Performance measurement in healthcare: part II--state of the science findings by stage of the performance measurement process.
Bibliographic record
Abstract
OBJECTIVE: This paper summarizes findings of a comprehensive, systematic review of the peer-reviewed and grey literature on performance measurement according to each stage of the performance measurement process--conceptualization, selection and development, data collection, and reporting and use. It also outlines implications for practice. METHODS: Six hundred sixty-four articles about organizational performance measurement from the health and business literature were reviewed after systematic searches of the literature, multi-rater relevancy ratings, citation checks and expert author nominations. Key themes were extracted and summarized from the most highly rated papers for each performance measurement stage. RESULTS: Despite a virtually universal consensus on the potential benefits of performance measurement, little evidence currently exists to guide practice in healthcare. Issues in conceptualizing systems include strategic alignment and scope. There are debates on the criteria for selecting measures and on the types and quality of measures. Implementation of data collection and analysis systems is complex and costly, and challenges persist in reporting results, preventing unintended effects and putting findings for improvement into action. CONCLUSION: There is a need for further development and refinement of performance measures and measurement systems, with a particular focus on strategies to ensure that performance measurement leads to healthcare improvement.
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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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".