Operations Research
Probabilistic Networks and Expert Systems
Probabilistic Networks and Expert Systems
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To effectively control the growth of medical expenditure, Bureau of National Health Insurance (NHI) of Taiwan has taken many measures, including the Reasonable Number of Outpatient Services, Ceiling Price, Global Budgets, Strategic Analysis and the Excellence Plan; however, these measures can only scratch the surface. Due to the change of life style and the deteriorating condition of over-nutrition and obesity, people now have a higher risk of diabetes, hypertension, hyperlipidemia, cardiovascular disease, gallbladder disease, cancer, gout, arthritis, and so on, which leads to higher medical expenditure. Therefore, good civil preventive health care is regarded as the solution of surging medical expenditure. According to NHI's statistics, the annual medical expenditure of diabetes is about 13 billion NT dollars. Among these diabetics, over 95% are affected by type 2 diabetes mellitus; at least two-thirds--over 80% according to some researches--are overweight or obese. The research says, losing 5% to 10% of the original body weight can lower the risk of chronic diseases effectively; also, giving early therapy for obesity can reduce the complication probability, thus for avoiding the waste of medical resources. By applying influence diagrams of Bayesian Network and Utility Expect of statistics, this paper evaluates the medical expenditure of Taiwan's NHI under the circumstances of providing and not providing benefit for weight-loss outpatient services. The result of this research is that the cost of not providing benefit for weight-loss outpatient services is 3.4 times of the contrary. Therefore, if Taiwan's NHI provides reasonable benefit for weight-loss outpatient services, not only the risk of people suffering from diabetes, hypertension, hyperlipidemia, cardiovascular disease, gallbladder disease, cancer, gout, arthritis, etc. will go down; but also the medical expenditure can be effectively reduced.