Selecting Performance Indicators and Targets in Health Care: An International Scoping Review and Standardized Process Framework
Bibliographic record
Abstract
Objective: Health care organizations monitor hundreds of performance indicators. It is unclear what processes and criteria organizations use to identify the indicators they use, who is involved in these processes, how performance targets are set, and what the impacts of these processes are. The purpose of this study is to synthesize international approaches to indicator selection and develop a standardized process framework. Methods: Using the PubMed and Web of Science search engines, a scoping review of peer reviewed and grey literature following PRISMA-ScR guidelines was conducted to identify documents describing indicator selection processes used by health systems. English-language papers from 11 countries published from 2010 to 2020 were included. Papers were thematically analyzed to develop a standardized process framework. Results: The review included 33 peer-reviewed papers and 11 grey-literature documents. While there are common practices used in health care to select indicators, no single standardized process framework for indicator selection exists. Arbitrary or incomplete indicator selection processes risk over-measurement, lack of alignment with strategic and operational goals, lack of support by end-users, and paralyzed decision-making ability. By consolidating international practices, we developed the 5-P indicator selection process framework to mitigate process risks and support high-quality indicator selection processes. Conclusion: The 5-P indicator selection process framework consists of five domains and 17 elements, and offers health care agencies a practical structure they can use to design indicator selection processes. The framework also provides researchers with a basis by which the implementation of these processes may be evaluated.
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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 | 0.445 | 0.454 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.011 |
| Bibliometrics | 0.083 | 0.089 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.021 | 0.023 |
| Open science | 0.009 | 0.020 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".