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,PubMedandEBSCO) 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 standardisedKPIsrelating to the performance and practice of hospital pharmacy. International research has demonstrated thatKPIsare valuable tools for measuring pharmacy performance; the need forKPIsis highlighted in research from theUK,USA, Canada, New Zealand and Australia. Particular challenges associated withKPIimplementation 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 ofKPIs; and negative attitudes towardKPIsby some pharmacists. Conclusion Before nationally standardisedKPIsare introduced into Australian hospital pharmacy practice, attention must be paid to developing relevant measures through careful consultation with all relevant stakeholders, including pharmacists themselves.KPIsshould provide relevant results, be easy to measure and highlight the value of hospital pharmacy services in a resource‐friendly manner.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.035 | 0.197 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.016 | 0.018 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| 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".