What Does Opioid Use Disorder Treatment Fraud Look Like?

What Does Opioid Use Disorder Treatment Fraud Look Like?

Publication date: Apr 11, 2019

Insurers may not be able to look across national data to find the telltale signs of this kind of fraud, but School of Public Health researchers can, with help from a top former federal healthcare regulator, a Washington, DC-based law and consulting firm, and some undercover -mystery shoppers. “

Together with support from the Laura and John Arnold Foundation, part of Arnold Ventures, four professors from the Department of Health Law, Policy & Management are working on a two-year project to identify insurance claims patterns that point to substance use disorder treatment scams; they will then build a tool for insurers and regulators to understand just how big the problem is, and how to stop it.

-This problem affects everybody,” says Melissa Garrido, research associate professor of health law, policy & management, and the lead researcher on the study.

In this phase, they are also interviewing key stakeholders-people working in regulation, law enforcement, and opioid use disorder treatment-in Florida, California, Texas, New York, and Pennsylvania; all are states with areas of high per-capita opioid use disorder rates and large numbers of opioid treatment programs.

Steven Pizer, associate professor of health law, policy & management and PEPReC’s chief economist, will then work with Garrido on the claims analysis and developing the algorithm.

Health economist Austin Frakt, associate professor of health law, policy & management, director of PEPReC, and a writer who regularly contributes to the New York Times’s Upshot column, will work with the team to disseminate the research findings to a broader audience.

Concepts Keywords
Algorithm Crimes
Blood Finance
Boston Articles
California Economy
Economist Point substance disorder
Florida Capita opioid disorder
Fraud Hard opioid disorder
Healthcare Milk insurance
Insurance Combination media
Management Director Colleagues insurance
Milk Federal healthcare regulator
Mystery Shoppers Balance insurers
Mystery Shopping Detection insurance
Opioid Property crimes
Outliers Sackler family
Overdose Insurance
Pennsylvania Drug overdose
Red Flag Fraud
Texas
Urinalysis
Urine
Washington DC

Semantics

Type Source Name
gene UNIPROT ACTG2
gene UNIPROT ACOT7
gene UNIPROT LARGE1
gene UNIPROT BEST1
gene UNIPROT DYRK3
gene UNIPROT IK
gene UNIPROT LAT2
disease MESH multiple
drug DRUGBANK Isoxaflutole
gene UNIPROT SMIM10L2A
gene UNIPROT SMIM10L2B
gene UNIPROT SET
gene UNIPROT POTEM
gene UNIPROT FHL5
gene UNIPROT ACTG1
gene UNIPROT SERPINA3
gene UNIPROT ACTBL2
gene UNIPROT MAP6
disease MESH substance use disorder
gene UNIPROT THOP1

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