| 摘要 |
A computer implemented technique, including database processing, is used to identify at risk congestive heart failure patientes where information about patients exists in a claims database. The technique includes processing the patient information in the claims database to find and extract claims information for a group of congestive heart failure patients. Next, using the extracted information, a set of events, relevant to congestive heart failure, is defined. Next, the extracted information and set of events are processed to create event level information which is organized with respect to events rauner than claims. A time windows is defined to provide a timeframe from which to judge whether events should be considered in subsequent processing; and a set of variables is defined as being potential predictors of adverse healt outcomes. Subsequently, the event level information, using the time window and the set of variables, is processed to generate an analysis file. Statistical analysis, such as logistic regression, is performed on the analysis file to generate a prediction model where the prediction model is function of a subset of the set of variables. Finally the prediction model is applied to the same or another claims database to identify and output at risk patients, diagnosed with congestive heart failure, likely to have adverse health outcomes.
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