Development of a New Electronic Death Certificate and Death Management Module Integrated Into the Health Information System of a Tertiary Hospital in Mali: Implementation Report
Bibliographic record
Abstract
Background: Death certification provides reliable epidemiological data that are essential for public health decision-making. Implementing an electronic death certificate improves data quality, accuracy, and timeliness. Recognizing its importance, we developed and integrated a mortality management module into the hospital "Le Luxembourg," enhancing mortality data collection and utilization. Objective: This implementation aimed to improve the completeness and accuracy of mortality data, accelerate the timeliness of death reporting, and facilitate the downstream use of data to inform public health planning and research. Methods: We began by analyzing the existing infrastructure and organizational setup within the hospital. Through a series of interviews, we identified the needs of all users, enabling the design of a module suited to all levels of operation. We implemented a module comprising multiple functionalities, including certificate editing, validation, storage, mortality statistics generation. It also integrates ICD-10 coding and follows the World Health Organization (WHO) model, while remaining adapted to the hospital's specific context. To ensure optimal usability, we assembled a project team that included a mortality audit committee. After implementation, all users received training and continuous direct technical support was provided. Results: We developed a new death certificate model in line with WHO recommendations. Access to the certificate is secured by a unique username and password. To improve data quality, the certification process involves several validation steps: initial recording, which can be modified when the medical section is not completed by a senior physician; pre-validation by the senior physician and final validation by the mortality audit committee. The chain of morbid events is documented using ICD-10 diagnoses. Beyond the certificate itself, the system also allows for civil registration of the death. Moreover, the module can generate statistics based on multiple criteria. This process takes place with the involvement and active engagement of all stakeholders. Conclusions: We established a unique, secure, WHO-compliant death certificate model that ensures high-quality, easily exploitable, and well-archived data. The experience of this hospital may serve as a foundation for scaling up this model to other healthcare facilities within the country.
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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 | 0.014 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".