Computer Networking to Enhance Pharmacist-Physician Communication
Bibliographic record
Abstract
Background: The use of technology to enhance communication about patients' drug therapy has been advocated by physician and pharmacist associations in Canada. The objective of this pilot study was to demonstrate the proof of concept for an electronic network linking pharmacists and community family physicians in order to exchange information about patients 65 years of age or older who were taking five or more medications regularly. Methods: Three community family physicians, 40 of their patients, and three community-based pharmacists with whom the patients most frequently filled their prescriptions participated in the study. Pharmacist-physician pairs were connected through a secure dial-up electronic network. Patients met with the pharmacist to review their medications, including over-the-counter (OTC) products, and the pharmacist generated an electronic profile for the physician. The physician could respond electronically to the pharmacist with edits in order to arrive at a consensus profile for each patient and to discuss discrepancies or drug-related problems. Electronic communication was followed for four months, after which interviews were conducted with the pharmacists and physicians. Results: Patients were mostly female (65%) and were 74.9 years of age on average. Physicians and pharmacists accessed the network a total of 144 times and 96 times, for an average duration of 7 minutes and 41 minutes, respectively. Physicians noted the benefit of learning about their patients' OTC medication use and about issues such as lack of compliance. The most common barrier to using the electronic communication system was lack of time. Conclusion: The electronic linkage was found to be useful. Further evaluation of the effectiveness of this communication system is needed. Patients' access to their electronic medication profile should also be considered for future projects of this type.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".