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The development of dentist practice profiles and management
Chinho Lin1, Chun-Mei Lin, Chienwen Hong
1Department of Industrial Management Science and Institute of Information Management, National Cheng Kung University, Tainan, Taiwan. linn@mail.ncku.edu.tw
Journal of Evaluation in Clinical Practice
|February 26, 2009
Summary
This study introduces a data mining approach to identify unusual dental practice patterns, helping payers detect fraud and improve care quality. The method clusters dentist profiles to reveal distinct practice characteristics for better claim management.
Area of Science:
- Dental Informatics
- Health Services Research
- Data Mining
Background:
- Increasing volume and sophistication of fraudulent dental claims necessitate advanced detection methods.
- Identifying unusual dental practice patterns is crucial for quality improvement and cost containment in healthcare.
Purpose of the Study:
- To develop an efficient and practical method for profiling dental practice patterns.
- To aid third-party payers in recognizing and managing novel or unusual dental practice behaviors.
- To prevent fraudulent claims and overcharges in dental healthcare.
Main Methods:
- Utilized knowledge discovery (data mining) techniques for clustering dentist profiles.
- Employed clustering based on service rate features.
- Integrated statistical and artificial neural network methodologies for knowledge extraction from dental claim databases.
Main Results:
- Clustering successfully highlighted distinct characteristics of dentists' practice patterns.
- Provided managerial guidance for third-party payers to manage diverse dentist practice patterns.
- Enabled better discrimination of suspicious claims and practice behaviors.
Conclusions:
- Successfully integrated dentist practice pattern development with knowledge discovery.
- Empowered third-party payers to identify and address unusual dental practice patterns.
- Contributed to strategies for preventing dental insurance fraud and overcharging.
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