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Opera Solutions Launches Advanced-Analytics-Based Capability to Help Hospital Systems Improve Charge Capture

Posted by Todd Higginson on Wed, Jun 20, 2012

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.

"Hospital billing has become increasingly complex, and approaches to manage it have not kept up," said Pieter Schouten, General Manager, Healthcare Solutions. "Traditional rules-based systems are only as good as the latest rules update; manual auditing cannot comprehensibly look at all the bills. And neither approach can fully take into account the medical context. In contrast, our pattern-based, machine-learning approach utilizes predictive modeling, anomaly detection and other advanced analytic techniques that allow us to dynamically detect patterns and zero in on inaccuracies as they emerge. Ultimately, the corrections our clients make and the efficiencies they derive can lead to better healthcare operations across the board."

The solution's analytics include an ensemble of advanced techniques that rapidly and accurately detect missing and erroneous charges. It is trained to search through each day's charges, pinpoint potential errors and omissions, and deliver prioritized "account review lists" to nurse auditors. Provided as a hosted service, the solution requires only minimal new IT and infrastructure development. The patent-pending analytics were developed by Opera Solutions' scientific team, which includes over 220 data scientists specializing in machine-learning techniques. Opera Solutions now plans to extend from revenue cycle management to cost containment and fraud detection.

Topics: Healthcare, Machine Learning, Press Releases