AI in Healthcare, an Insider's Account (with Arnaub Chatterjee)
Arnaub Chatterjee, a Senior Expert and Associate Partner in the Pharmaceutical and Medical Products group at McKinsey & Company, discusses cutting through the hype about artificial intelligence (AI) and machine learning (ML) in healthcare.
About Arnaub Chatterjee
Arnaub is a Senior Expert/Associate Partner in the Pharmaceutical and Medical Products group at McKinsey & Company. Additionally, he serves as Teaching Associate in the Department of Health Care Policy at Harvard Medical School and Lecturer in the Department of Policy Analysis and Management at Cornell University. At McKinsey, he advises pharmaceutical and biotech companies on a range of topics, including digital transformation, utilizing novel data and analytics for product positioning and preparing for launch. He also works with leading technology companies and startups who are entering the healthcare sector on a range of market strategy and entry issues.
Prior to McKinsey, he served as Director within Merck's Data Science and Insights group where he led ventures and partnerships with academic medical centers, payers and technology companies to power R&D and commercial activities. He previously served in the Obama Administration as an advisor to former Chief Technology Officers Todd Park and Bryan Sivak at the U.S. Department of Health and Human Services (HHS). At HHS, he co-led efforts for the Health Data Initiative, designed the www.healthdata.gov platform, and launched the Innovation Fellows Program. At HHS, he also worked in the Secretary's Office as a lead policy analyst on healthcare fraud and abuse policy initiatives around the Affordable Care Act. Prior to government service, he spent a number of years as a strategy consultant at Deloitte Consulting, supporting pharmaceutical companies on M&A strategy.
He holds graduate degrees in health administration (MHA) and public administration (MPA) from Cornell University and received his undergraduate degree from the University of Michigan.
Links from the show
FROM THE INTERVIEW
- McKinsey Analytics on Twitter
- Hot off the press article for HBR’s Future of Healthcare online forum (By Arnaub Chatterjee)
- Our latest piece on the promise & challenge of AI (By James Manyika and Jacques Bughin)
- Are robots coming for our jobs? (mckinsey.com)
- Analytics Careers page (mckinsey.com)
- How we help clients in healthcare analytics (mckinsey.com)
- AI analysis of 400+ use cases, including ones in healthcare (By Michael Chui et al. mckinsey.com)
- McKinsey’s Opioids Insights (mckinsey.com)
- An Executive’s Guide to AI (mckinsey.com)
- Compendium of AI thought leadership (mckinsey.com)
FROM THE SEGMENTS
Machines that Multi-task (with Manny Moss)
Part 1 at ~21:05
- Responsible AI in Consumer Enterprise
- Hilary Mason, DJ Patil and Mike Loukides on Data Ethics
- EthicalOS Tookit
Part 2 at ~40:00
- 21 Definitions of Fairness Tutorial from FAT* (Arvind Naranayan)
- Kate Crawford's keynote address "The Trouble with Bias" from NIPS 2017
- The (im)possibility of Fairness (Sorelle et al. arXiv.org)
- Learning from disparate data sources (Li Y et al. PubMed.gov)
- Distributed Multi-task Learning (Liyang Xie et al. KDD.org)
- The Cost of Fairness in Binary Classification (Aditya Krishna Menon et al. proceedings.mlr.press)
Original music and sounds by The Sticks.