Smart Drug Order Monitoring: AI Meets Low Code/ No Code - ERP Spain.com - España y América Latina

As the pharma industry confronts the dual challenges of regulatory compliance and the opioid crisis, innovative technological solutions are essential for safeguarding public health and ensuring legal adherence. This webinar explores how the convergence of AI and low code/no code (LCNC) platforms are revolutionizing the monitoring of suspicious orders in wholesale drug distribution. Our focus will be on the practical application of these technologies to enhance the monitoring and control of controlled drug dispensation, for millions of orders processed by a large wholesale pharma distributor. Key takeaways: - Understanding the need for monitoring suspicious orders: Overview of regulatory requirements and compliance. The impact of the opioid crisis and challenges in legacy methods. - Introduction to LCNC platforms: Definition, benefits, and examples. How LCNC democratizes AI and tech development. - Integrating AI with LCNC: Integration of AI capabilities with LCNC platforms. Benefits and case studies of AI-LCNC integration from various industries. - Specialized use cases: Objectives and scope of monitoring suspicious orders in drug distribution. System architecture and workflow from data ingestion to alert generation. - Implementation insights: Implementation process using LCNC tools. Adherence to security and compliance. - AI models and algorithms: Overview of AI models used. Training, validation, and handling large datasets for accuracy. - Real-world results: Key metrics, KPIs achieved, and case studies of detected suspicious orders. Impact on compliance and operational efficiency. - Challenges, lessons learned and future directions: Technical and operational challenges faced and ways to overcome them. Future enhancements, scaling and emerging trends in AI and LCNC. Join us to learn how the synergy of AI and LCNC platforms can transform the monitoring of suspicious orders in drug distribution, ensuring a safe and compliant pharma supply chain.