January 2006
Sun Mon Tue Wed Thu Fri Sat
1 2 3 4 5 6 7
8 9 10 11 12 13 14
15 16 17 18 19 20 21
22 23 24 25 26 27 28
29 30 31        

Authors' Committee

Chair:

Matt Blackwell (Gov)

Members:

Martin Andersen (HealthPol)
Kevin Bartz (Stats)
Deirdre Bloome (Social Policy)
Andy Eggers (Gov)
John Graves (HealthPol)
Rich Nielsen (Gov)
Maya Sen (Gov)
Gary King (Gov)

Weekly Research Workshop Sponsors

Alberto Abadie, Lee Fleming, Adam Glynn, Guido Imbens, Gary King, Arthur Spirling, Jamie Robins, Don Rubin, Chris Winship

Recent Comments

Recent Entries

Categories

Blogroll

Brad DeLong
Cognitive Daily
Complexity & Social Networks
Developing Intelligence
EconLog
The Education Wonks
Empirical Legal Studies
Free Exchange
Freakonomics
Health Care Economist
Junk Charts
Language Log
Law & Econ Prof Blog
Machine Learning (Theory)
Marginal Revolution
Mixing Memory
Mystery Pollster
New Economist
Political Arithmetik
Political Science Methods
Pure Pedantry
Science & Law Blog
Simon Jackman
Social Science++
Statistical modeling, causal inference, and social science

Archives

Notification

Powered by
Movable Type 4.24-en


« January 16, 2006 | Main | January 18, 2006 »

17 January 2006

Network Analysis and Detection of Health Care Fraud

You, Jong-Sung

In my earlier entries on “Statistics and Detection of Corruption� and “Missing Women and Sex-Selective Abortion,� I demonstrated that examination of statistical anomaly can be a useful tool for detection of crime and corruption. In these cases, binomial probability distribution was a very useful tool.

Professor Malcolm Sparrow at the Kennedy School of Government shows how network analysis can be used to detect health care fraud in his book, License to Steal: How Fraud Bleeds America's Health Care System (2000). He gives an example of network analysis performed within Blue Cross/Blue Shield of Florida in 1993.

An analyst explored the network of patient-provider relationships with twenty-one months of Medicare data, treating a patient as linked to a provider if the patient had received services during the twenty-one-month period. The resulting patient-provider network had 188,403 links within it. The analyst then looked for unnaturally dense cliques within that structure. He found a massive one. “At its densest core, the cluster consisted of a specific set of 122 providers, linked to a specific set of 181 beneficiaries. The (symmetric) density criteria between these sets were as follows:
A. Any one of these 122 providers was linked with (i.e., had billed for services for) a minimum of 47 of these 181 patients.
B. Any one of these 181 patients was linked with (i.e., had been “serviced� by) a minimum of 47, and an average of about 80, of these providers.�
After the analyst found this unnaturally dense clique, field investigations confirmed a variety of illegal practices. “Some providers were indeed using the lists of patients for billing purposes without seeing the patients. Other patients were being paid cash to ride a bus from clinic to clinic and receive unnecessary tests, all of which were then billed to Medicare.�

Professor Sparrow suggests that many ideas and concepts from network analysis can be useful in developing fraud-detection tools, in particular for monitoring organized and collusive multiparty frauds and conspiracies.

Posted by Jong-sung You at 2:36 AM