Making performance indicators work: Studies exploring the actionability of healthcare performance indicators applied to primary health care and COVID-19 decision-making
Bibliographic record
Abstract
As healthcare systems become more data-rich and technology-enabled, the importance to harness this potential or risk the consequences of performance measurement that fails to add value, has put a spotlight on the use of healthcare performance indicators that are actionable. Despite the clarity with which actionability has been prioritised, its conceptualisation, and how it can be translated into practice within and across healthcare systems, still remains ambiguous. This thesis was guided by the overarching aim to explore actionability and its constructs of fitness for purpose and fitness for use both conceptually and in practice. In Part I, a more nuanced understanding of fitness for purpose and use is pursued. Parts II and Parts III investigate real-world applications of healthcare performance indicators in the context of primary health care (PHC) and the COVID-19 pandemic. The results of this research highlight a range of resources for strengthening actionability, including a framework for measuring PHC across countries, considerations for using different primary care data sources, features for optimising dashboards, and lessons about the process of dashboard development that could apply both in a pandemic context and beyond. Ultimately, the findings call for the continued prioritisation of measurement, governance and management, and the use of measurement in practice, for performance indicators that work.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".