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Connected Health Predictive Analytics: A Long Road Ahead

February 11, 2013

We’re spending a lot of time at the Center for Connected Health (CCH) these days thinking about and experimenting with algorithms.  It’s part of our general interest in micro-segmenting the population and creating unique, engaging health messaging for each individual that will keep them on the path to better health.  Healthrageous is working fast and furious on this as well.  Of course, we’re not the only ones.  A number of other labs and firms are on the same journey.  The vision is compelling.

However, today when you get health related messages from your insurer or another source, they are typically public health focused.  Stop smoking!  Get your mammogram! Get your flu shot!  These three messages illustrate the challenge. I’ve been the recipient of all of them recently.  I’ve never smoked, clearly do not need a mammogram and was vaccinated for influenza in early October.

I always thought our friends on the consumer web side were doing better.  The first time you experience Amazon’s or Netflix’s recommendation engines, they tend to raise eyebrows.  Over time, the experience is less salient.  And let’s face it, it’s got to be easier to guess which type of movie I might want to watch or a book that might interest me than to predict what a really engaging health-related message might be.

At CCH we’re in the middle of an interesting trial funded by the McKesson Foundation, where we collect three types of data (a measure of readiness to change, ongoing activity data and location data) and use an algorithm to generate motivational messages based on these variables.  It’s ongoing now, so I don’t know how it will turn out, but we’re excited about the possibilities. Still, it’s only three variables and only one (activity level) is continuous. My instinct is that we have a long journey ahead of us.

I got some confirmatory evidence when I read a front-page article in the Boston Globe last week.  A Harvard professor picked up a sad fact about Google’s algorithms.  If one searches for an African American sounding name such as Trevon, Rasheed or Tamika, you are more likely to get an ad offering you an opportunity to run a background check on that individual than if you put in a more Caucasian sounding name such as Brad, Cody or Amy.

Imagine if we goofed like this in our ‘highly customized’ health care messaging.  What a disaster.  We’d lose patients and consumers by the legion.

Health messaging is complex. It needs to be highly individualized, motivational, caring and of course accurate.

Indeed we have a long way to go. Although our friends in consumer, retail and financial services industries have not quite conquered this complex challenge (witness the Google gaff noted above), they are way ahead of where we are in healthcare. So, I hope some of those bright algorithm scientists can be convinced to turn their attention to connected health. We need the talent. The problem is intellectually challenging enough and the rewards will be great.

15 Comments leave one →
  1. February 11, 2013 7:03 pm

    I share your view about personalized health messaging.
    Our pre frontal cortex is watching for self relevant information. Natural selection made it so.
    Rapid reinforcement after our behavior is interpreted as self relevant.
    Powerful principles !

  2. February 11, 2013 8:39 pm

    Reblogged this on Salud@Móvil.

  3. February 12, 2013 9:32 am

    Sounds like you are on the right track. However, after this initial proof of concept you may need to look more broadly at the data sources that are available to you. In our analytics practice we find that organizations have more data than they realize. Also,with powerful data mining tools you can simultaneously study the effect of hundreds of variables. Often the predictive variables turn out not to be what you expected.

  4. February 15, 2013 9:51 am

    Great post. I think the main point that we can steal from Google and the search engines, is use data to our advantage and I believe that this post states it greatly. I love data, but at the same time we need to find that fine line that is the human element. We need to make sure that we don’t cross it and forgot to talk to to our patients regularly.

  5. February 18, 2013 10:46 pm

    Right on target! Algorithms to develop the right message are necessary but not sufficient. We will need to develop the ‘right’ message & deliver it at the ‘right’ time on the right channel & make it easy for the receiver to take action. So a long road ahead indeed but well worth the journey!

  6. February 26, 2013 3:35 pm

    This reminds me of a Venture Capitalist I know who claimed that “65% of doctors will one day be replaced by a software algorithm.” handles secure mobile-messaging between about 8,000 doctors and 3 million patients. A mix of text, images and voice – “Mobile Health as a Service” (mHaaS) may not be “splitting the atom with predictive big data analytics,” but it’s doing something many Americans have wanted to do for a long time – message their doctor on their smartphone. In our zeal to solve “tomorrow’s medicine” today, I think we often forget about the incremental progress of “medicine today” that we’re making here in San Francisco and Silicon Valley.

  7. February 26, 2013 5:57 pm

    Reblogged this on lava kafle kathmandu nepal.

  8. February 26, 2013 6:39 pm

    Got algorithms? We do…and what we have learned matters most is uncovering the real problem or question that needs answering. 25 percent of the United States’ total annual health care expenditures are the result of behaviors that could be changed… smoking, exercise, and diet. Do we need complex algorithms or do we really need a different approach to analytics?

    The consumerization of healthcare is transforming healthcare. Not Large integrated health delivery networks, not life science companies, not insurance, not wellness companies, not Health IT, not clinicians, not AMCs, and not the government!
    Thanks Joe for the post. You are a moving the needle.

  9. April 17, 2013 5:18 pm

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