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SunGard report depicts the new role of Predictive Technology in medical debt recovery Print E-mail
Thursday, 26 June 2014

The American Hospital Association estimates that U.S. hospitals provided $45.9 billion in uncompensated care in 2012, representing 6.1 percent of annual hospital expenses. With increasing regulations and tighter margins forward-thinking medical providers are looking “outside the box” for a solution. For that reason, medical debt collectors are now engaging in the use of predictive modeling to help identify which accounts have the highest propensity to pay.

A recent paper published by SunGard shows how predictive models can be the key to more successful medical debt collections.

The methodology helps healthcare providers develop a more targeted, effective and patient-friendly collections strategy. The model generates a score based on millions of medical payment observations blended with socioeconomic and demographic data. This produces predictions of which patients are most likely to pay, which then enables medical debt collections efforts to be targeted to make the most profitable use of time and resources.

“The models are particularly attractive because they do not require bureau data or personally identifiable information,” explains Dwayne Banasiak, vice president, SunGard’s Predictive Metrics.  “Concerns about violating patient privacy or permissible purpose rules are alleviated.  In our paper:  How Statistical Models Can Help Navigate the Future of Medical Debt Collections, we offer a more in-depth view of how these models enables collectors to not only reduce those concerns, but also to develop more effective collections strategies.”

(Source - SunGard Press Release)  


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