CARY, N.C.--(BUSINESS WIRE)--University of Louisville researchers are uncovering better ways for insurers and health care providers to comply with the US health care bill President Obama signed into law. They are using SAS Analytics software from the leader in business analytics to analyze structured, quantitative data and unstructured, textual information from medical records and health care claims. Knowledge gleaned from data and text mining can assist in claims negotiations between insurers and health care providers. Related research also defines optimal treatment plans that both improve quality of care and reduce costs – insights that health care consumers welcome.
Outdated information management strategies and invalid statistics cause serious problems in investigating health outcomes and negotiating reimbursements. Predictive modeling, however, goes beyond standard regression techniques, expanding advanced analytical options for better, faster decision making. Predictive models use a variety of tools to deliver more accurate, long-range views of treatments and costs.
Predictive analytics enable better decisions
“The truth is in the data,” said Dr. Patricia Cerrito, Professor of Mathematics at the University of Louisville and author of Text Mining Techniques for Healthcare Provider Quality Determination: Methods for Rank Comparisons. “SAS’ data preprocessing tools enable a thorough investigation of complex health care claims. Predictive analytics reveal relationships between treatments and outcomes, as well as costs. Many conditions that formerly required surgery, such as ulcers, are now effectively addressed through medication, which greatly reduces costs. Under the health care bill, prescription medications can appear expensive, even when they cost less than alternative treatments.”
Many models attempt to determine who provides the highest quality health care with the best patient outcomes. Quality rankings have become critical for determining provider reimbursements. Cerrito has developed a patient severity index for use in predictive analytics models to rank provider quality more accurately. She will present the new model at the World Congress Leadership Summit on Predictive Analytics.
“SAS Analytics software is extremely versatile for investigating complex data,” Cerrito said. Using a sample of 8 million publicly available records, she compared her severity index with public models currently in use. She discovered that health care providers using current models could boost their quality rankings without actually improving care. Yet providers delivering the best care often don’t receive the level of reimbursements that reflect that quality. Cerrito believes that a comprehensive patient severity index that encompasses the entire patient record will enable accurate rankings of quality of care across providers. This will ensure that health care providers must improve quality of care to boost rankings.
The big picture
Using SAS Analytics, organizations are analyzing huge quantities of data to make discoveries, solve complex problems and deploy results such as Cerrito’s patient severity index. They are consolidating structured and unstructured information to derive more complete views that improve decision making. Cerrito commends SAS for having the best data mining algorithms and the simplest interface for managing and importing data.
"SAS has always integrated its analytic tools better than other software vendors,” said Cerrito. “We move seamlessly from SAS Text Miner to SAS® Enterprise Miner™ to combine and analyze this unstructured text with structured data. That's why we standardized on SAS for our research. No other software delivers SAS’ depth and breadth of analytic functionality.”
SAS Enterprise Miner makes quick work of data mining models with numerous variables, as in health care records and claims. It breezes through the largest databases to create accurate models that help insurers and providers negotiate fair settlements.
Using SAS predictive analytics, researchers investigating potential rationing of treatment coverage have determined that this practice will unfortunately increase mortality rates. Cerrito urges insurers and health care providers to use predictive analytics to ensure that quality is not lost to bottom-line concerns.
Patient records contain more unstructured than structured data. SAS Text Miner unearths the value otherwise hidden in textual information. Text mining can analyze the text stream of an entire patient condition or population to identify those with high mortality risk. Text mining can also spotlight health issues by automating the analysis of medical notes. For example, researchers analyzing such notes can identify conditions not explicitly documented in the patient records that are used in defining health care provider quality rankings. They can also analyze patient comments to better understand patient quality of life, which is commonly used in comparative effectiveness analyses that influence treatment rationing.
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