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Record W1500008462 · doi:10.4212/cjhp.v56i1.408

Implementation of Pharmacist-Initiated Orders: “Pharmacist Suggests”

2003· article· en· W1500008462 on OpenAlexaffvenueabout
Emily Ko, William J. McBride

Bibliographic record

VenueThe Canadian Journal of Hospital Pharmacy · 2003
Typearticle
Languageen
FieldMedicine
TopicPharmaceutical Practices and Patient Outcomes
Canadian institutionsKingston General Hospital
Fundersnot available
KeywordsPharmacistMedicinePharmacyDispensaryDrugClinical pharmacyMedication therapy managementMedical emergencyFamily medicineNursingPharmacology

Abstract

fetched live from OpenAlex

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.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.039
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.069

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.039
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0030.002
Open science0.0020.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0210.006

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.

Opus teacher head0.162
GPT teacher head0.440
Teacher spread0.278 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2003
Admission routes3
Has abstractyes

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