Improving pediatric care in Uganda with a digital platform and quality improvement initiative: A retrospective review of Smart Triage + QI
Bibliographic record
Abstract
OBJECTIVE: This is a retrospective review of the feasibility study and implementation of the Smart Triage and Quality Improvement (QI) initiative at Holy Innocents Children's Hospital (HICH), a dedicated pediatric hospital in Mbarara, Uganda, over a 5-year period. The aim of this QI initiative was to improve triaging rates and the time-to-antimicrobials in HICH's outpatient department (OPD). METHODS: Smart Triage is a risk prediction algorithm and digital platform that enables healthcare workers to triage patients and track treatments effectively. Following the feasibility study, the QI program was implemented in September 2021 using three Plan-Do-Study-Act cycles: 1) Standardize Training, 2) Adjust Workflows, and 3) QI Team Communication. Data sources were triage and hospital reports. Monthly run charts of OPD attendance, acuity of illness, triaging rates, median-time-to-antimicrobials, and mortality rates of admitted patients were created. The trajectories of the variables were assessed using linear regression with time as the explanatory variable. RESULTS: 121,521 children attended HICH OPD from November 2018 to October 2023. The OPD triaging rate increased to 91% by October 2023, with a sustained plateau above 90% since July 2022. There was a significant reduction in the median time-to-antimicrobials during the 5-year period, from 77.6 to 53.6 minutes, with a slope of -0.4 minutes per month (CI: -0.73 to -0.04, p-value: 0.029). The inpatient mortality rate decreased from 5.1% in August 2018 to 2.6% in October 2023, with a significant increase in the number of cases with comparable illness severity. CONCLUSION: The impact of Smart Triage was sustained beyond the end of the feasibility trial and showed sustained improvements in processes such as treatment times and clinical outcomes including a reduction in mortality. HICH's leadership integrated a culture of QI across disciplines and departments, contributing to this initiative's sustainability and impact.
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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 | 0.009 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".