Physician Beliefs about the Meaningful Use of the Electronic Health Record: A Follow-Up Study
Bibliographic record
Abstract
Background There is continuing interest in how physicians are responding to the meaningful use of the electronic health record (EHR) incentive program. However, little research has been done on physician beliefs about the meaningful use of the EHR. Objective This study aims to conduct a follow-up study of physician beliefs about the meaningful use of the EHR. Methods Online survey of physicians at two academic medical centers (AMCs) in the northeast who were participating in the meaningful use of the EHR incentive program and were using an internally developed EHR was conducted. Results Of the 2,033 physicians surveyed, 1,075 completed the survey for an overall response rate of 52.9%. Only one-fifth (20.5%) of the physicians agreed or strongly agreed that meaningful use of the EHR would help them improve quality of care, and only a quarter (25.2%) agreed or strongly agreed that the meaningful use of the EHR would improve the care that their organization delivers. Physician satisfaction with the outpatient EHR was the strongest predictor of self-efficacy with achieving stage 2 of the meaningful use of the EHR incentive program (odds ratio: 2.10, 95% confidence interval: 1.61, 2.75, p < 0.001). Physicians reported more negative beliefs in stage 2 than stage 1 across all belief items. For example, 28.1% agreed or strongly agreed that the meaningful use of the EHR would decrease medical errors in stage 2 as compared with 35.9% in stage 1 (p < 0.001). Conclusion Only one-fifth of the physicians in our study believed that the meaningful use of the EHR would improve quality of care, patient-centeredness of care, or the care they personally provide. Primary care physicians expressed more negative beliefs about the meaningful use of the EHR in stage 2 than in stage 1. These findings show that physicians continue to express negative beliefs about the meaningful use of the EHR. These ongoing negative beliefs are concerning for both implementation and policy.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".