Reengineering claims processing using probabilistic inductive learning

With health care costs in the United States skyrocketing, and $.25 of every health care dollar being spent on systems and claims administration, technological advances such as electronic claims filing are being advocated as cost-reducing measures. These improvements alone, however, will not significantly reduce costs unless they are accompanied by revisions in the entire claims processing system. This study explores the reliability and utility of probabilistic inductive learning (PrIL), a statistically enhanced decision tree algorithm, for improving the decision-making process at the New York State Workers' Compensation Board (WCB). Results indicate that the PrIL algorithm is favorably comparable to both the purely statistical and the classical decision tree methodologies, with the added advantages of easy to understand rules and user-defined reliability measures for each of those rules. Given the appropriate information regarding the relative value of correct and incorrect classification of cases in the WCB system, PrIL can be used to accurately assist in the decision making process in terms of reducing cost, predicting and enhancing quality and case outcomes in managed care practices.