Michael Gutnick: Converting data into wisdom

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Michael Gutnick, Executive vice president and chief financial officer, Memorial Sloan Kettering Cancer Center

Michael Gutnick,
Executive vice president and chief financial officer, Memorial Sloan Kettering Cancer Center

Let’s start from the beginning. What was the goal behind building this massive database of cancer patients?

We started in 2002, acquiring 100,000 patient records from New York state to add to the hospital’s existing patient record database. But we knew we had to go bigger, so we branched out across the nation. We don’t have records on every cancer patient for every year. But we do have, for instance, records on every Medicare patient who was diagnosed with cancer in 2006. That’s 1 million patients, equal to approximately 1.5 billion records. To the best of our knowledge, no one has studied cancer at this depth, looking across the country to identify where and how patients are diagnosed and treated. This is big data applied to cancer.

What are you learning?

We’ve developed a heat map that covers the entire country, which we’ll ultimately publish. It points out, for particular kinds of cancer, if you live in X, Y, Z location, these are the health care providers that have the best outcomes, and which providers you should avoid. The risks for getting treatment in the wrong place are significant, and completely avoidable. We already know from published data, including our own, that there is a high correlation between volume and clinical outcome. We are now looking to take this work a step further.

What does this have to do with the CFO?

My aim is to better understand this competitive advantage. I have to. I lead the negotiations with our payers and with the various insurance companies. To command an appropriate price for our services, I have to defend our value proposition to the payers, to the plan sponsors and ultimately, to patients. We’re unique because we can extend people’s lives at no material increase—measured as additional cost per day survived. We know which diagnostic tests to perform. We don’t get repeat admissions for conducting unnecessary procedures. We’ve learned that what’s good for the patient is good for the entire organization. I’m surrounded by great clinical minds, impressive researchers and administrative whizzes who handle the day-to-day operation. I look at the macro picture. It’s my job to understand how we are performing and how that fits our value proposition. That’s the job of the CFO.

We don’t often hear about the business strategy behind defeating cancer. When you are up against a disease that is often terminal, how do you define the value proposition?

Our objective is to diagnose, treat and extend the lives as many patients as possible. You have to focus on what’s important for the patient otherwise you misalign yourself. The most important metrics for me come from these questions: Did you survive? Are you able to resume a normal life? That’s what I’m interested in, and that’s why I’ve been collecting data on this subject for over 15 years.

It’s been often said that one of the biggest challenges facing oncologists is the sheer explosion of medical information, which, for cancer alone, doubles every five years. Memorial Sloan Kettering cited this issue when it announced its partnership with IBM in 2012 to enlist the help of Watson. How did the partnership come about?

The idea sprang from Jeopardy! After the show aired, several of our strategic planning teams at the hospital started talking excitedly that cognitive computing could be a path forward for us. We have all this data. But we needed to translate it into knowledge, and ultimately, wisdom.

And what’s been the upshot of your push into cognitive computing?

I work alongside a lot of physicians who have a really difficult job. The people they deal with every day may not make it. Since our move into cognitive computing, I can tell you, I’ve never seen this much excitement on their faces. It frees them up from the mundane stuff. They can feed Watson humongous amounts of data—academic research, case data, physician notes—and it will crunch it into a smarter outcome. The upshot is they don’t have to spend nearly as much time in their offices poring over research articles. They can plug in a scenario like, ‘I have a patient with stage 2 ovarian cancer who has contracted this kind of problem.’ After further inquiry, Watson will respond with the specific research articles pertinent to that issue. And it will lay out the diagnostic and treatment options, ‘Here are the issues; here are the focus points.’ Under this approach, we can also train the medical students as we continue to feed it new information. We’re still in the testing phase, mind you. We haven’t seen what it’s going to do in the real market, but we already understand its potential.

What is that potential?

With many types of cancer, there still is not an adequate standard of care practiced in a community setting. Community physicians generally don’t have access to or rely on the current intelligence. Cognitive computing will disrupt this model. It will mean the latest tools for fighting cancer are regularly updated and matched with the latest ideas so that any physician anywhere in the world will have access to all the information relevant for the proper diagnosis and treatment of that patient. It will mean avoiding unnecessary tests. It will mean a much more rapid treatment cycle. And, the way technology is evolving, this information can be accessible to medical professionals anywhere in the world, even from a tablet or smartphone. We have been building this tool kit for some time. Now, thanks to cognitive computing, we can take it global.

So, what’s the big picture of cognitive computing’s potential effect on your organization?

I’m interested in any tool that helps oncologists make better decisions. If Watson, or cognitive computing in general, can help medical professionals avoid prescribing unnecessary tests or unnecessary drugs, that will in turn produce better outcomes at a lower cost—and it will save time too, which is a huge factor here. The discussion in data analytics is often about deriving intelligence from the data. We want to go one step further. We want to create wisdom about how to treat the next generation of patients.

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