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Applied Big Data: The Freakonomics of Healthcare

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Applied Big Data: The Freakonomics of Healthcare

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I went with a less provocative title this time because my last blog post (http://brianoneill.blogspot.com/2014/04/big-data-fixes-obamacare.html) evidently incited political flame wars.   In this post, I hope to avoid that by detailing exactly how Big Data can help our healthcare system in a nonpartisan way. =)

First, lets decompose the problem a bit.


Our healthcare system is still (mostly) based on capitalism: more patients + more visits = more money.  Within such a system, it is not in the best interest of healthcare providers to have healthy patients.  Admittedly, this is a pessimistic view, and doctors and providers are not always prioritizing financial gain.   Minimally however, at a macro-scale there exists a conflict of interest for some segment of the market, because not all healthcare providers profit entirely from preventative care.


Right now, with a few exceptions, everyone pays the same for healthcare.  Things are changing, but broadly speaking, there are no financial incentives to make healthy choices.  We are responsible only for a fraction of the medical expenses we incur.  That means everyone covered by my payer (the entity behind the curtain that actually foots the bills) is helping pay for the medical expenses I may rack up as a result of my Friday night pizza and beer binges.


Finally, the government is trying.  They are trying really hard.   Through transparency, reporting, and compliance, they have the correct intentions and ideas to bend the cost curve of healthcare.  But the government is the government, and large enterprises are large enterprises.  And honestly, getting visibility into the disparate systems of any large single large enterprise is difficult (ask any CIO).  Imagine trying to gain visibility into thousands enterprises, all at once.  It's daunting: schematic disparities, messy data, ETL galore.

Again, this is a pessimistic view and there are remedies in the works.  Things like high deductible plans are making people more aware of their expenses.  Payers are trying to transition away from fee-for-service models. (http://en.m.wikipedia.org/wiki/Fee-for-service).  But what do these remedies need to be effective?  You guessed it.  Data.  Mounds of it.
If you are a payer and want to reward the doctors that are keeping their patients healthy (and out of the doctors offices!), how would you find them?   If you are a patient, and want to know who provides the most effective treatments at the cheapest prices, where would you look?  If you are the government and want to know how much pharmaceutical companies are spending on doctors, or which pharmacies are allowing fraudulent prescriptions, what systems would you need to integrate?
Hopefully now, you are motivated.  This is a big data problem.  What's worse is that it is a messy data problem.  At HMS, its taken us more than three years and significant blood, sweat and tears to put together a platform that deals with the big and messy mound o' data.   The technologies had to mature, along with people and processes.  And finally, on sunny days, I can see a light at the end of the tunnel for US healthcare.
If you are on the same mission, please don't hesitate to reach out.
Ironically, I'm posting this from a hospital bed as I recover from the bite of a brown recluse spider.
I guess there are certain things that big data can't prevent. ;)

Check out the Exaptive data application Studio. Technology agnostic. No glue code. Use what you know and rely on the community for what you don't. Try the community version.


Published at DZone with permission of Brian O' Neill, DZone MVB. See the original article here.

Opinions expressed by DZone contributors are their own.


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