Using quality improvement frameworks to develop, implement, and evaluate a novel ambulatory oncology pharmacy practice model: A descriptive example
Bibliographic record
Abstract
PURPOSE: To describe the application of the Plan-Do-Study-Act quality improvement framework in the development, implementation, and evaluation of a novel pharmacy practice model in ambulatory oncology. SUMMARY: Four iterations of the Plan-Do-Study-Act framework were completed to develop a patient-facing, pharmacist-led ambulatory oncology clinic program. The clinic provided care to patients with prostate cancer on oral anticancer therapy. Metrics were collected throughout all stages of development to inform target processes for improvement. The pharmacist saw 136 patients between July 2019 and January 2023, resulting in 464 total encounters. The pharmacist provided clinical interventions and counseling to patients newly starting on oral anticancer therapy and those established on therapy using a longitudinal model of care. CONCLUSION: Application of the Plan-Do-Study-Act quality improvement framework to a novel pharmacy practice model supported the development, evaluation, and sustainability of a pharmacist-led ambulatory oncology clinic providing care to patients with prostate cancer on oral anticancer therapy.
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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 | 0.049 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".