he prospect of improved clinical outcomes and more efficient health systems has fueled a rapid rise in the development and evaluation of AI systems over the last decade. Because most AI systems within healthcare are complex interventions designed as clinical decision support systems, rather than autonomous agents, the interactions among the AI systems, their users and the implementation environments are defining components of the AI interventions' overall potential effectiveness. Therefore, bringing AI systems from mathematical performance to clinical utility needs an adapted, stepwise implementation and evaluation pathway, addressing the complexity of this collaboration between two independent forms of intelligence, beyond measures of effectiveness alone 1 . Despite indications that some AI-based algorithms now match the accuracy of human experts within preclinical in silico studies 2 , there

With proper oversight and stakeholder involvement, this model is a potential solution to improve availability of essential medicines in LMICs. These pilots exemplify the feasibility of implementing and scaling up this model in other locations.

The Coronavirus disease 2019 (COVID-19) global pandemic has transformed almost every facet of human society throughout the world. Against an emerging, highly transmissible disease, governments worldwide have implemented non-pharmaceutical interventions (NPIs) to slow the spread of the virus. Examples of such interventions include community actions, such as school closures or restrictions on mass gatherings, individual actions including mask wearing and self-quarantine, and environmental actions such as cleaning public facilities. We present the Worldwide Non-pharmaceutical Interventions Tracker for COVID-19 (WNTRAC), a comprehensive dataset consisting of over 6,000 NPIs implemented worldwide since the start of the pandemic. WNTRAC covers NPIs implemented across 261 countries and territories, and classifies NPIs into a taxonomy of 16 NPI types. NPIs are automatically extracted daily from Wikipedia articles using natural language processing techniques and then manually validated to ensure accuracy and veracity. We hope that the dataset will prove valuable for policymakers, public health leaders, and researchers in modeling and analysis efforts to control the spread of COVID-19.

This study found a high frequency of MRPs among patients with chronic kidney disease receiving care in urban sub-Saharan tertiary hospital settings. The predictors of MRPs among CKD patients in this setting are likely to be multifactorial and include the CKD stage, polypharmacy, and comorbidities.

Objective. To develop a prerequisite elective course to prepare students for an advanced pharmacy practice experience (APPE) in Kenya. Design. The course addressed Kenyan culture, travel preparation, patient care, and disease-state management. Instructional formats used were small-group discussions and lectures, including some Web-based presentations by Kenyan pharmacists on disease states commonly treated in Kenya. Cultural activities include instruction in conversational and medical Kiswahili and reading of a novel related to global health programs. Assessment. Student performance was assessed using written care plans, quizzes, reflection papers, a formulary management exercise, and pre-and post-course assessments. Student feedback on course evaluations indicated that the course was well received and students felt prepared for the APPE. Conclusion. This course offered a unique opportunity for students to learn about pharmacy practice in global health and to apply previously acquired skills in a resource-constrained international setting. It prepares students to actively participate in clinical care activities during an international APPE.