Implementation of a Technology-Based Mobile Obstetric Referral Emergency System (MORES): Qualitative Assessment of Health Workers in Rural Liberia
Notice bibliographique
Résumé
BACKGROUND: Maternal mortality remains a persistent challenge in low- and middle-income countries, where evidence-based interventions of obstetric triage and prehospital communication remain sparse. There is limited implementation evidence for technology-based approaches to improve obstetric care in such contexts. Liberia struggles with maternal mortality, particularly in rural areas where deaths are attributable to delays from absent triage and interfacility communication. We implemented a Mobile Obstetric Referral Emergency System (MORES) in rural Bong County to improve prehospital transfer, health worker attentiveness, and patient care for critical obstetric patients. MORES consisted of triage training and a 2-way, templated WhatsApp communication system to reduce delays among patients transferred from rural health facilities (RHF) to hospitals. OBJECTIVE: This study aimed to examine MORES implementation outcomes of usability, fidelity, effectiveness, sustainability, and scalability, as well as additional impacts on the wider health system. METHODS: A structured case study design interview was developed by Liberian and US experts in obstetric triage. Participants included 62 frontline obstetric health providers including midwives (38/62, 61%), nurses (20/62, 32%), physicians assistants (3/62, 5%), and physicians (1/62, 2%) from 19 RHFs and 2 district hospitals who had used MORES for 1 year. Individual interviews were conducted on MORES implementation outcomes, transcribed, and analyzed in NVivo (version 12; Lumivero) with a team-based coding methodology. Content analysis with a deductive approach examined implementation outcomes of usability, fidelity, effectiveness, sustainability, and scalability, while an inductive approach categorized the unanticipated impacts of MORES on the wider health system. RESULTS: Four domains were identified regarding MORES implementation: Usability and Fidelity, Effectiveness, Sustainability and Scalability, and Health System Impact. All participants perceived MORES to have high usability and fidelity, as the triage and messaging system was implemented as intended for critical obstetric patients (62/62, 100%). For effectiveness, MORES accomplished its intended aims by improving prehospital transfer (57/62, 92%), increasing health worker attentiveness (39/62, 63%), and contributing to improved patient care (34/62, 55%). MORES was perceived as sustainable and scalable (62/62, 100%), particularly if technological barriers (21/62, 34%) and staff training (19/62, 31%) were addressed. MORES impacted the wider health system in unanticipated ways including improved coordination and accountability (55/62, 89%), feedback mechanisms for hospitals and RHFs (48/62, 77%), interprofessional teamwork (21/62, 34%), longitudinal follow-up care (20/62, 32%), creating a record of care delays (17/62, 27%), and electronic health record infrastructure (13/62, 21%). CONCLUSIONS: MORES was perceived to have high usability, fidelity, effectiveness, sustainability, and scalability by frontline obstetric providers in rural Liberia. MORES accomplished the intended aims of improving prehospital transfer, increasing health worker attentiveness, and contributing to improved patient care. Additionally, MORES strengthened the health system through 6 domains which impacted individual and system levels. Future studies should quantitatively evaluate delay and morbidity reductions and strategies for scaling MORES.
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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 | 0,017 | 0,020 |
| 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 | 0,004 | 0,005 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,004 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».