Physicians' Estimates of Electronic Prescribing's Impact on Patient Safety and Quality of Care
Bibliographic record
Abstract
Background Electronic prescribing (e-prescribing) is a potentially important intervention that can be used to reduce errors. It provides many potential benefits over handwritten medication prescriptions, including standardization, legibility, audit trails, and decision support. Electronic health record (EHR) and e-prescribing systems may greatly enhance communication and improve the quality and safety of care. Objectives Our aim is to investigate physician's opinions about the influence of electronic prescriptions on patient safety and quality of care. Methods This study extends the technology acceptance model to analyze the acceptance of e-prescribing and adds an understanding of what kind of impact the external variables (patient identification and the interoperability of applications) have on physicians' individual work performance (i.e., patient safety and quality of care). The empirical analysis uses data from surveys conducted in 2012 and 2014 in Finland. The participants were physicians, and e-prescribing was the only method that could be used for prescribing medication when these studies were conducted. Results Physicians' perceived usefulness of e-prescribing was significantly associated with patient safety and quality of care. The interoperability of an EHR had a significant effect on both the perceived ease of use and perceived usefulness of e-prescribing. The findings show that experience with an e-prescribing system has a positive effect on participants' perceived ease of use and perceived usefulness of e-prescribing. Conclusion This study highlights potential safety and efficiency benefits associated with integrated health information technology in health care. The perceived usefulness of e-prescribing affected physicians' opinions on patient safety and quality of care.
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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 | 0.008 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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; 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".