2011 InteractiveLearningforEfficient

• (Ghani & Kumar, 2011) ⇒ Rayid Ghani, and Mohit Kumar. (2011). “Interactive Learning for Efficiently Detecting Errors in Insurance Claims.” In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2011) Journal. ISBN:978-1-4503-0813-7 doi:10.1145/2020408.2020463

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222011%22+Interactive+Learning+for+Efficiently+Detecting+Errors+in+Insurance+Claims
• http://dl.acm.org/citation.cfm?id=2020408.2020463&preflayout=flat#citedby

Quotes

Author Keywords

• Algorithms; budgeted learning; cost sensitive learning; data mining; error detection; health insurance claims; interactive machine learning; measurement; performance; quality control

Abstract

Many practical data mining systems such as those for fraud detection and surveillance deal with building classifiers that are not autonomous but part of a larger interactive system with an expert in the loop. The goal of these systems is not just to maximize the performance of the classifier but to make the experts more efficient at performing their task, thus maximizing the overall Return on Investment of the system. This paper describes an interactive system for detecting payment errors in insurance claims with claim auditors in the loop. We describe an interactive claims prioritization component that uses an online cost-sensitive learning approach (more-like-this) to make the system efficient. Our interactive prioritization component is built on top of a batch classifier that has been trained to detect payment errors in health insurance claims and optimizes the interaction between the classifier and the domain experts who are consuming the results of this system. The goal is to make these auditors more efficient and effective as well as improving the classification performance of the system. The result is both a reduction in time it takes for the auditors to review and label claims as well as improving the precision of the system in finding payment errors. We show results obtained from applying this system at two major US health insurance companies indicating significant reduction in claim audit costs and potential savings of $20-$26 million / year making the insurance providers more efficient and lowering their operating costs. Our system reduces the money being wasted by providers and insurers dealing with incorrectly processed claims and makes the healthcare system more efficient.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2011 InteractiveLearningforEfficient	Rayid Ghani Mohit Kumar			Interactive Learning for Efficiently Detecting Errors in Insurance Claims				10.1145/2020408.2020463		2011