SOURCE: Apollo Data Technologies

July 17, 2007 09:00 ET

Apollo Data Technologies Launches Predictive Analytics Healthcare Practice

Helps Providers Shorten Revenue Cycles by Uncovering Patterns in Patient and Billing Data

CHICAGO, IL--(Marketwire - July 17, 2007) - Healthcare providers are always looking for ways to improve their margins and identify best practices to optimize revenue cycles. A smarter and more efficient way to do this is to use predictive analytics (also known as data mining) to automate the process of discovering patterns in large quantities of patient and billing data collected by financial management systems, electronic medical records and clinical information databases.

Apollo Data Technologies (, a provider of predictive analytics software and solutions, announced today the launch of its Healthcare Practice designed to help healthcare providers improve revenue cycles and more efficiently sort through the wealth of patient data and reduce inefficiencies in claims processing, regulatory complexities, Medicare and increases in self-pay receivables.

"Healthcare providers across the country are capturing massive amounts of data in the form of HIS systems, Electronic Medical Records, billing systems, etc. Unfortunately, many providers don't know how to mine this data to help identify such things as trends in reimbursement rates from insurance companies, patterns in denied claims, and correlations in care and symptoms that lead to various medical errors," said Tim Miller, Director, Stockamp & Associates (, nationally recognized as the leading provider of revenue improvement solutions, focusing on financial and operational solutions for hospitals and health systems.

By leveraging Apollo's predictive analytics providers can now mine through their terabytes of data in minutes versus months to identify underpaid claims, at-risk and denials. This can create significant positive impact on operating margins and enable providers to reinvest their dollars toward improved patient care.

Moving forward, hospitals have the opportunity to mine EMR and HIS data to score all claims prior to billing. Using these predictive models, the provider can proactively flag "likely denials" for further review. This leads to a decrease in the volume of denials and ultimately, drives faster collection times and improvements in their revenue cycle (for more information please see November 2006 article from Revenue Cycle Strategist --

Apollo's suite of Healthcare Predictive solutions include:

  --  Revenue Cycle Applications
      --  Self-Pay Analytics
      --  Denial Management
      --  Claim Underpayment Prediction
      --  At-Risk Claims Prediction

  --  Patient Care Applications
      --  Medical Error Prediction
      --  Revenue Capture Analytics
      --  Physician Performance Analytics
      --  Facility Capacity Optimization

  --  Supply Chain Applications
      --  Supply Optimization
      --  Supply Use Anomaly Detection

"Our predictive analytic software and solutions have proven to be highly effective for Fortune 500 companies in retail, telecommunications, and information technology industries. We feel the healthcare market is greatly underserved, and there is a tremendous opportunity for providers to leverage predictive analytics to make more timely decisions and react faster to inefficiencies in revenue cycle practices and the changing demands of a patient population," said Jeff Kaplan, managing director, Apollo Data Technologies.

About Apollo Data Technologies

Apollo Data Technologies ( is a predictive modeling firm helping companies leverage their data to increase their bottom-line. Apollo discovers hidden patterns and trends in data to help organizations operate more effectively and efficiently. Its world-renowned and award-winning team of Ph.D.s bring decades of distinguished work in analytics, expertise in building and designing data mining applications, and the practical business know-how in applying the results. Apollo provides solutions for all stages of data analysis, including setting data collection strategies, data warehousing, and data mining.

Contact Information

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    Martin Levy
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