Better Measurement for Performance Improvement in Low‐ and Middle‐Income Countries: The Primary Health Care Performance Initiative (PHCPI) Experience of Conceptual Framework Development and Indicator Selection
Bibliographic record
Abstract
Policy Points: Strengthening accountability through better measurement and reporting is vital to ensure progress in improving quality primary health care (PHC) systems and achieving universal health coverage (UHC). The Primary Health Care Performance Initiative (PHCPI) provides national decision makers and global stakeholders with opportunities to benchmark and accelerate performance improvement through better performance measurement. Results from the initial PHC performance assessments in low- and middle-income countries (LMICs) are helping guide PHC reforms and investments and improve the PHCPI's instruments and indicators. Findings from future assessment activities will further amplify cross-country comparisons and peer learning to improve PHC. New indicators and sources of data are needed to better understand PHC system performance in LMICs. CONTEXT: The Primary Health Care Performance Initiative (PHCPI), a collaboration between the Bill and Melinda Gates Foundation, The World Bank, and the World Health Organization, in partnership with Ariadne Labs and Results for Development, was launched in 2015 with the aim of catalyzing improvements in primary health care (PHC) systems in 135 low- and middle-income countries (LMICs), in order to accelerate progress toward universal health coverage. Through more comprehensive and actionable measurement of quality PHC, the PHCPI stimulates peer learning among LMICs and informs decision makers to guide PHC investments and reforms. Instruments for performance assessment and improvement are in development; to date, a conceptual framework and 2 sets of performance indicators have been released. METHODS: The PHCPI team developed the conceptual framework through literature reviews and consultations with an advisory committee of international experts. We generated 2 sets of performance indicators selected from a literature review of relevant indicators, cross-referenced against indicators available from international sources, and evaluated through 2 separate modified Delphi processes, consisting of online surveys and in-person facilitated discussions with experts. FINDINGS: The PHCPI conceptual framework builds on the current understanding of PHC system performance through an expanded emphasis on the role of service delivery. The first set of performance indicators, 36 Vital Signs, facilitates comparisons across countries and over time. The second set, 56 Diagnostic Indicators, elucidates underlying drivers of performance. Key challenges include a lack of available data for several indicators and a lack of validated indicators for important dimensions of quality PHC. CONCLUSIONS: The availability of data is critical to assessing PHC performance, particularly patient experience and quality of care. The PHCPI will continue to develop and test additional performance assessment instruments, including composite indices and national performance dashboards. Through country engagement, the PHCPI will further refine its instruments and engage with governments to better design and finance primary health care reforms.
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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 | 0.309 | 0.209 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.020 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.021 | 0.015 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.003 | 0.012 |
| 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".