Evaluation of a digital triage platform in Uganda: A quality improvement initiative to reduce the time to antibiotic administration
Bibliographic record
Abstract
BACKGROUND: Sepsis is the leading cause of death in children under five in low- and middle-income countries. The rapid identification of the sickest children and timely antibiotic administration may improve outcomes. We developed and implemented a digital triage platform to rapidly identify critically ill children to facilitate timely intravenous antibiotic administration. OBJECTIVE: This quality improvement initiative sought to reduce the time to antibiotic administration at a dedicated children's hospital outpatient department in Mbarara, Uganda. INTERVENTION AND STUDY DESIGN: The digital platform consisted of a mobile application that collects clinical signs, symptoms, and vital signs to prioritize children through a combination of emergency triggers and predictive risk algorithms. A computer-based dashboard enabled the prioritization of children by displaying an overview of all children and their triage categories. We evaluated the impact of the digital triage platform over an 11-week pre-implementation phase and an 11-week post-implementation phase. The time from the end of triage to antibiotic administration was compared to evaluate the quality improvement initiative. RESULTS: There was a difference of -11 minutes (95% CI, -16.0 to -6.0; p < 0.001; Mann-Whitney U test) in time to antibiotics, from 51 minutes (IQR, 27.0-94.0) pre-implementation to 44 minutes (IQR, 19.0-74.0) post-implementation. Children prioritized as emergency received the greatest time benefit (-34 minutes; 95% CI, -9.0 to -58.0; p < 0.001; Mann-Whitney U test). The proportion of children who waited more than an hour until antibiotics decreased by 21.4% (p = 0.007). CONCLUSION: A data-driven patient prioritization and continuous feedback for healthcare workers enabled by a digital triage platform led to expedited antibiotic therapy for critically ill children with sepsis. This platform may have a more significant impact in facilities without existing triage processes and prioritization of treatments, as is commonly encountered in low resource settings.
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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 | 0.034 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".