Bala G. Nair, PhD

Director and Associate Professor, University of Washington

Dr.Nair is the director of the Center for Perioperative & Pain Initiatives in Quality Safety & Outcome (PPiQSO) at University of Washington and holds an associate professor appointment in the Department of Anesthesiology & Pain Medicine. He is also the Chief Solution Architect and technology advisor for Perimatics LLC. Dr.Nair brings over 20 years of considerable expertise in perioperative informatics, clinical decision support and medical devices from two world-class institutions – The Cleveland Clinic and the University of Washington. Dr.Nair is the inventor of the Smart Anesthesia Manager (SAM) decision support software that has improved care for over 500,000 surgery patients in multiple hospitals. In his previous appointment at the Cleveland Clinic, he developed their anesthesia information management system that was showcased to President George W Bush. Dr.Nair has over 50 publications in peer-reviewed journals, holds 7 patents and has been an invited speaker on perioperative decision support in multiple national and international conferences.

Speaker Sessions

14 – Using a Real-Time Data Science Platform to Drive Perioperative Quality and Efficiency (analyst, AI, clinical)

Perioperative care for surgery patients contribute to more than 50% of a hospital’s revenue. However, due to the complexity and dynamic nature of the perioperative environment, delivering optimal and efficient care is challenging. Real-time perioperative data science platforms support a wide ecosystem of data and technology solutions including real-time decision support, machine learning predictive models, and robotic business intelligence. Learn about solutions built on these platforms that have: delivered a return on investment (ROI) of $1.2 million per 10,000 surgeries in terms of quality of care using the real-time decision support and guidance system in over 400,000 surgeries; improved efficiencies, resulting in a potential to reduce overage by $263,000 using machine learning models to predict operating room and post-anesthesia care unit occupancy times; reduced surgical supplies cost by $565,000 using robotic business intelligence algorithms.

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