Collaborating Authors

Big Data: Healthcare must move beyond the hype


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."

The Value Of Pairing Machine Learning With EMRs


Anne Zieger is veteran healthcare consultant and analyst with 20 years of industry experience. Zieger formerly served as editor-in-chief of 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.

Machine learning will replace human radiologists, pathologists, maybe soon


Artificial intelligence, machine learning and cognitive computing systems will replace a number of human jobs, even those requiring higher education, including doctors. "The numbers suggest that machine learning is happening," Leonard D'Avolio, CEO of Cyft, said at the Big Data & Healthcare Analytics Forum on Monday. "The opportunity has been sensed and the money is flowing." D'Avolio pointed specifically to radiology and pathology as ripe areas for machines to replace humans -- even suggesting that in the future it could become unethical not to do so. "In any part of healthcare where a human is interpreting data or images, when a computer does a better job than a human and costs less, the argument could be made that it would be wrong not to use a computer," D'Avolio said.

Machine Learning in Healthcare Takes Another Step data analytics, technology, readmissions, health plans, population health


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.

The mashup approach: How healthcare can save billions on AI and machine learning


Healthcare is at a two-tined fork: One strip leads to repeating the same mistakes others have already made while the more enlightened rail learns from those instead.