Abstract: According to one example embodiment, there is provided a method and an apparatus to evaluate the credit of a healthcare patient. The example embodiment provides methods and computer systems programmed to use multiple variables that are known about a patient prior to a service being rendered to segment the patient population into finer grained groupings. These finer grained groupings allow financial factors, such as a credit score, to be a more accurate predictor. Also, according to another example embodiment, the model is not a generic model for all patients, but the variables and their parameters are specific to a particular healthcare organization's or facility's patient population. This creates a custom model that further enhances its predictiveness.

Abstract: According to one example embodiment, there is provided a method and an apparatus to evaluate the credit of a healthcare patient. The example embodiment provides methods and computer systems programmed to use multiple variables that are known about a patient prior to a service being rendered to segment the patient population into finer grained groupings. These finer grained groupings allow financial factors, such as a credit score, to be a more accurate predictor. Also, according to another example embodiment, the model is not a generic model for all patients, but the variables and their parameters are specific to a particular healthcare organization's or facility's patient population. This creates a custom model that further enhances its predictiveness.

Abstract: According to one example embodiment, there is provided a method and an apparatus to evaluate the credit of a healthcare patient. The example embodiment provides methods and computer systems programmed to use multiple variables that are known about a patient prior to a service being rendered to segment the patient population into finer grained groupings. These finer grained groupings allow financial factors, such as a credit score, to be a more accurate predictor. Also, according to another example embodiment, the model is not a generic model for all patients, but the variables and their parameters are specific to a particular healthcare organization's or facility's patient population. This creates a custom model that further enhances its predictiveness.