As the proportion of facility-based births increases, so does the need to ensure that mothers and their newborns receive quality care. Developing facility-oriented obstetric and neonatal training programs grounded in principles of teamwork utilizing simulation-based training for emergency response is an important strategy for improving the quality care. This study uses 3 dimensions of the Kirkpatrick Model to measure the impact of PRONTO International (PRONTO) simulation-based training as part of the Linda Afya ya Mama na Mtoto (LAMMP, Protect the Health of mother and child) in Kenya. Changes in knowledge of obstetric and neonatal emergency response, self-efficacy, and teamwork were analyzed using longitudinal, fixed-effects, linear regression models. Participants from 26 facilities participated in the training between 2013 and 2014. The results demonstrate improvements in knowledge, self-efficacy, and teamwork self-assessment. When comparing pre-Module I scores with post-training scores, improvements range from 9 to 24 percentage points (p values < .0001 to .026). Compared to baseline, post-Module I and post-Module II (3 months later) scores in these domains were similar. The intervention not only improved participant teamwork skills, obstetric and neonatal knowledge, and self-efficacy but also fostered sustained changes at 3 months. The proportion of facilities achieving self-defined strategic goals was high: 95.8% of the 192 strategic goals. Participants rated the PRONTO intervention as extremely useful, with an overall score of 1.4 out of 5 (1, extremely useful; 5, not at all useful). Evaluation of how these improvements affect maternal and perinatal clinical outcomes is forthcoming.

Introduction: Models for digital triage of sick children at emergency departments of hospitals in resource poor settings have been developed. However, prior to their adoption, external validation should be performed to ensure their generalizability. Methods:  We externally validated a previously published nine-predictor paediatric triage model (SMART Triage) developed in Uganda using data from two hospitals in Kenya. Both discrimination and calibration were assessed, and recalibration was performed by optimizing the intercept for classifying patients into emergency, priority, or non-urgent categories based on low-risk and high-risk thresholds. Results: A total of 2539 patients were eligible at Hospital 1 and 2464 at Hospital 2, and 5003 for both hospitals combined; admission rates were 8.9%, 4.5%, and 6.8%, respectively. The model showed good discrimination, with area under the receiver-operator curve (AUC) of 0.826, 0.784 and 0.821, respectively. The pre-calibrated model at a low-risk threshold of 8% achieved a sensitivity of 93% (95% confidence interval, (CI):89%-96%), 81% (CI:74%-88%), and 89% (CI:85%–92%), respectively, and at a high-risk threshold of 40%, the model achieved a specificity of 86% (CI:84%–87%), 96% (CI:95%-97%), and 91% (CI:90%-92%), respectively. Recalibration improved the graphical fit, but new risk thresholds were required to optimize sensitivity and specificity. Conclusion: The Smart Triage model showed good discrimination on external validation but required recalibration to improve the graphical fit of the calibration plot. There was no change in the order of prioritization of patients following recalibration in the respective triage categories.  Recalibration required new site-specific risk thresholds that may not be needed if prioritization based on rank is all that is required. The Smart Triage model shows promise for wider application for use in triage for sick children in different settings.