Evaluating the Clinical Impact of a Novel Pediatric Emergency Medicine Curriculum on Asthma Outcomes in Belize.

BACKGROUND: Respiratory-related complaints prompt most pediatric visits to Karl Heusner Memorial Hospital Authority's (KHMHA) Emergency Department (ED) in Belize. We developed and taught a novel pediatric respiratory emergencies module for generalist practitioners there. We assessed the curriculum's clinical impact on pediatric asthma emergency management. OBJECTIVE: This study assesses the clinical impact of a pediatric emergency medicine curriculum on management of pediatric asthma emergencies at KHMHA in Belize City, Belize. METHODS: We conducted a randomized chart review of pediatric (aged 2-16 y) visits for asthma-related diagnosis at the KHMHA ED between 2015 and 2018 to assess the training module's clinical impact. Primary outcomes included time to albuterol and steroids. Secondary outcomes included clinical scoring tool (Pediatric Respiratory Assessment Measure [PRAM]) usage, ED length of stay, usage of chest radiography, return visit within 7 days, and hospital admission rates. Kaplan-Meier survival analysis and Cox proportional hazard regression were used. RESULTS: Two hundred eighty-three pediatric asthma-related diagnoses met our inclusion criteria. The patients treated by trained and untrained physician groups were demographically and clinically similar. The time to albuterol was significantly faster in the trained (intervention) group compared with the untrained (control) physician group when evaluating baseline of the group posttraining (P < 0.05). However, the time to steroids did not reach statistical significance posttraining (P = 0.93). The PRAM score utilization significantly increased among both control group and intervention group. The untrained physician group was more likely to use chest radiography or admit patients. The trained physician group had higher return visit rates within 7 days and shorter ED length of stay, but this did not reach statistical significance. CONCLUSIONS: The curriculum positively impacted clinical outcomes leading to earlier albuterol administration, increased PRAM score use, obtaining less chest radiographs, and decreased admission rates. The timeliness of systemic steroid administration was unaffected.

Artificial Intelligence in Global Health -A Framework and Strategy for Adoption and Sustainability.

Artificial Intelligence (AI) applications in medicine have grown considerably in recent years. AI in the forms of Machine Learning, Natural Language Processing, Expert Systems, Planning and Logistics methods, and Image Processing networks provide great analytical aptitude. While AI methods were first conceptualized for radiology, investigations today are established across all medical specialties. The necessity for proper infrastructure, skilled labor, and access to large, well-organized data sets has kept the majority of medical AI applications in higher-income countries. However, critical technological improvements, such as cloud computing and the near-ubiquity of smartphones, have paved the way for use of medical AI applications in resource-poor areas. Global health initiatives (GHI) have already begun to explore ways to leverage medical AI technologies to detect and mitigate public health inequities. For example, AI tools can help optimize vaccine delivery and community healthcare worker routes, thus enabling limited resources to have a maximal impact. Other promising AI tools have demonstrated an ability to: predict burn healing time from smartphone photos; track regions of socioeconomic disparity combined with environmental trends to predict communicable disease outbreaks; and accurately predict pregnancy complications such as birth asphyxia in low resource settings with limited patient clinical data. In this commentary, we discuss the current state of AI-driven GHI and explore relevant lessons from past technology-centered GHI. Additionally, we propose a conceptual framework to guide the development of sustainable strategies for AI-driven GHI, and we outline areas for future research.