Suchergebnisse

It is estimated that between $600 and $850 billion annually is lost to fraud, waste, and abuse in the US healthcare system,with $125 to $175 billion of this due to fraudulent activity (Kelley 2009). Medicaid, a state-run, federally-matchedgovernment program which accounts for roughly one-quarter of all healthcare expenses in the US, has been particularlysusceptible targets for fraud in recent years. With escalating overall healthcare costs, payers, especially government-runprograms, must seek savings throughout the system to maintain reasonable quality of care standards. As such, the need foreffective fraud detection and prevention is critical. Electronic fraud detection systems are widely used in the insurance,telecommunications, and financial sectors. What lessons can be learned from these efforts and applied to improve frauddetection in the Medicaid health care program? In this paper, we conduct a systematic literature study to analyze theapplicability of existing electronic fraud detection techniques in similar industries to the US Medicaid program.

Fraud, waste, and abuse in the U.S. healthcare system are estimated at $700 billion annually. Predictive analytics offers government and private payers the opportunity to identify and prevent or recover such billings. This paper proposes a data-driven method for fraud detection based on comparative research, fraud cases, and literature review. Unsupervised data mining techniques such as outlier detection are suggested as effective predictors for fraud. Based on a multi-dimensional data model developed for Medicaid claim data, specific metrics for dental providers were developed and evaluated in analytical experiments using outlier detection applied to claim, provider, and patient data in a state Medicaid program. The proposed methodology enabled successful identification of fraudulent activity, with 12 of the top 17 suspicious providers (71%) referred to officials for investigation with clearly anomalous and inappropriate activity. Future research is underway to extend the method to other specialties and enable its use by fraud analysts.