Reducing Misdiagnosis through Artificial Intelligence Predictive Algorithm, Deployed by a Specialist Care Coordination and Communication Portal

Jeremy Kagan Israel

Data consistently shows that medical errors and health-care related adverse events occur in 8% to 12% of hospitalizations and that misdiagnosis of chronic conditions is one of the leading causes of death. Much of this staggering problem is due to the the sad fact that critical patient and diagnostic information flows neither consistently, nor timely, between specialties and institutions- in even the most technologically advanced countries. To make matters worse, chronic disease patients often have multiple concurrent conditions and complications that require different specialists, while the needs of aging populations add complexity to their care: Diverse patients need multiple and varied care specialties, each with its own clinical workflow.

This talk presents a solution that is based on our new software technology paradigm which utilizes social networking concepts and secure data access to improve specialist workflow and communication. The author is developing artificial intelligence (AI) predictive diagnosis algorithms to study this technology driven data which include patient history, measurements, doctor’s reports, and outcomes and compare to each specialist’s diagnosis patterns. This application will then recommend treatment alternatives, identify patients who are at high risk for other conditions than those tested for, identify high-relevance candidates for drug/treatment trials, and identify areas for research on cause/effect based on patient commonalities in history and outcomes.

This talk will present the status of these AI algorithms that reduce medical error, wrong treatment, and death due to misdiagnosis of chronic conditions, and the candidate requirements of physicians to do trials and use this tool effectively.









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