcare, gender, and years of computer use. This study used Structural Equation Modeling (SEM) and an analysis of covariance (ANCOVA) to address the research hypotheses proposed. The survey instrument was based on existing construct measures that have been previously validated in literature, however, not in a single model. Thus, construct validity and reliability was done with the help of subject matter experts (SMEs) using the Delphi method. Moreover, a pilot study of 20 participants was conducted before the full data collection was done, where some minor adjustments to the instrument were made. The analysis consisted of SEM using the R software and programming language.
A Web-based survey instrument consisting of 45 items was used to assess the six constructs and demographics data. The data was collected from healthcare professionals across the United States. After data cleaning, 258 responses were found to be viable for further analysis. Resistance to EMR Systems amongst healthcare professionals was examined through the utilization of a quantitative methodology and a cross-sectional research measuring the self-report survey responses of medical professionals. The analysis found that the overall R2 after the SEM was performed, the model had an overall R2 of 0.78, which indicated that 78% variability in RES could be accounted by CSE, PC, ATE, PP, and AXY. The SEM analysis of AXY and RES illustrated a path that was highly significant (β= 0.87, p < .001), while the other constructs impact on RES were not significant. No covariates, besides years of computer use, were found to show any significance differences.
This research study has numerous implications for practice and research. The identification of significant predictors of resistance can assist healthcare administrators and EMR system vendors to develop ways to improve the design of the system. This study results also help identify other aspects of EMR system implementation and use that will reduce resistance by healthcare professionals. From a research perspective, the identification of specific attitudinal, demographic, professional, or knowledge-related predictors of reference through the SEM and ANCOVA could provide future researchers with an indication of where to focus additional research attention in order to obtain more precise knowledge about the roots of physician resistance to using EMR systems.
Emmanuel Patrick Bazile. 2016. Electronic Medical Records (EMR): An Empirical Testing of Factors Contributing to Healthcare Professionals’ Resistance to Use EMR Systems. Doctoral dissertation. Nova Southeastern University. Retrieved from NSUWorks, College of Engineering and Computing. (964)
Databases and Information Systems Commons, Health and Medical Administration Commons, Health Information Technology Commons, Health Services Research Commons, Medicine and Health Commons, Other Computer Sciences Commons, Quantitative, Qualitative, Comparative, and Historical Methodologies Commons