Implementation of Pharmacist-Initiated Orders: “Pharmacist Suggests”
Bibliographic record
Abstract
INTRODUCTION The Kingston General Hospital is a 452-bed teaching hospital with 20 full-time pharmacists providing direct patient care and support services to a wide variety of medical and surgical programs. Drug distribution is accomplished through a centralized unit-dose system. Pharmacists and pharmacy technicians use the pharmacy computer system (RxTFCTM, BDM Information Systems Ltd., Saskatoon, Saskatchewan) to maintain patient medication profiles. Pharmacists have the knowledge and skills to ensure optimal drug therapy. However, delays in implementing drug therapy may be caused by the lack of a mechanism for notifying physicians of pharmacists’ recommendations. Before the implementation of “Pharmacist Suggests” orders at Kingston General Hospital, pharmacists primarily used medication memoranda (medication memos) to communicate nonurgent drug order problems and drug therapy recommendations to physicians. Drug order problems included errors, orders for nonformulary or restricted drugs, and drug alerts (e.g., drug allergy, duplication, or drug interaction). Drug therapy recommendations included recommendations to modify drug therapy or perform additional drug monitoring. The medication memos (Appendix 1) were generated from RxTFCTM in the main dispensary and placed in the physician’s orders section of the patient chart as a permanent record. Physicians were required to review any medication memos and write new drug orders if needed. Approximately 700 memos were generated by pharmacists each month, for which the average resolution rate was 86%. Informal feedback from pharmacists, physicians, and nurses indicated that the recommendations in the memos were not addressed in a consistent and timely fashion. A previous study1 at the same hospital showed that the mean resolution time (± standard deviation) for medication memos was 1.92 ± 1.19 days (range 0 to 13 days) for “clarification” discrepancies (i.e., drug name, dose, route, frequency, duplication, or allergy) and 2.46 ± 2.58 days (range 0 to 15 days) for resolution of nonformulary medication issues. In attempts to have memos and drug-related problems resolved more quickly and efficiently, a decision was taken to implement “Pharmacist Suggests” orders. A “Pharmacist Suggests” order was defined as a conditional order written by a pharmacist in the patient chart according to specific order criteria. Physician cosignature was required for these orders to be processed. The purpose of this report is to describe the approval process and the institution’s experience with the implementation of “Pharmacist Suggests” orders.
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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 | 0.006 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.006 |
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".