And the next step after that would be diagnosis: "Hey doctor, have you considered A, B or C, and here's some evidence that would suggest those possibilities." And of course there's the potential for the computer to provide treatment options... I'm hoping to do some work to help push the envelope with the IBM implementation of the software and move it further away from being just a question and answering device, and further away from being just a super-focused medical textbook, but to also have it start to do these tasks related to synthesis, vigilance and surveillance and then ultimately diagnosis and treatment.

You mentioned earlier the billions of transactions recorded in the VA's archives. What's the big obstacle of getting that knowledge to Watson - scrubbing the identifying information?

Scrubbing identifying information from the data is a huge and critically important issue. We have spent weeks just scrubbing data from 10 patients, having to check, recheck, triple check, because you can scrub the name of the file, but somewhere hidden in a progress note, someone refers to Mr. McGillicutty, or Mr. McGillicutty's next door neighbor Mr. Jones. There's a lot of identifying information in unstructured data such as progress notes or discharge summaries that's difficult to scrub. Our recommendation to IBM is to consider bringing the technology in house. So once it's inside the VA's firewall, then it just becomes another information system that's mining data, so you don't have to deidentify the data if it's just essentially mining the data in order to be able to answer your questions.

Is that going on now?

We're in the process of getting an IRB, investigational review board approval, for being able to mine those data. We're applying for that at the University of Maryland. We've already talked with the Vinci team who already do really impressive natural language processing for health services research and development, using some other software developed by IBM that does NLP. The VA's Vinci developers and experts are really enthusiastic about the potential of this rich database; and systems that are fast and sophisticated enough to extract the data in real-time for patient decision-making would be a tremendous addition to the current research applications and could create the next generation in medical information systems and decision support.

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