The effect of electronic medical record system use on communication between pharmacists and prescribers
Bibliographic record
Abstract
BACKGROUND: The Electronic Medical Record (EMR) is becoming increasingly common in health care settings. Research shows that EMRs have the potential to reduce instances of medication errors and improve communication between pharmacists and prescribers; however, more research is required to demonstrate whether this is true. This study aims to determine the effect of a newly implemented EMR system on communication between pharmacists and primary care clinicians. METHODS: A retrospective chart analysis of primary care EMR data comparing faxed pharmacy communications captured before and after the implementation of an EMR system at an academic family medicine clinic. Communication requests were classified into the following various categories: refill accepted, refill denied, clarification, incorrect dose, interaction, drug insurance/coverage application, new prescription request, supplies request, continued care information, duplicate fax substitution, opioid early release request, confirmation by phone call, and other. RESULTS: The number and percentage of clarification requests, interaction notifications, and incorrect dose notifications were lower after the implementation of the EMR system. The number and percentage of refills accepted and new prescription requests increased after the implementation of the EMR system. CONCLUSION: The implementation of an EMR in an academic family medicine clinic had a significant effect on the volume of communication between pharmacists and prescribers. The amount of clarification requests and incorrect dosing communications decreased after EMR implementation. This suggests that EMRs improve prescribing safety. The increased amount of refills accepted and new prescription requests post EMR implementation suggests that the EMR is capable of changing prescription patterns.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.019 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".