2010 DataMiningtoPredictandPreventEr

• (Kumar et al., 2010) ⇒ Mohit Kumar, Rayid Ghani, and Zhu-Song Mei. (2010). “Data Mining to Predict and Prevent Errors in Health Insurance Claims Processing.” In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2010). doi:10.1145/1835804.1835816

Subject Headings:

Notes

• Categories and Subject Descriptors: H.2.8 Database Management: Database Applications — Data Mining; H.4.2 Information Systems Applications: Types Of Systems — Decision Support.
• General Terms: Design, Experimentation, Performance

Cited By

• http://scholar.google.com/scholar?q=%22Data+mining+to+predict+and+prevent+errors+in+health+insurance+claims+processing%22+2010
• http://portal.acm.org/citation.cfm?id=1835816&preflayout=flat#citedby

Quotes

Author Keywords

Health insurance claims, Claim rework identification, Predictive system, Machine Learning.

Abstract

Health insurance costs across the world have increased alarmingly in recent years. A major cause of this increase are payment errors made by the insurance companies while processing claims. These errors often result in extra administrative effort to re-process (or rework) the claim which accounts for up to 30% of the administrative staff in a typical health insurer. We describe a system that helps reduce these errors using machine learning techniques by predicting claims that will need to be reworked, generating explanations to help the auditors correct these claims, and experiment with feature selection, concept drift, and active learning to collect feedback from the auditors to improve over time. We describe our framework, problem formulation, evaluation metrics, and experimental results on claims data from a large US health insurer. We show that our system results in an order of magnitude better precision (hit rate) over existing approaches which is accurate enough to potentially result in over $15-25 million in savings for a typical insurer. We also describe interesting research problems in this domain as well as design choices made to make system easily deployable across health insurance companies.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2010 DataMiningtoPredictandPreventEr	Rayid Ghani Mohit Kumar Zhu-Song Mei			Data Mining to Predict and Prevent Errors in Health Insurance Claims Processing		KDD-2010 Proceedings		10.1145/1835804.1835816		2010