Bridging Risk and Innovation: AI Strategies for Safer Patient Care
Artificial Intelligence is changing the future of healthcare, but how do we make sure it’s safe?
This session explores how AI tools like machine learning and natural language processing can help reduce errors, support clinical decisions, and improve patient safety. You’ll walk away with practical strategies for using AI in real-world settings, especially where resources are limited, and learn how to lead innovation without compromising care.
Objectives
Attendees will be able to:
- Describe key domains of AI deployment in healthcare, including clinical decision support, pharmacovigilance, and documentation/monitoring.
- Explain how AI-driven clinical decision support systems—such as alarm filtering, clinical reporting, and adverse drug event detection—enhance error detection and risk stratification.
- Analyze how explainable AI (XAI) models, including tree-based and attention-driven systems, support identification of drug reactions and drug–drug interactions while maintaining clinician trust and regulatory compliance.
- Evaluate the impact of generative and predictive AI systems on documentation efficiency, safety monitoring, and equitable care delivery, particularly in rural or resource-limited settings.
Session Description
Artificial intelligence (AI) applications, spanning machine learning (ML), natural language processing (NLP), and explainable AI (XAI) have demonstrated strong potential to reduce patient harm, streamline operations, and support safer clinical decisions. AI offers scalable, evidence-based interventions well-suited for healthcare safety mission. Key success factors include stakeholder engagement, data infrastructure alignment, ethical XAI deployment, and ongoing evaluation. By uniting multidisciplinary expertise such as public health, informatics, rural care, and patient safety, healthcare institutions can pilot AI innovations that not only reduce errors but also inform state-wide health policy and quality improvement.
This presentation offers practical strategies for improving diagnostic accuracy, medication safety, and system-wide oversight. Emphasizing collaboration, transparency, and ethical deployment, it provides actionable insights for aligning AI innovations with patient’s safety mission to reduce harm and improve care quality across multidisciplinary teams.
Attendees will leave with actionable blueprints to integrate AI into patient-safety initiatives, especially in resource-limited rural settings, strengthening health providers’ leadership in safety and innovation.
Note: This event qualifies for Continuing Education credits (CEs).
