Healthcare Fix: More Data, Better Models


Big-data, it pretty much goes without saying, is a hot topic no matter in what line of work your interests lie. The availability of so much data, and so many different types of data, can transform one industry after another.

Take healthcare. The advent of the electronic medical record (EMR), for example, has suddenly created a data bonanza for healthcare providers and academic researchers studying healthcare challenges. With so much data, solving big health issues should be easier than ever.

Now, hang on a minute. Despite what lots of people out there think, having lots of data doesn't mean being able to answer the questions. People sometimes forget about the at times arduous task of turning that data into insight. As Brian Denton, a healthcare researcher, told me in a recent phone interview, "It's applying the analytics model that makes it possible to turn data into useful information that doctors can use at the point of care."

Brian Denton
Brian Denton

I caught up with Denton after the INFORMS Healthcare 2013 conference that took place in Chicago in late June. In its second year, the conference is a reflection of the growing momentum in healthcare analytics right now. "There's an enormous growth in the number of problems people are working on and the number of people working on them," said Denton, who, among other credentials, is an associate professor in the Department of Industrial and Operations Engineering at University of Michigan and INFORMS secretary.

The excitement in healthcare analytics comes on two fronts: The first is healthcare delivery and management, and the second is the medical side of healthcare. Denton's interest and work falls in the second area, where his experience exemplifies how increased amounts of data, when poured into sophisticated analytics models, can change patient care.

For the past five years or so, Denton's team has been collaborating with investigators at the Mayo Clinic in Type 2 diabetes research funded by the National Science Foundation. The goals are to help define better treatment guidelines for high blood pressure and high cholesterol -- two major factors for diabetes -- at a national level, and to provide decision support to physicians at the point of care. In a nutshell, Denton said, "We've been using large amounts of historical data to develop models that can be used to simulate what happens when you apply guidelines for treatment to patients with Type 2 diabetes."

Initially, the team looked at how to control one risk factor. "We asked a simple question, that is, 'If and when should patients start using statins to control or lower their cholesterol?' "

Then, over time, it began looking at multiple drug treatments. "There are more drugs than just statins for controlling cholesterol, so we looked at using quantitative models to define a plan for how to use these kinds of medications together to reduce cholesterol."

From there, the team branched out to looking at other risk factors -- and along the way developed a model for blood pressure independent of what it had done for cholesterol. Then came bringing the two together into a model that "coordinates drug treatments for both cholesterol and blood pressure," Denton said.

That brings us to the present, and the team's exploration of blood sugar.

    The breadth of our model is growing in terms of how comprehensive it is regarding medical treatment decisions for patients with Type 2 diabetes. And the data we've been using has been growing as well. Our ability to harness large datasets to calibrate the models we've been working with has improved over time.

In its initial work, the team used data collected as part of a small study at Mayo Clinic. "So that's a very specific population of people in one region." More recently, it's been with a much bigger and more comprehensive dataset compiled from a far-flung population.

The dataset comes from more than 100,000 people across the US who have Type 2 diabetes, and includes data on various medical factors, such as blood pressure, cholesterol, and blood-sugar levels, and how they've changed over a 10-year period, Denton said. "Twenty years ago, we just didn't have access to datasets like this. It's the availability of the data that's making these kinds of studies possible."

And it's groundbreaking analytical work that's making the difference between having data and gaining meaningful intelligence from it. Denton's group is the first to develop an analytics model for optimizing treatment decisions.

As yet, however, the group is still doing the groundwork for the decision support system physicians would use at the point of care. Denton said he hopes to begin developing the point-of-care system within the next year. "We're getting close to having most of the risk factors covered in our prototype software, and at that point it becomes a question of implementing in a decision support system for the point of care."

What other innovations in healthcare can we attribute to more data and better analytics models? Share your ideas below.

Beth Schultz, Editor in Chief

Beth Schultz has more than two decades of experience as an IT writer and editor.  Most recently, she brought her expertise to bear writing thought-provoking editorial and marketing materials on a variety of technology topics for leading IT publications and industry players.  Previously, she oversaw multimedia content development, writing and editing for special feature packages at Network World. In particular, she focused on advanced IT technology and its impact on business users and in so doing became a thought leader on the revolutionary changes remaking the corporate datacenter and enterprise IT architecture. Beth has a keen ability to identify business and technology trends, developing expertise through in-depth analysis and early adopter case studies. Over the years, she has earned more than a dozen national and regional editorial excellence awards for special issues from American Business Media, American Society of Business Press Editors, Folio.net, and others.

Midmarket Companies: Bring on the Big Data

The "big" in big data is no reflection of the size of the organization embracing its potential.

Push Yourself to New Analytical Discoveries

Take inspiration from Christopher Columbus as you pursue your analytical journeys.


Re: Useful is the operative word
  • 7/15/2013 10:44:53 AM
NO RATINGS

I agree. The wheel chair bound should definately see some advnatage here.

Re: Useful is the operative word
  • 7/15/2013 10:24:34 AM
NO RATINGS

I'm not sure how much there is to disagree over here. Who do you have in mind as potential other beneficiaries of this type of technology if not the wheelchair-bound themselves? 

 

Re: Useful is the operative word
  • 7/15/2013 8:52:14 AM
NO RATINGS

Perhaps. But the biggest question to answer would be "who does this serve?" Unless we can fully agree that the beneficiaries are the people in the wheelchairs, then perhaps it would only be another burden for people already dealing with challenges

Re: Useful is the operative word
  • 7/14/2013 8:48:22 PM
NO RATINGS

How about a prosthetic, as it were, for your entire body -- i.e., an "exoskeleton suit" that would enable wheelchair-bound people capable of standing erect and walking? That would be fantastic use of better data and more advanced analytics modeling. 

Useful is the operative word
  • 7/14/2013 6:37:30 PM
NO RATINGS

"It's applying the analytics model that makes it possible to turn data into useful information that doctors can use at the point of care."

I think that underlies what data analytics can provide if it can turn a bunch of numbers into useful information that the medical field can use to apply in research, diagnosis, and treatment. One area of interest is in prosthetic development and how data acquisition and analysis is critical in engineering and designing new limbs.

Re: Really interesting
  • 7/12/2013 9:26:43 PM
NO RATINGS

Hopefully better decision support systems lead to lesser cases of misdiagnosis. This could also mean healthcare providers with less experience in the field will still have more information at their finger tips to provide better care for patients.

Re: Really interesting
  • 7/12/2013 6:45:54 PM
NO RATINGS

This is why Medicare and Medicaid starting cracking down and enforcing that hospitals treat and pay for it themselves cases of hospital infections and complications that are avoidable.  This is of course, just Medicaid and Medicare 

Re: Really interesting
  • 7/12/2013 12:15:17 AM
NO RATINGS

That Huff post report is depressing!

Re: Really interesting
  • 7/11/2013 11:20:03 PM
NO RATINGS

While there is now, much more data than ever before.  Many hospitals are not taking advantage of it, simply because of the costs of analyzing it all.  The ones that can take real advantage of it are pharmaceutical companies which have the resources to do so.  The other entity is the U.S. government, which has the big picture of all the hospitals in the country. 

Many hospitals don't make a profit.  And the ones that do, often sacrifice quality of care to get those profits.  Really what needs to happen is the whole model of U.S. health care needs to change. 

For example:

Re: Really interesting
  • 7/11/2013 5:43:46 PM
NO RATINGS

As long as patients have the option to opt-in -- rather than be required to opt-out -- then it seems like we could balance personal privacy against the greater good.

Page 1 / 2   >   >>
INFORMATION RESOURCES
ANALYTICS IN ACTION
CARTERTOONS
VIEW ALL +
QUICK POLL
VIEW ALL +