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.