Authors:
Fernando Boccanera
and
Alexander Brodsky
Affiliation:
George Mason University, United States
Keyword(s):
Recommender, Personalization, Health Insurance Plan Choice, Decision Support Systems, Pareto Optimality.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Case-Based Reasoning
;
Enterprise Information Systems
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Pattern Recognition
;
Strategic Decision Support Systems
;
Symbolic Systems
;
Theory and Methods
;
User Profiling and Recommender Systems
Abstract:
Choosing a health insurance plan, even when the plans are standardized, is a daunting task. Research has shown that the complexity of the task leads consumers to make non-optimal choices most of the time. While a number of systems were introduced to assist the selection of health insurance plans, they fail to significantly reduce the main causes of poor decisions. To address this problem, this paper proposes OptiHealth, a recommender framework for Pareto optimal selection of health insurance plans. The proposed framework is based on (1) actuarial analysis of medical data and a method to accurately estimate the expected annual cost tailored to specific individuals, (2) finding and presenting a small number of diversified Pareto optimal plans based on key performance indicators, and (3) allowing decision makers to iteratively conduct a trade-off analysis.