Obstetric Emergency Supply Chain Dynamics and Information Flow Among Obstetric Emergency Supply Chain Employees: Key Informant Interview Study
Notice bibliographique
Résumé
BACKGROUND: For the past several decades, the Ethiopian Ministry of Health has worked to decrease the maternal mortality ratio (MMR)-the number of pregnant women dying per 100,000 live births. However, with the most recently reported MMR of 267, Ethiopia still ranks high in the MMR globally and needs additional interventions to lower the MMR to achieve the sustainable development goal of 70. One factor contributing to the current MMR is the frequent stockouts of critical medications and supplies needed to treat obstetric emergencies. OBJECTIVE: This study describes the obstetric emergency supply chain (OESC) dynamics and information flow in Amhara, Ethiopia, as a crucial first step in closing stockouts and gaps in supply availability. METHODS: Applying qualitative descriptive methodology, the research team performed 17 semistructured interviews with employees of the OESC at the federal, regional, and facility level to describe and gain an understanding of the system in the region, communication flow, and current barriers and facilitators to consistent emergency supply availability. The team performed inductive and deductive analysis and used the "Sociotechnical Model for Studying Health Information Technology in Complex Adaptive Healthcare Systems" to guide the deductive portion. RESULTS: The interviews identified several locations within the OESC where barriers could be addressed to improve overall facility-level readiness, such as gaps in communication about supply needs and availability in health care facilities and regional supply hubs and a lack of data transparency at the facility level. Ordering supplies through the integrated pharmaceutical logistics system was a well-established process and a frequently noted strength. Furthermore, having inventory data in one place was a benefit to pharmacists and supply managers who would need to use the data to determine their historic consumption. The greatest concern related to the workflow and communication of the OESC was an inability to accurately forecast future supply needs. This is a critical issue because inaccurate forecasting can lead to undersupplying and stockouts or oversupplying and waste of medication due to expiration. CONCLUSIONS: As a result of these interviews, we gained a nuanced understanding of the information needs for various levels of the health system to maintain a consistent supply of obstetric emergency resources and ultimately increase maternal survival. This study's findings will inform future work to create customized strategies that increase supply availability in facilities and the region overall, specifically the development of electronic dashboards to increase data availability at the regional and facility levels. Without comprehensive and timely data about the OESC, facilities will continue to remain in the dark about their true readiness to manage basic obstetric emergencies, and the central Ethiopian Pharmaceutical Supply Service and regional hubs will not have the necessary information to provide essential emergency supplies prospectively before stockouts and maternal deaths occur.
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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 | 0,004 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,003 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| 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 ».