Physician Satisfaction Following Electronic Health Record Adoption in Three Massachusetts Communities
Bibliographic record
Abstract
BACKGROUND: Despite mandates and incentives for electronic health record (EHR) adoption, little is known about factors predicting physicians' satisfaction following EHR implementation. OBJECTIVE: To measure predictors of physician satisfaction following EHR adoption. METHODS: A total of 163 physicians completed a mailed survey before and after EHR implementation through a statewide pilot project in Massachusetts. Multivariable logistic regression identified predictors of physician satisfaction with their current practice situation in 2009 and generalized estimating equations accounted for clustering. RESULTS: The response rate was 77% in 2005 and 68% in 2009. In 2005, prior to EHR adoption, 28% of physicians were very satisfied with their current practice situation compared to 25% in 2009, following EHR adoption (P < .001). In multivariate analysis, physician satisfaction following EHR adoption was correlated with self-reported ease of EHR implementation (adjusted odds ratio [OR] = 5.7, 95% CI 2.1 - 16), resources for practice improvement (adjusted OR = 2.6, 95% CI 1.2 - 6.1), pre-intervention satisfaction (adjusted OR = 4.8, 95% CI 1.5 - 15), and stress (adjusted OR = 5.3, 95% CI 1.1 - 25). Male physicians reported lower satisfaction following EHR adoption (adjusted OR = 0.3, 95% CI 0.2 - 0.6). CONCLUSIONS: Interventions to expand EHR use should consider additional support for practices with fewer resources for improvement and ensure ease of EHR implementation. EHR adoption may be a factor in alleviating physicians' stress. Addressing physicians' satisfaction prior to practice transformation and anticipating greater dissatisfaction among male physicians will be essential to retaining the physician workforce and ensuring the quality of care they deliver.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".