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Big Data Analytics: Where's The ROI? -- InformationWeek

Big Data Analytics: Where's The ROI? -- InformationWeek | Healthcare | Scoop.it

If you're old enough to remember the Reagan administration, you remember the politically charged expression "trickle-down economics," which referred to the theory that if you provide benefits and incentives to businesses and the wealthy, those benefits would trickle down to wage earners at lower socioeconomic levels.In some ways, big data analytics is like trickle-down economics. Only the biggest healthcare providers with the deepest pockets can afford the kind of analytics platforms required to get useful intelligence from tens of thousands of patient records. But in theory, those benefits will trickle down to smaller providers that either don't have the financial support or the large patient populations to do this type of data crunching on their own.

 

How exactly is this supposed to work? Consider this example: A recent study conducted by Kaiser Permanente looked at the incidence of blood clots among women from two integrated health care programs and two state Medicaid programs, who were taking various oral contraceptive (OC) formulas. Their analysis revealed that one formula in particular, containing drospirenone, increased the threat of blood clots by 77% when compared to the risk in women taking several other OC formulas.

How might such big data analytics benefit the checkout girl at my local supermarket, or my car mechanic and his wife, who live in a small town and may be part of a group practice that can't do this kind of research?

Proponents of big data would say that's what peer-reviewed journals are for. The big guys publish the work in a creditable venue and clinicians in smaller community practices learn from their findings.

 

The only flaw in that reasoning is the huge lag time between published research and widespread adoption of that research in the trenches -- 5 to 10 years, by some estimates. In some cases, the research never gets put into practice.

And that disconnect between big data analysis and the rest of the healthcare system is only one of many. A panel discussion at last week's Connected Health Symposium in Boston raised several others. Even the session's title suggested controversy: "Big Data Healthcare Analytics: Frontier or Fiction?"

Charlie Baker, president of General Catalyst Partners, a venture capital firm, and former Secretary of Health and Human Services for the Commonwealth of Massachusetts, suggested that the industry put less emphasis on big data and more on fundamental payment reform. To fix what's broken in the healthcare system, Baker says, "I'd start with a reimbursement system that awards technology, procedures, fragmentation... and punishes primary care... There's nothing about big data in the short term that's going to solve that problem."

Baker went on to emphasize what many other stakeholders are shouting about: Finding a way to move from a pay-for-services system to a pay-for-value system has to be our top priority, not big data analysis.

That's not to suggest that such analysis shouldn't play a role in healthcare reform. But all the big data in the world isn't going to give us a substantial ROI if hospitals and individual practitioners continue to be rewarded for the quantity rather than quality of care they provide.


Via Chatu Jayadewa
Matthew Mayabb's insight:

"Trickle-down economics" barrier to leveraging analytics to gather useful intel and share best practices: huge lag time between published research and widespread adoption of that research in the trenches -- 5 to 10 years, by some estimates

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Population/Community Health | Small/Rural Hospitals

Population/Community Health | Small/Rural Hospitals | Healthcare | Scoop.it
Merging the resources and skills of hospitals and health care systems with community partners is essential for the integration and expansion of population health management programs. Online tools from the American ...
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Importance and opportunity of building strong community partnerships for effective population health management in rural areas

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Welcome to the stone age of healthcare analytics | Juergen Fritsch Blog | ComputerWorld.com

Welcome to the stone age of healthcare analytics | Juergen Fritsch Blog | ComputerWorld.com | Healthcare | Scoop.it

The Institute of Health Technology Transformation recently released a report which identified data analytics for population health management to be one of the critical capabilities for a successful accountable care organization (ACO). The ability to identify care gaps, categorize patients based on their health risks, and focus on prevention rather than just reacting to health issues has always been considered a key requirement in an ACO model. However, the use of the latest technology advances for large scale, big data analytics across structured and unstructured health data sets has increasingly made the difference for successful ACOs, enabling them to achieve these requirements where others have struggled.

 

One such organization - Dartmouth-Hitchcock - recently showed just how successful this model can be if relevant patient data is analyzed effectively. It was able to hit all 33 quality benchmarks in the first year of the Pioneer ACO Model, while saving $1.7 million over 17,000 patients enrolled in the ACO. What made them so successful at reigning in cost? It turns out they had a head start of seven years. Dartmouth researchers were looking into the factors contributing to savings under an earlier ACO model already in place before the start of the Pioneer ACO program and gained some valuable insights. One of the key conclusions was that the organization needs to be able to identify patients with multiple chronic diseases and focus their attention on care coordination, prevention and outreach activities customized specifically to that population and those diseases.

 

The use of technology that can look across and analyze the entirety of patient health data, spotting the equivalent of a needle in a haystack therefore becomes a crucial factor to success.

 

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Via Chuck Sherwood, Senior Associate, TeleDimensions, Inc
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Health data analytics used to identify patients with multiple chronic diseases and focus their attention on care coordination, prevention and outreach activities customized specifically to that population and those diseases

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