A Comparison of Physician Pre-Adoption and Adoption Views on Electronic Health Records in Canadian Medical Practices
Bibliographic record
Abstract
BACKGROUND: There is a major campaign involving large expenditures of public money to increase the adoption rate of electronic health record (EHR) systems in Canada. To maximize the chances of success in this effort, physician views on EHRs must be addressed, since user perceptions are key to successful implementation of technology innovations. OBJECTIVE: We propose a theoretical model comprising behavioral factors either favoring or against EHR adoption and use in Canadian medical practices, from the physicians' point of view. EHR perceptions of physicians already using EHR systems are compared with those not using one, through the lens of this model. METHODS: We conducted an online cross-sectional survey in both English and French among medical practitioners across Canada. Data were collected both from physicians using EHRs and those not using EHRs, and analyzed with structural equation modeling (SEM) techniques. RESULTS: We collected 119 responses from EHR users and 100 from nonusers, resulting in 2 valid samples of 102 and 83 participants, respectively. The theoretical adoption model explained 55.8% of the variance in behavioral intention to continue using EHRs for physicians already using them, and 66.8% of the variance in nonuser intention to adopt such systems. Perception of ease of use was found to be the strongest motivator for EHR users (total effect .525), while perceptions of usefulness and of ease of use were the key determinants for nonusers (total effect .538 and .519, respectively) to adopt the system. Users see perceived overall risk associated with EHR adoption as a major obstacle (total effect -.371), while nonusers perceive risk only as a weak indirect demotivator. Of the 13 paths of the SEM model, 5 showed significant differences between the 2 samples (at the .05 level): general doubts about using the system (P = .02), the necessity for the system to be relevant for their job (P < .001), and the necessity for the system to be useful (P = .049) are more important for EHR nonusers than for users, while perceptions of overall obstacles to adoption (P = .03) and system ease of use (P = .042) count more for EHR users than for nonusers. CONCLUSIONS: Relatively few differences in perceptions about EHR system adoption and use exist between physicians already using such systems and those not yet using the systems. To maximize the chances of success for new EHR implementations from a behavioral point of view, general doubts about the rationale for such systems must be mitigated through improving design, stressing how EHRs are relevant to physician jobs, and providing substantiating evidence that EHRs are easier to use and more effective than nonusers might expect.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.051 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.009 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".