The Affordable Care Act (ACA) and Accountable Care Organizations (ACOs) have made it more expensive to readmit patients soon after treatment. But the spread of technology like electronic health records (EHRs) and other applications also makes it possible to use data in a variety of ways, perhaps many of which we have yet to discover and define.
A lot of the insurers’ population health strategy is driven by two facts: The ACA squeezes per-patient profit margins, and maintenance of many diseases is expensive.
If you are a physician or hospital administrator, you would be concerned with chronic disease in an occurrence in specified population from causes-and-treatments, as considerably as a financial perspective. To that end, hospitals are frequently using remote patient monitoring and analytics as embedded components in the care process. While much information is being assembled, there is a gap between the data we can compile and know what to perform with it.
“Analytics provides a huge opportunity, but we lack the data science and medical algorithms,” says Gregg Malkary, managing director of Spyglass Consulting Group. “We actually don’t know how to interpret certain data because medical science is still immature.”
A high-profile example of what Malkary describes is the failure of Google Flu Trends (GFT), the company’s effort at covering such data and alerting public health officials of flu outbreaks before the Centers for Disease Control could know about them.
“When Google quietly euthanized the program … it turned the poster child of big data into the poster child of the foibles of big data,” writes political science professors David Lazer and Ryan Kennedy in Wired. “But GFT’s failure doesn’t erase the value of large data… The value of the data maintained by entities like Google is nearly unbounded if applied correctly.”
Google’s adventure becomes a deterrent example for those that come later, adding to acquired knowledge and contribution to later success.
“The traditional population health definition can then be reserved for geographic populations, which are the concern of public health officials, community systems, and business leadership,” he states, and the factor in contributors is like education, employment, and other non-clinical topics.
Geography on one side and whatever the determinant is—ethnicity, education, diet—on the other. It may not get us down the way to universal reason, merely it does supply the kind of flexibility that will likely come in handy as we wait for fresh ways to break down the mounds of data in search of healthier populations.
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