Doctoral dissertation · 2016
Electronic medical records: resistance factors among clinicians
Even mandated adoption fails if healthcare professionals continue to resist the technology. This study asked why, and measured which factors matter most.
The problem
Adoption was stalling
When this research began, electronic medical records were policy, but not yet practice.
0%
of office-based physicians had even a basic EMR system
0%
had a fully functional EMR
Study model
Five forces of resistance: explore them
Each card is one construct the model tested. Open them to see what it measures and the hypothesis behind it.
A clinician's confidence in their own ability to use computer systems effectively in daily work.
H1: Computer self-efficacy is a significant predictor of clinician resistance to EMR systems.
Together, these five constructs explained 78% of the variability in clinician resistance (R² = 0.78).
Methodology
Quantitative, validated, replicable
Structural equation modeling and ANCOVA in R and SPSS, on a 45-item web survey validated by a Delphi expert panel and a pilot study.
0
Survey responses (n)
0
Survey items
0
Pilot participants
0
Covariates
0%
R² = 0.78
Results
The model explained 78% of the variability in clinician resistance
An unusually strong result for a behavioral model, evidence that resistance to EMR systems is measurable, predictable, and therefore addressable. The findings inform how hospitals plan implementations and how I teach the people who run them.
Where this research went next
The dissertation became two books and an ongoing research agenda in digital health.