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
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,309 | 0,209 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,011 | 0,020 |
| Études des sciences et des technologies | 0,004 | 0,014 |
| Communication savante | 0,021 | 0,015 |
| Science ouverte | 0,004 | 0,015 |
| Intégrité de la recherche | 0,003 | 0,012 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».