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Jun 22

Telling Signal from Noise in the Era of Value-based Care

The $3.2 trillion healthcare sector today faces tremendous change.[1] More individuals than ever before have regular access to healthcare services, and once-untreatable diseases are becoming manageable chronic conditions.

Meanwhile, the business of healthcare is changing quickly. The fee-for-service model that primarily incentivized volume is transitioning to a value-based system that reimburses for outcomes. Healthcare also is digitizing rapidly, an industry-wide effort that is creating immediate challenges like IT interoperability and data security. This transition from manila medical file folders to electronic health records holds tremendous promise, but only if stakeholders can differentiate signal from noise when it comes to examining the available datasets.

So then, exactly what healthcare data is valuable—and why?

Clinical data: Roughly 50 percent of all U.S. healthcare costs are generated by about 5 percent of patients.[2] Addressing the lopsided nature of healthcare spend in the United States has long been a priority in the industry, but it’s becoming more immediate as bundled payment programs come online and force providers and payers to share the financial risks of treating patients. One area that shows considerable promise is predictive analytics, a field that combines Big Data with artificial intelligence. For example, International Business Machines’ Watson supercomputer accurately prescribed cancer treatments in 99 percent of the cases it reviewed recently at the North Carolina School of Medicine.[3]

Financial data: Another priority for healthcare data scientists involves connecting the dots between seemingly unrelated data points across a vast spectrum. Financial information, in particular, must play a larger role in healthcare decision making in the coming years, as more advanced treatment options come online, patients take on more of their own healthcare expenses and healthcare transitions away from volume-based payments. For example, Medicare has set a goal of converting 50 percent of its fee-for-service payments to value-based reimbursement programs by 2018. [4] As part of this cost-containment initiative, the agency is rolling out MACRA, which once fully implemented will incentivize physicians to accept bundled reimbursements in cases like heart attacks, strokes and total joint replacement.[5] In order to successfully treat them, however, participating physicians will need to carefully consider a wide range of not just financial information, but also clinical data in order to manage countless challenges related to patient selection, staffing levels, supply chain and revenue cycle.

Interested in learning more about some of the ways Alveo is harnessing the power of Big Data to enable your healthcare business? Please visit www.alveohealth.com.

 

[1] U.S. Centers for Medicare and Medicaid Studies, NHE Fact Sheet, accessed 2017
[2] Bates, D. “Big Data in Health Care: Using Analytics to Identify and Manage High-Risk and High-Cost Patients,” 2014
[3] Vena, D., “IBM’s Watson is Tackling Healthcare with Artificial Intelligence,” 2017
[4] U.S. Centers for Medicare and Medicaid Studies, Health Care Payment Learning and Action Network, accessed 2017
[5] Lagasse, J., “Lawmakers must engage low-spending consumers, Health Affairs says,” 2017

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