Performance measurement in healthcare: part I--concepts and trends from a State of the Science Review.
Bibliographic record
Abstract
OBJECTIVE: Performance measurement is touted as an important mechanism for organizational accountability in industrialized countries. This paper describes a systematic review of business and health performance measurement literature to inform a research agenda on healthcare performance measurement. METHODS: A search of the peer-reviewed business and healthcare literature for articles about organizational performance measurement yielded 1,307 abstracts. Multi-rater relevancy ratings, citation checks, expert nominations and quality ratings resulted in 664 articles for review. Key themes were extracted from the papers, followed by multi-reader validation. Information was supplemented with grey literature. RESULTS: The performance literature was diverse and fragmented, and relevant evidence was difficult to locate. Most literature is non-empirical and originates from the United States and the United Kingdom. No agreement on definitions or concepts is evident within or across disciplines. Study quality is not high in either field. Performance measurement arose in public services and business at about the same time. The evolution of thought on performance measurement ranges from unfettered enthusiasm to sober reassessment. CONCLUSIONS: The research base on performance measurement is in its infancy, and evidence to guide practice is sparse. A coherent multidisciplinary research agenda on the topic is needed.
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 | 0.038 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.014 | 0.026 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".