| Summary: | 碩士 === 東海大學 === 工業工程學系 === 90 === A Study for Developing the Clinical Path
— The Data Mining Approach
Student: Shu-Fen Lee Advisor: Dr. Wei-Hua Andrew Wang
Institute of Industrial Engineering and Enterprise Information
Tunghai University
Abstract
Due to the increasing medical practicing cost, the Bureau of National Health Insurance (BNHI) decided to implement the concept of Prospective Payment System (PPS) to help the medical hospitals in planning and controlling their medical care cost and quality. Several payment system have been implemented and are scheduling to be implemented in these one or two years. These systems are trying to constraint the hospitals’ incomes and in a way to accelerate the cost and quality control efforts in the hospitals. In the PPS concept, the medical process design and control is a key way to guarantee the efforts and the results. Clinical path is one of the most important tool in making PPS functional and helping the BNHI and the hospitals to reach the expected goals.
Traditional way in developing clinical paths is to organize some related medical teams. And, within the teams, the related medical experiences could be integrated and hopefully reach a common agreement in the medical treatments procedure. After that, some general clinical paths could be formed. However, this way wastes lots of time and energy in negotiations and argues and can hardly reach the medical practice agreements. In this research, a data mining approach is developed to provide a new way in developing the first and common-agreed, in the sense of data, blueprint of clinical path. A common blueprint is provided for the members in the medical teams to design the appropriate clinical paths for the patients.
In this research, two difference examples have been selected as the test bed for the proposed method. One is the Caesarian operation, which is a case payment example. The other is the Pneumonia of Pediatrics in which no clinical path has been reported before. We have implemented the proposed method in these two examples and received the satisfied results. In the Caesarian operation, a clinical path is generated and the accurate rate of ANN classifier is above 98%. In the Pneumonia of Pediatrics example, two clinical paths are generated and the accurate rate of ANN classifier is above 77%. In the above examples, the generated clinical pathways have been approved by the domain experts as well.
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