Impact of a Pharmacist on Compliance With Hospital Core Measures
Bibliographic record
Abstract
PURPOSE: The role of a pharmacist in achieving compliance with hospital core measures is described. SUMMARY: Core measures for hospitals, also known as quality measures, were introduced by the Department of Health and Human Services as an initiative to improve health care through accountability and public disclosure. Hospitals receive financial incentives for compliance with these core measures, but most importantly, these measures ensure that evidence-based therapy is consistently provided to each patient. If a core measure is not met, documentation must be provided to ensure that there is not a failure to meet the measure. Pharmacists were granted the authority to provide core measure documentation in 2007. There are a total of 44 core measures, 22 (50%) of which are medication related and can be documented by a pharmacist. Over a 5-year period, clinical pharmacists have recorded 1281 interventions for core measure documentation. In an analysis of a 1-year period of charts with missing core measure documentation, pharmacists prevented failure to meet the measure in 96% of the cases. CONCLUSION: Given the great impact that pharmacists can have on hospital core measure compliance, each hospital's Pharmacy Department should evaluate ways to improve involvement in the quality programs at their hospitals.
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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 | 0.027 | 0.196 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".