Improving the Quality of Clinical Pharmacy Services: A Process to Identify and Capture High-Value “Quality Actions”
Bibliographic record
Abstract
Patients are harmed and resources wasted because of underuse, overuse, and misuse of medications and treatments. To improve health services, governments and other key organizations use quality indicators. These indicators improve quality through 2 main mechanisms. First, the process of developing quality indicators allows standards, targets, and priorities to be set. Second, quality indicators are used to retrospectively measure and report various aspects of care, providing a framework that increases accountability, allows benchmarking, and identifies areas for improvement. Despite the widespread use of quality indicators to improve health services, the pharmacy profession has not widely adopted this concept for quality improvement in the clinical realm. We propose that this concept can be used to redesign the delivery of care. As pharmacists, we need to redefine what we need to be doing, find out whether we are doing it, and then use this information to find areas to improve. Pharmacists cannot identify and manage all of the drugrelated problems that patients experience or are at risk of experiencing. Rather, the goal should be to maximize patient benefit with available resources. Pharmacists need to identify those drug-related problems for which management or prevention would result in the greatest benefit for as many patients as possible. In other words, they need to prioritize. In this article, we define a new concept that we call “quality actions” and describe a process to identify high-value quality actions for specific patient populations. Measurement consists of documenting whether or not a quality action has been considered or performed. This system will allow pharmacists to identify, measure, and report what they should be doing, which is fundamental to achieving improvement. BACKGROUND ON QUALITY INDICATORS AND QUALITY IMPROVEMENT
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".