Developing key performance indicators for prescription medication systems
Bibliographic record
Abstract
OBJECTIVE: To develop key performance indicators that evaluate the effectiveness of a prescription medication system. METHODS: A modified RAND/UCLA appropriateness method was used to develop key performance indicators (KPIs) for a prescription medication system. A broad list of potential KPIs was compiled. A multidisciplinary group composed of 21 experts rated the potential KPIs. A face-to-face meeting was held following the first rating exercise to discuss each potential KPI individually. The expert panel undertook a final rating of KPIs. The final set of KPIs were those indicators where at least 80 percent of experts rated the indicator highly i.e. rating of ≥ 7 on a scale from 1 to 9. RESULTS: 292 KPIs were identified from the published literature. After removing duplicates and combining similar indicators 71 KPIs were included. The final ranking resulted in six indicators being ranked 7 or higher by 80% of the respondents and an additional seven indicators being ranked 7 or higher by ≥70 but ≤80% of respondents. The six selected indicators include four specific disease areas, measure structural and process aspects of health service delivery, and assessed three of the domains of healthcare quality: efficiency, effectiveness, and safety. CONCLUSIONS: These indicators are recommended as a starting point to assess the current performance of prescription medication systems. Consideration should be given to developing indicators in additional disease areas as well as indicators that measure the domains of timeliness and patient-centeredness. Future work should focus on the feasibility of measuring these indicators.
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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 | 0.053 | 0.163 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.031 | 0.022 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".