ORLANDO, Fla.--(BUSINESS WIRE)--Healthcare payers and providers increasingly face a squeeze from fraudulent healthcare claims and penalties for patient readmissions. Atigeo and Actian are teaming to offer proven applications with configurable capabilities that combine big data analytics, data science and deep domain expertise to address the gaps.
Atigeo, a compassionate technology company, and Actian Corporation, a big data analytics innovator, today introduced joint capabilities to dramatically enhance medical fraud detection and patient readmission risk analytics. Together, these will help drive down healthcare costs while improving patient outcomes. By combining Atigeo’s xPatterns™ predictive algorithmic technology with the power and scalability of the Actian Analytics Platform™, the companies deliver high-performance customizable healthcare offerings that will help drive down costs while improving patient outcomes.
“As the National Health Care Anti-Fraud Association notes, between $70 billion and $223 billion is stolen through health care fraud schemes annually1,” said Michael Sandoval, CEO of Atigeo. “The combination of xPatterns on the Actian Analytics Platform scrutinizes large claims datasets rapidly to discover utilization patterns that deviate from what’s expected, flagging potential fraudulent claims before processing to reduce unnecessary payouts.”
The companies are also combining capabilities to offer customers the ability to identify patients whose profile shows them to be at risk of unplanned rehospitalization. This is critical because, for example, Medicare levied an estimated $227 million2 in penalties against hospitals with high readmissions rates in 2013. In the United States, nearly 20 percent of Medicare inpatients are rehospitalized within 30 days, at a cost of $17 billion3. Proactive intervention to mitigate readmission dramatically improves patient outcomes and reduces care expenses.
“The Actian and Atigeo alliance furthers our commitment to healthcare organizations to deliver the maximum value from their data through innovative and proven predictive analytics. Knowing which patients are readmission risks before they are discharged lets healthcare providers intervene with proactive strategies to ensure patient compliance with follow-up treatment plans and to reduce readmission risks,” said Actian CMO Ashish Gupta. “Applications delivered on the Actian Analytics Platform will help healthcare customers better meet evolving standards such as Clinical Auto-Coding, Accountable Care Organizations and readmission requirements, while lowering latent costs of potential claims fraud and enabling better preventive care.”
Atigeo (booth 5183) and Actian (booth 3393) will be demonstrating their fraud prevention and patient readmission analytics capabilities at HIMSS14. The capabilities focus on rapidly discovering patterns in the data and supporting iterative analytics on both legacy and emerging data sources, with the speed and economics of cloud-based deployment. The offering includes pre-packaged healthcare-related datasets, with public and proprietary datasets, kept current and relevant as part of a managed HIPAA-compliant cloud service. Atigeo’s xPatterns analytics engines combine EMR, claims and pharmacy data to produce measurably superior data mining, text mining, inference and predictive analytics. The Actian platform speeds connectivity to diverse and large data sources and provides YARN-certified acceleration of native Hadoop processing with high-performance SQL queries on Hadoop to make the power of Hadoop accessible to the broad market.
In addition, Atigeo and Actian will be demonstrating xPatterns Clinical Auto-Coding (C.A.C.), which improves healthcare coding efficiency and accuracy. xPatterns C.A.C. automatically infers clinical codes, including ICD-10, by extracting concepts from the text in clinical encounter notes such as physician notes, lab results, and admit/discharge records. xPatterns C.A.C. detects under-coding, over-coding, and miscoding to deliver higher accuracy and coder productivity.
Atigeo is a compassionate technology company for a wiser planet. Its big data analytics platform, xPatterns, operates either on premise or in the cloud to lower any company’s cost barrier for extracting knowledge from all available data, even with a legacy IT infrastructure. Enterprise-grade and developer-accessible, xPatterns makes data easier to leverage in its native state, greatly reducing the effort, expense and limitations of data architecture and analysis. For more information, please visit http://atigeo.com.
About Actian: Accelerating Big Data 2.0™
Actian transforms Big Data into business value for any organization – not just the privileged few. Actian provides transformational business value by delivering actionable insights into new sources of revenue, business opportunities, and ways of mitigating risk with high-performance in-database analytics complemented with extensive connectivity and data preparation. The 21st century software architecture of the Actian Analytics Platform delivers extreme performance on off-the-shelf hardware, overcoming key technical and economic barriers to broad adoption of Big Data. Actian also makes Hadoop enterprise-grade by providing high-performance ELT, visual design and SQL analytics on Hadoop without the need for Map Reduce skills. Among tens of thousands of organizations using Actian are innovators using analytics for competitive advantage in industries like financial services, telecommunications, digital media, healthcare and retail. The company is headquartered in Silicon Valley and has offices worldwide. Stay connected with Actian Corporation at www.actian.com or on Facebook, Twitter and LinkedIn.
Atigeo and xPatterns are trademarks of Atigeo. Actian, Big Data for the Rest of Us, Accelerating Big Data 2.0 and Actian Analytics Platform are trademarks of Actian Corporation and its subsidiaries. All other trademarks, trade names, service marks, and logos referenced herein belong to their respective companies.
3 “Predicting 30-day all-cause hospital readmissions,” Mollie Shulan et al., http://www.ncbi.nlm.nih.gov/pubmed/23355120