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Activity Number: 564 - Statistical Integrity of Health Services: Detecting Fraud and Improving Data Quality
Type: Topic Contributed
Date/Time: Wednesday, August 2, 2017 : 2:00 PM to 3:50 PM
Sponsor: Health Policy Statistics Section
Abstract #323569
Title: Network Assortativity of Physicians Who Account for Extreme Amounts of Medicare Spending
Author(s): James O'Malley* and Thomas Bubolz and Jonathan Skinner
Companies: Geisel School of Medicine at Dartmouth and Geisel School of Medicine at Dartmouth and Geisel School of Medicine at Dartmouth
Keywords: Assortativity ; Diffusion ; Fraud ; Medicare ; Peer effect ; Social network
Abstract:

Little is known about the diffusion of potentially fraudulent or "gray area" activities in medicine and whether it transmits through physician networks, making the identification of fraudulent activity through networks one approach to improving the quality of care while reducing costs dramatically. We develop methods to assess whether: 1) physicians who are the highest billers of Medicare preferentially share patients; 2) non-outlier physician with strong connections with outlier physicians are more likely to become outliers. We use 100% of national Medicare claims data from 2004-2014 to identify two specific types of services with unusual Medicare billing activity: home health care services (HHC) and durable medical equipment (DME). For each treatment, we build a network of the physicians in which edges in the network quantify the number of patients treated by each pair of physicians. We investigate whether unadjusted positive assortativity holds up against competing explanations and estimate a peer effects regression model to gauge whether there is evidence of physician-to-physician diffusion in the network. We also test whether physicians were outliers in both treatments.


Authors who are presenting talks have a * after their name.

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