Understanding the interaction between physicians and pharmacists as a method of quality improvement.
Bibliographic record
Abstract
90 Background: Pharmacists and pharmacy technicians are essential to safe medication delivery. Filling prescriptions can be time consuming when the prescription is not complete or clear and requires inter-professional communication. To improve the quality and efficiency of dispensing medication we performed an audit of callbacks to physicians in the outpatient pharmacy associated with our cancer center with a focus on oral chemotherapy. Methods: The Princess Margaret is one of the largest cancer centers in the world, with an outpatient pharmacy on the premises that fills over 70,000 prescriptions a year. The pharmacists have access to the hospital’s Health Information System and chemotherapy prescribing system. A Prescription Audit Form was developed to track the mode of oral chemotherapy prescription (handwritten, preprinted, computerized physician order entry (CPOE), verbal) and reason for callback. The form was implemented from February 10th until March 7th, 2014 with coded data collection. The form was incorporated into the normal workflow of the outpatient pharmacy and concurrently used to document pharmacy interactions for billing purposes. Results: A total of 5,546 prescriptions were filled with 1,166 prescriptions for oral chemotherapy. Nine percent of prescriptions for oral chemotherapy required a callback to a physician and accounted for 32% of the total callbacks made to physicians. Of the 1,166 oral chemotherapy prescriptions; 39% were refills, 34% were handwritten, 22% were CPOE, 4% were verbal, 1% were preprinted, and 1% were not documented. The top two reasons for callbacks were; drug interaction (32%) and incorrect dose (24%). In the 9% of cases where the physician was contacted to clarify a prescription, the prescription was changed 43% of the time. Conclusions: Understanding the reasons for callbacks can be used to determine optimal data fields required in oral chemotherapy prescribing. Ensuring that prescriptions are appropriately completed will reduce the number of callbacks to physicians - significantly impacting the workflow and efficiency of the pharmacy. Reducing unnecessary callbacks will allow the pharmacy team to deliver timely, safe and effective patient centered care.
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 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 | 0.085 | 0.144 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".