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Background |
Background Data Description and Healthcare Financial Analytics have combined their talents and resources to develop and validate Recovery and Monitoring tools for the healthcare industry. These user-friendly, microcomputer software tools, based on the award-winning Data Desk software, are designed to target specific clinical practice and/or billing anomalies within hospitals (and other provider systems) whose clinical, coding or billing practices differ from benchmarks based on our extensive nationwide comparison datasets. A particularly powerful application of the technology is in the identification and recovery of fraudulent, abusive or otherwise inappropriately submitted billing claims. They are based on the combined vision of Dr. Paul Velleman and Dr. Richard Newbold. Dr. Velleman, the President of Data Description, is a world-renowned expert and pioneer in statistics, exploratory data analysis, and statistical software design. Dr. Newbold, the Chief Medical Director of Healthcare Financial Analytics, is a nationally-recognized leader in designing and applying highly-sophisticated exploratory data analytics for validating, recovering and monitoring inappropriate healthcare services claims (the ongoing hospital bacterial pneumonia claims recovery project for HCFA/DOJ is based on his upcoding analytics). The Newbold-Velleman partnership offers payers and regulatory agencies a comprehensive, validated approach to the in-depth analysis of healthcare claims, including efficient recovery of overbilling informed by leading-edge statistics, data mining and analysis. This focus is quite different from traditional, accounting-based approaches, or even other "data mining" technologies that depend upon "black box" algorithms and methodologies. Rather than starting by examining each tree, our approach begins by viewing the forest of electronic claims as a whole, identifies likely areas of fraud or clinical impropriety, and then drills down to examine individual records and sets of records. Our approach can identify patterns of fraud and increased expenses that cannot be detected at the individual case-by-case level of analysis. This offers vastly increased opportunities for recovery and cost reduction with far greater efficiency and lower expert labor burdens than prior methods. |
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