Harry’s guest this week is Tom Davenport, who argues that the healthcare industry is way behind in its use of big-data analytics software to make smarter decisions about business and patient care. “This is a period of lots of opportunity to use new technologies to change healthcare, and God knows we need it, from a value-for-expense standpoint,” Davenport tells Harry. “But we’re not really at the point, at least on the clinical side yet, where we see a lot of direct applications. We’re still in the age of compiling transaction data. We haven’t used it much yet to make decisions and take actions.”
Tom Davenport knows analytics, big data, and AI—he teaches executive courses on the subject at Babson College, Harvard Business School, the Harvard School of Public Health, and the MIT Sloan School of Management, and is widely known for his books on analytics and AI in business, Competing on Analytics (2007), Only Humans Need Apply (2016), and The AI Advantage (2018).
Davenport notes that a number of life science startups are attempting to use machine learning, big data, and AI to reinvent drug discovery (a subject thoroughly covered in previous episodes of MoneyBall Medicine). But in other areas, progress has barely begun. A few startups are trying to bring machine learning into the world of providers and payers, to offer insight-based recommendations about care gaps and treatment. And a few researchers are studying the use of deep learning for pattern recognition in radiology and pathology imaging. But substantive advances are years away.
On the clinical side, Davenport says, “The biggest changes are in the institutions that have more data—combined provider/payer organizations like Geisinger and Kaiser—who absorb the risk of care and need to make informed decisions about it, and are more focused on treating the entire patient and keeping the patient as well as possible. But even there it’s still early days.”
Healthcare organizations that haven’t already started to implement analytics may never catch up, Davenport warns. “This is not an area where it’s going to be successful to take a fast-follower strategy, because it requires so much data, so much learning, and so much trial and error over time.”