Analytics

AI and machine learning can help overcome barriers to social determinants of health, says COO of HSBlox Lynn Carroll. Understanding where to take action requires analysis of datasets and tools integrated into traditional medicine.

Vice president of research science and advanced analytics at Inovalon Christie Teigland describes HEI scores, what will be measured within plans to determine their efforts on improving health equity and HEI's effect on health plans.  

Ann Maxwell, deputy inspector general for evaluations at the U.S. Department of Health and Human Services Office of Inspector General, discusses the case for making the pandemic-related CMS telehealth expansion permanent.  

Helen Waters, EVP and COO at Meditech, explains how the electronic health vendor uses artificial intelligence, its partnership with Google and the system's requirements to implement AI in healthcare.  

Karl Ulfers, cofounder and CEO of DUOS, discusses how the company utilizes AI to match older patient needs to proper resources in order to improve access to care and patients’ understanding of their health benefits.  

Home technology, like cognitive games and reminiscence therapy, can help patients with mild cognitive impairment, but CEO and founder of Neuroglee Aniket Singh Rajput says AI is needed to determine what modalities work best for individuals.  

Professor David Lowe of the Scottish government discusses use cases of AI, including lung cancer detection, and the legal and ethical considerations around adopting AI.

Dr. Adam Landman, chief information officer at Mass General Brigham, relays how generative AI and LLM technology are changing the healthcare workforce burden and how these scalable solutions improve efficiencies within the health system.

Ryan Helon, VP of investment funds at Rev1 Ventures, discusses how the firm plans to invest $30 million in growing life sciences startups within the central Ohio region. He describes the resources it and its partners provide to companies.  

Gill Margolin, CTO of Talkspace, relays how machine learning algorithms have been trained in suicide risk and crisis identification and how the company measures its effectiveness in strengthening the patient/therapist relationship.