Obstetric Emergency Supply Chain Dynamics and Information Flow Among Obstetric Emergency Supply Chain Employees: Key Informant Interview Study
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.013 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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; both teacher heads agree on what is shown here.
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".