Leveraging Health Information System Maturity Assessments to Guide Strategic Priorities: Perspectives from African Leaders
Notice bibliographique
Résumé
Abstract Introduction Central to a functional public health system is a strong health information ecosystem and robust data use. Many low-and-middle-income countries (LMICs) face the task of digitizing their health information systems (HIS). For health leaders, deciding what to prioritize when investing in HIS strengthening is central to this daunting challenge. Objectives The study explores how HIS maturity assessment contributes to HIS strengthening, describes the facilitators and barriers to HIS maturity assessments, and how health leaders can prioritize conducting maturity assessments. Methods This descriptive qualitative study employed key informant interviews (KIIs) with fourteen eHealth leaders at national and international levels working or supporting Ministries of Health’s national HIS in LMICs. Results were analyzed using Dedoose Version 9.0 to develop themes based on the health systems’ building blocks as a framework for identifying facilitators and barriers to conducting HIS maturity assessment. Results Participants identified maturity assessments as a critical beginning step to HIS strengthening, showing the system’s performance, and building a baseline response to systematic data quality challenges. Barriers to conducting HIS maturity assessment include lacking collaborators’ buy-in, fragmented vision, low financial/human resources, and overdependence on donor priorities. Non- supportive policies, a lack of execution champions, and an inadequately skilled workforce in conducting maturity assessments or negotiating for their prioritization hinder maturity assessment implementation. Frequently identified facilitators to promoting HIS maturity assessment include multi-stakeholder engagement, understanding the country’s HIS ecosystem, and priorities to appropriately integrate maturity assessment objectives. Recommendations include capacity building in data use and conducting maturity assessments at all health system levels to grow the demand and value of HIS maturity assessments. Conclusion Promoting HIS maturity assessments can help leaders prioritize areas to improve in the HIS ecosystem, making appropriate decisions that steward HIS maturity advancement. Addressing challenges that hinder HIS assessment implementation holds promise to identify a pathway to a strengthened health system. Author Summary Our manuscript specifically spotlights the perspectives of African eHealth leaders, centering voices on the barriers and facilitators to planning and implementing HIS maturity assessments. We demonstrate their perspective on how conducting maturity assessments can inform understanding of gaps to address in the HIS and strategic direction. We detail the leaders’ recommendations for using HIS maturity assessments in strengthening HIS governance and overall health systems for better population health outcomes in LMIC settings.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,049 | 0,038 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,012 | 0,007 |
| Communication savante | 0,008 | 0,008 |
| Science ouverte | 0,002 | 0,010 |
| Intégrité de la recherche | 0,003 | 0,007 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
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 ».