Narrative review: status of key performance indicators in contemporary hospital pharmacy practice
Bibliographic record
Abstract
Abstract Aim The aim of this review was to explore the status of key performance indicators ( KPIs ) in Australian hospital pharmacy practice. Data sources For this narrative review, databases ( MEDLINE , PubMed and EBSCO ) were searched for relevant publications within the period from April 1980 to April 2014 using the following search terms: hospital pharmacy, key performance indicators, performance measures, clinical indicators and benchmarking. The inclusion criteria were as follows: full text papers (papers only available as abstracts were discarded) and English language. Reference lists of selected papers were also searched to identify additional literature. Results While there are established competencies, standards and quality use of medicines ( QUM ) indicators for hospital pharmacy in Australia, there are no standardised KPIs relating to the performance and practice of hospital pharmacy. International research has demonstrated that KPIs are valuable tools for measuring pharmacy performance; the need for KPIs is highlighted in research from the UK , USA , Canada, New Zealand and Australia. Particular challenges associated with KPI implementation include: the need for relevance to all stakeholders; difficulties in measuring pharmacists’ activities due to the inherent nature of their work; lack of resources for data collection; limited understanding of KPIs ; and negative attitudes toward KPIs by some pharmacists. Conclusion Before nationally standardised KPIs are introduced into Australian hospital pharmacy practice, attention must be paid to developing relevant measures through careful consultation with all relevant stakeholders, including pharmacists themselves. KPIs should provide relevant results, be easy to measure and highlight the value of hospital pharmacy services in a resource‐friendly manner.
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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 | 0.012 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.004 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| 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".