Implementation of Smart Triage combined with a quality improvement program for children presenting to facilities in Kenya and Uganda: An interrupted time series analysis
Notice bibliographique
Résumé
Abstract Sepsis occurs predominantly in low-middle-income countries. Sub-optimal triage contributes to poor early case recognition and outcomes from sepsis. We evaluated the impact of Smart Triage using improved time to intravenous antimicrobial administration in a multisite interventional study. Smart Triage was implemented (with control sites) in Kenya (February 2021-December 2022) and Uganda (April 2020-April 2022). Children presenting to the outpatient departments with an acute illness were enrolled. A controlled interrupted time series was used to assess the effect on time from arrival at the facility to intravenous antimicrobial administration. Secondary analyses included antimicrobial use, admission rates and mortality ( NCT04304235 ). During the baseline period, the time to antimicrobials decreased significantly in Kenya (132 and 58 minutes) at control and intervention sites, but less in Uganda (3 minutes) at the intervention site. Then, during the implementation period in Kenya, the time to IVA at the intervention site decreased by 98 min (57%, 95% CI 81-114) but increased by 49 min (21%, 95% CI: 23-76) at the control site. In Uganda, the time to IVA initially decreased but was not sustained, and there was no significant difference between intervention and control sites. At the intervention sites, there was a significant reduction in IVA utilization of 47% (Kenya) and 33% (Uganda), a reduction in admission rates of 47% (Kenya) and 33% (Uganda) and a 25% (Kenya) and 75% (Uganda) reduction in mortality rates compared to the baseline period. We showed significant improvements in time to intravenous antibiotics in Kenya but not Uganda, likely due to COVID-19, a short study period and resource constraints. The reduced antimicrobial use and admission and mortality rates are remarkable and welcome benefits but should be interpreted cautiously as these were secondary outcomes. This study underlines the difficulty of implementing technologies and sustaining quality improvement in health systems. Author Summary Implementing the Smart Triage platform and quality improvement program for children in Kenya and Uganda resulted in inconsistent improvements in time to intravenous antimicrobial administration. The time to IVA decreased significantly in Kenya during baseline and reduced further during the intervention while increasing at the control site. In Uganda the time to treatment initially decreased but was not sustained. The treatment times were significantly influenced by the improvements during baseline data collection and multiple external health system factors such as drug shortages, the COVID -19 pandemic, staff shortages and strikes. The dramatic reduction in treatment, admission, and mortality rates should be further investigated.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,006 | 0,012 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».