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MEDai’s Predictive Modeling Makes Strides in TPA and Stop-Loss Markets Orlando, Fla. (March 28, 2005) - MEDai, Inc., headquartered in Orlando, FL, a leading provider of innovative healthcare solutions, announced earlier this week that TPABenefits, Inc. headquartered in San Antonio, TX has signed a contract to integrate MEDai’s Risk Navigator Clinical predictive modeling solution in their current product suite. TPABenefits develops web based applications specifically designed for Third Party Administrators (TPA’s), payors, HMO’s and stop/loss carriers. This agreement will allow TPABenefits clients’ to integrate the information from the predictive models into their daily care management efforts. In addition, the models will allow stop/loss carriers to accurately assess the risk for small group employers resulting in more competitive rates.
“TPABenefits has designed a web front end that will give our clients seamless access to the actionable information MEDai provides within their models,” said James Burkholder, founder of TPABenefits, Inc. “Employers and stop-loss carriers alike will now have a more accurate way of looking at aggregate claims data and proactive managing those covered lives,” he added. “MEDai is very excited about this partnership,” said Steve Epstein, President and CEO at MEDai, Inc. “For years healthplans have benefited from the accuracy of our modeling methodology. This relationship will now extend that benefit further into the third-party and stop-loss markets,” he added. About
TPABenefits, Inc. About
MEDai |
MEDai’s
Risk Navigator Clinical accurately forecasts prospective high-risk
patients to improve patients’ health and quality of care
while controlling cost. The advantage of MEDai’s prediction
engine is its use of the right combination of linear and non-linear
models to correctly “fit” the characteristics of the
client’s population. Without eliminating outliers—the
very events clients want to predict—Risk Navigator Clinical
consistently yields the highest accuracy available as evidenced
by their performance in The Association for Computing Machinery’s
Knowledge Discovery and Data Mining (ACM KDD) competition. 