Implementation of a Technology-Based Mobile Obstetric Referral Emergency System (MORES): Qualitative Assessment of Health Workers in Rural Liberia
Bibliographic record
Abstract
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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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 | 0.017 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".