Edited by Yan Zhang

Many pundits speaking about the state of the nation’s healthcare infrastructure routinely point to fraud, waste, and abuse (FWA) as major reasons for many of the problems the public witnesses every day – increased healthcare costs and the resulting increase in insurance premiums. The net effect is an annual loss of billions of dollars, and these losses affect the public in very real ways. 

The size of the healthcare sector, the enormous amount of money involved, and the lack of surveillance and monitoring mechanisms across the healthcare ecosystem make it an attractive target for FWA. According to the Office of Management and Budget, in 2010, about 9%, or $47.9 billion was lost to fraud in Medicare alone. It is therefore imperative to develop effective FWA technologies and solutions for reducing the costs associated with our healthcare system.

Data science and its primary enabling methodology of machine learning represent the country’s best chance for detecting FWA to avoid extraordinary sources of loss. Data science possesses the facilities to make a significant difference healthcare industry budgets and their impact to the public. Opera Solutions is the industry leader in applying Big Data technologies to the most challenging and significant business problems. We are the company charged with developing the analytics to identify fraud for the Centers for Medicare & Medicaid Services (CMS) on the health insurance exchanges. Here’s a look into just how big this challenge is — and some of the approaches we’re taking to overcome one of the costliest burdens in America.

Switching to a new medical coding system won’t be easy, but when combined with data science and machine learning, ICD-10 presents enormous potential benefits for both the financial and the clinical sides of healthcare.

Part of why the healthcare industry is such a notorious laggard in jumping on the Big Data bandwagon is that every attempted change faces a huge domino effect, rendering many good ideas useless until everyone — and everything — is ready. One big step in the right direction, however, is an important upgrade to the computerized codes used for electronic medical records (EMR), which will take hold in the next year or two. These codes, known as ICD or International Classification of Diseases, determine what ailments patients have and how much they and their insurers should pay for a treatment. The set of codes, currently called ICD-9, is scheduled for its 10th revision this fall (but there may be a year-long delay). The updated codes, called ICD-10 codes, improve on the previous standard by adding more descriptive capabilities that will help healthcare professionals better categorize and keep track of patient disorders and treatments. Through the use of machine learning and other data science techniques, this increased granularity is expected to open up patient treatment analytics along with the ability to better monitor public health threats.

Since retail clinics started gaining popularity in 2007, healthcare professionals have been worried — for all sorts of reasons. Will patients still get the care they need? Will they visit their doctors less? How will these clinics impact patient volume in nearby emergency departments?

Predictive analytics–based staffing solutions can save hospitals millions.

While we wait to see how insuring more of the population affects healthcare costs for patients, hospital groups are also working hard to reduce the cost of healthcare. Some hospitals are reducing unnecessary department labor costs — in some cases staff hours have been reduced by up to 8% — by using solutions that analyze Big Data to predict patient inflows. With accurate patient forecasts, hospitals can optimize scheduling to better match supply (nurses) to demand (expected patients). Not only does this reduce costs, it makes their hospitals more efficient and even increases patient satisfaction.

While some users are just now getting their footing in Big Data, two — healthcare management and fraud prevention experts —are already successfully using predictive analytics to deliver unprecedented results. And so it makes sense that the Centers for Medicare and Medicaid are turning to Big Data predictive analytics solutions to prevent fraud and enhance operational controls in the new health insurance exchanges that form the centerpiece of the program.

NEW YORK, N.Y., Nov. 15, 2012 — Opera Solutions, LLC, a leading global Big Data science company, today announced that it has been selected by the Centers for Medicare & Medicaid Services (CMS) to provide advanced analytics to enhance operational controls and prevent fraud in federally funded Health Insurance Exchange Operations (“HIX”) managed by CMS’ Center for Consumer Information and Insurance Oversight (CCIIO). Opera Solutions will apply its unique healthcare claims data anomaly detection experience, in tandem with its extensive origination/eligibility and fraud detection expertise, to support CMS’ rollout of the Exchanges that begins in January 2014.

NEW YORK, N.Y., June 20, 2012 — Opera Solutions, LLC, a leading global Big Data predictive analytics company, today announced the general availability of its Revenue Cycle Management solution.

Already in use in more than 60 hospitals, the solution helps hospitals significantly increase profitability and operating efficiencies by identifying missing charges and reducing audit expenses. Based on industry profitability averages, hospitals using the solution can increase their operating income by 10 percent or more.