The Growing Gap in Electronic Medical Record Satisfaction Between Clinicians and Information Technology Professionals
Bibliographic record
Abstract
BACKGROUND: With the alarming statistics concerning the quality of national health care, it is hoped that electronic health records (EHRs) will reduce inefficiencies associated with medical delivery and improve patient safety. This study reports the results of a survey that demonstrates a pattern in EHR system implementation that indicates that health-care information technology decisions are based more on the preferences of information technology professionals (ITPs) and hospital administrators than clinicians. METHODS: We present survey data highlighting the growing discrepancy in EHR-related satisfaction between clinicians and ITPs. We conducted a literature search to identify major barriers that must be overcome to achieve optimal EHR benefits. We summarize our recommendations in order to maximize the favorable impact of EHRs on the health-care system. RESULTS: The existing gap in postimplementation EHR satisfaction ratings between ITPs and clinicians reveals an underlying systematic problem. Electronic medical record vendors perceive administrators and ITPs as the "buyers" for many EHR systems, and their needs are given higher priority than those of clinicians. This possibly may lead to the lack of clinically optimized EHRs, with systems often presenting as rigid and standardized with a limited exchange of health information. CONCLUSIONS: EHRs have the potential to become a powerful tool that may improve many processes related to health care, including quality, safety, and economical aspects. The involvement of physicians in every step of the process, from electronic medical record selection to acquisition, implementation, and ongoing optimization, is crucial for enabling the achievement of the medical organization's mission.
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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 | 0.020 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".