C&B Notes

Filling the Doctor Gap with IBM’s Watson

Search could eventually be IBM’s Watson ‘listening’ to a lunch meeting to correct mistakes and offer additional insights in real time.  We are a long way from that reality, but a pilot program at the Cleveland Clinic is a step in this direction. Watson will supplement the diagnosis process in exam rooms.

At a glance, American hospitals seem ripe for a tool like Watson.  The country is facing a major shortage of primary care physicians, the all-purpose doctors working on the front lines of medicine, and the shortage will only get worse in the coming decade.  The Association of American Medical Colleges estimates the US will be short as many as 45,000 primary care doctors by 2020…More digitized hospitals have meant more comprehensive medical records, offering more reports to sift through for each patient even as there’s less and less time to work through each one.  To IBM, this looks like an information processing problem: too much data, and not enough doctors to manage it.  To solve the issue, it’s putting Watson to work summarizing medical records, giving doctors a quick summary of a patient’s medical history.  In theory, that should help them to treat more patients, more effectively.  If doctors are curious about a particular diagnosis or piece of data, they can drill down, tracking the information back to its source.

Since the program is still in a trial phase, it’s only used after doctors have made their initial diagnosis, but the doctors involved say it’s already showing promise.  “I’ve had a couple of patients where Watson found things that I had missed,” says Dr. Neil Mehta, the staff physician who’s leading the Cleveland Clinic’s end of the Watson project.  “It doesn’t work every time, but it’s getting better.”  One example is a patient suffering from sleep apnea-like symptoms.  Years earlier, this patient had a blood gas test that would have confirmed the diagnosis, but the test results were hidden in a hard-to-find section of the medical record.  Without Watson, Mehta says he never would have seen the result.  The process for finding that crucial test turns out to be remarkably similar to finding the right answer to a Jeopardy question.  Having built a basic concept map from studying medical exams, Watson parses the medical records for facts and test results, then knits them together into competing theories that might explain the patient’s symptoms


So far, the biggest roadblock isn’t machine intelligence, but human organization.  Watson works best with clean, unambiguous data sources, and at the moment, electronic health records don’t quite fit the bill.  “It does not come in a clean format at all,” Barborak says.  “It comes in a very noisy format.”  Doctors will frequently leave out the end date of a prescription, or the related causes of a particular ailment — omissions that rarely confuse human doctors, but throw Watson for a loop.  The structure of medical language can also be confusing.  A simple condition like high cholesterol can be diagnosed by multiple different names, each with a subtly different meaning.

Selling doctors on the process may be an even bigger problem.  The same overworked conditions that make doctors ideal candidates for Watson also leads them to cut corners in record-keeping.  For many doctors, electronic health records are just one more kind of paperwork, a distraction from the primary goals of medicine — and so far, research hasn’t given them much reason to change their mind.  “I’ll tell you, I love electronic health records, I can’t live without them,” Mehta says, “But in all the data you look at, we have not shown improvement in key outcomes in patient care or cost in spite of using them.  And it’s because we don’t use them right and we don’t have the time.”