During a recent Digital Health Conference sponsored by the New York eHealth Collaborative, Martin Kohn, M.D., chief medical scientist at IBM, and Pat Skarulis, CIO at Memorial Sloan-Kettering Cancer Center (MSKCC) in New York, outlined a joint venture to use the Watson supercomputer's big data capabilities to help oncologists provide better care for MSKCC patients.
Kohn pointed out that Watson isn't just a "search engine on steroids," or even a massive database. It relies on parallel probabilistic algorithms to analyze millions of pages of unstructured text in patient records and the medical literature to locate the most relevant answers to diagnostic and treatment-related questions.
Ninety percent of the world's data has been created in the last two years, and 80% of that data is unstructured. As any clinician with a pile of unread medical journals knows, that massive collection of information includes far too many papers for any one human to read.
Watson reads it for them at lightening fast speed.
With the help of natural language processing (NLP), the computer not only pulls out relevant terms to match the search terms in a clinician's query, but it also understands the idioms and other idiosyncratic expressions in the English language. And with the help of temporal, statistical paraphrasing and geospatial algorithms, it finds meaningful relationships between the clinician's question and its massive collection of medical facts and theories.
MSKCC decided to collaborate with IBM to "build an intelligence engine to provide specific diagnostic test and treatment recommendations," Skarulis said. The two organizations now are combining data from MSKCC's massive database, called Darwin, with all of Watson's NLP capabilities. IBM is using all of the medical center's structured patient data and its NLP tools to convert the medical center's free text consultation notes into usable data. Skarulis hopes to launch a pilot shortly that will allow the supercomputer to work on real medical cases.