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Tag: Heuristics

Research Bites Dog

Screen Shot 2016-04-03 at 10.42.56 AMWe live in a headline/hyperlinked world.  A couple of years back, I learned through happenstance that my most popular blog posts all had catchy titles.  I’m pretty confident that people who read this blog do more than scan the titles, but there is so much information coming at us these days, it’s often difficult to get much beyond the headline.  Another phenomenon of information overload is that we naturally apply heuristics or short cuts in our thinking to avoid dealing with a high degree of complexity.  Let’s face it: it’s work to think!

In this context, I thought it would be worth talking about two recent headlines that seem to be set backs for the inexorable forward march of connected health.  These come in the form of peer reviewed studies, so our instinct is to pay close attention.

In fact, one comes from an undisputed leader in the field, Dr. Eric Topol.  His group recently published a paper where they examined the utility of a series of medical/health tracking devices as tools for health improvement in a cohort of folks with chronic illness.  In our parlance, they put a feedback loop into these patients’ lives.  It’s hard to say for sure from the study description, but it sounds like the intervention was mostly about giving patients insights from their own data.  I don’t see much in the paper about coaching, motivation, etc.

If it is true that the interactivity/coaching/motivation component was light, that may explain the lackluster results.  We find that the feedback loops alone are relatively weak motivators.  It is also possible that, because the sample included a mix of chronic illnesses, it would be harder to see a positive effect.  One principle of clinical trial design is to try to minimize all variables between the comparison groups, except the intervention.  Having a group with varying diseases makes it harder to say for sure that any effects (or lack of effects) were due to the intervention itself.

Dr. Topol is an experienced researcher and academician.  When they designed the study, I am confident they had the right intentions in mind.  My guess is they felt like they were studying the effect of mobile health and wearable technology on health (more on that at the end of the post). But you can see that, in retrospect, the likelihood of teasing out a positive effect was relatively low.

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Will Getting More Granular Help Doctors Make Better Decisions?

flying cadeuciiI’ve been thinking a lot about “big data” and how it is going to affect the practice of medicine.  It’s not really my area of expertise– but here are  a few thoughts on the tricky intersection of data mining and medicine.

First, some background: these days it’s rare to find companies that don’t use data-mining and predictive models to make business decisions. For example, financial firms regularly use analytic models to figure out if an applicant for credit will default; health insurance firms can predict downstream medical utilization based on historic healthcare visits; and the IRS can spot tax fraud by looking for fraudulent patterns in tax returns. The predictive analytic vendors are seeing an explosion of growth: Forbes recently noted that big data hardware/software and services will grow at a compound annual growth rate of 30% through 2018.

Big data isn’t rocket surgery. The key to each of these models is pattern recognition: correlating a particular variable with another and linking variables to a future result. More and better data typically leads to better predictions.

It seems that the unstated, and implicit belief in the world of big data is that when you add more variables and get deeper into the weeds, interpretation improves and the prediction become more accurate.Continue reading…