About Keynote Speaker Murray Brozinsky

“We are at an inevitability point in the transformation to personalized health.” Murray Brozinsky
Buckle Up for this cool and exciting look into how nascent but powerful technologies will converge to transform the future business of healthcare from episodic, in-person visits treating the average patient to continuous conversations using data analytics and virtual health to treat the individual person. True personalized health.

Murray’s keynote demonstrates how Artificial Intelligence (AI), Machine Learning (ML), Sensors (IoT), Augmented Reality (AR), and Natural Language Processing (NLP) will individually – and collectively – impact care delivery, cost, and experience. He uses specific cases from personal experience, including how:

– AI will dramatically change care management through automated personalized conversations (advanced chatbots) designed to improve outcomes, cut operating & medical costs, and enable the continuous collection of patient-generated health data (PGHD) – a vastly underutilized data source.

– IoT will be driven not by the active trackers we see today but by invisible, passive sensors leveraging “data exhaust” to monitor a wide spectrum of clinically relevant biological data that can be used to create highly-accurate predictive models and power AI-driven, personalized care plans and communications.

– AR will revolutionize specialist consults by enabling doctors and surgeons to teleport to other locations via hologram and review patient cases and information together, revolutionizing the costly and inconvenient referral process.

– NLP and ML will mine medical records to increasingly automate the risk adjustment process for the risk-bearing entities (increasingly at the point of care), dramatically improving risk stratification for population health and reducing cost.

– AI and ML will be used to help design treatment plans and aid in clinical decision support (CDS) through better matching of evidence to patient cases and incorporating real world evidence (RWE) into the mix, which will make treatment plans more personalized and real-time.

What will the patient experience look like in 2022? How will new health innovations, especially data-driven technologies, change healthcare over the next several years? Health technology guru Murray will fast forward to the year 2022 and share predictions for the future of healthcare – ways in which the practice of medicine will be dramatically different and which technologies will have the most impact on patient care.

Buckle Up for this cool and exciting look into how nascent but powerful technologies will converge to transform the future business of healthcare from episodic, in-person visits treating the average patient to continuous conversations using data analytics and virtual health to treat the individual person – True personalized health.

Murray’s keynote demonstrates how Artificial Intelligence (AI), Machine Learning (ML), Sensors (IoT), Augmented Reality (AR), and Natural Language Processing (NLP) will individually – and collectively – impact care delivery, cost, and experience.  He uses specific cases from personal experience, including how:

~ AI will dramatically change care management through automated personalized conversations (advanced chatbots) designed to improve outcomes, cut operating & medical costs, and enable the continuous collection of patient-generated health data (PGHD) – a vastly underutilized data source.

~ IoT will be driven not by the active trackers we see today but by invisible, passive sensors leveraging “data exhaust” to monitor a wide spectrum of clinically relevant biological data that can be used to create highly-accurate predictive models and power AI-driven, personalized care plans and communications.

~ AR will revolutionize specialist consults by enabling doctors and surgeons to teleport to other locations via hologram and review patient cases and information together, revolutionizing the costly and inconvenient referral process.

 ~ NLP and ML will mine medical records to increasingly automate the risk adjustment process for the risk-bearing entities (increasingly at the point of care), dramatically improving risk stratification for population health and reducing cost.

~ AI and ML will be used to help design treatment plans and aid in clinical decision support (CDS) through better matching of evidence to patient cases and incorporating real world evidence (RWE) into the mix, which will make treatment plans more personalized and real-time.