The hype surrounding so-called Big Data – the computational analysis of vast data sets to uncover patterns, trends and associations – is "bi-polar." That's how Leonard D'Avolio, an assistant professor at Harvard Medical School, describes all the chatter around this technology. "Either we are reading about how Big Data will cure cancer or about how it's foolish to believe Big Data will replace doctors," D'Avolio said. "I think the narrative should be in the middle, where we are talking about these technologies as tools that could be used to complement the work of not just clinicians but also healthcare administrators, operational leaders and others. Big Data is another set of technologies with pros and cons."
Machine learning has proven it can beat traditional human techniques in healthcare for some time now, yet it remains limited in use in the healthcare industry. But that may be about to change. "Machine learning is changing everything -- except maybe healthcare," MIT professor John Guttag said here at the Big Data and Healthcare Analytics Forum on Oct. 24. While machine learning drives products and services such as Google Maps, many websites' tracking of shopping habits and presenting options, banking, credit card companies and others, healthcare providers have done much less with the existing technologies. "There's lots of talk, but very little action, very little progress in healthcare," Guttag said.
Anne Zieger is veteran healthcare consultant and analyst with 20 years of industry experience. Zieger formerly served as editor-in-chief of FierceHealthcare.com and her commentaries have appeared in dozens of international business publications, including Forbes, Business Week and Information Week. She has also contributed content to hundreds of healthcare and health IT organizations, including several Fortune 500 companies. Contact her at @ziegerhealth on Twitter or visit her site at Zieger Healthcare. According to Leonard D'Avolio, the healthcare industry has tools at its disposal, known variously as AI, big data, machine learning, data mining and cognitive computing, which can turn the EMR into a platform which supports next-gen value-based care.
Get ready for the next wave of predictive analytics, capable of identifying future admissions and health plan disenrollments. Until recently, many of the machine learning applications talked about for healthcare had been used to teach computing systems enough to be able to suggest a diagnosis on a specific disease. It essentially sent Watson to medical school. IBM had Watson ingest large amounts of medical literature to learn everything physicians are taught about patients' conditions, and then taught it to make diagnoses. But a Harvard professor who leads a startup supplying machine learning technology to Senior Whole Health, a Medicaid managed care organization active in New York state and Massachusetts, says that machine learning will eventually power all technologies we know today as predictive analytics and population health.
When we asked dozens of venture capitalists where they see the most potential for applied artificial intelligence, they unanimously agreed on healthcare. Technology has already been used to incrementally improve patient medical records, care delivery, diagnostic accuracy, and drug development, but with A.I. we could achieve exponential breakthroughs. Deep learning first caught the media's attention when a team from the lab of Geoffrey Hinton at the University of Toronto won a Merck drug discovery competition despite having no experience with molecular biology and pharmaceutical development. Recently, a multidisciplinary research team at Stanford's School of Medicine comprised of pathologists, biomedical engineers, geneticists, and computer scientists developed deep learning algorithms that diagnose lung cancer more accurately than human pathologists. The ultimate dream in healthcare is to eradicate disease entirely.