Soon after its largest-ever bust of healthcare providers involved in the fraudulent distribution of opioids, the Department of Justice announced a new Opioid Fraud and Abuse Detection Unit that draws on healthcare data analytics to find misuse of controlled substances.
The task force will focus specifically on slashing fraud originating in the healthcare system, with 12 prosecutors overseeing the following regions that have been hit hard by the ongoing opioid epidemic:
- Middle District of Florida,
- Eastern District of Michigan,
- Northern District of Alabama,
- Eastern District of Tennessee,
- District of Nevada,
- Eastern District of Kentucky,
- District of Maryland,
- Western District of Pennsylvania,
- Southern District of Ohio,
- Eastern District of California,
- Middle District of North Carolina,
- Southern District of West Virginia.
U.S. Attorney General Jeff Sessions announced the formation of the new unit, noting that opioid overdoses and related health problems took the lives of close to 60,000 Americans last year.
“I am announcing a new data analytics program — the Opioid Fraud and Abuse Detection Unit,” Sessions said in a statement. “I have created this unit to focus specifically on opioid-related healthcare fraud using data to identify and prosecute individuals that are contributing to this opioid epidemic. This sort of data analytics team can tell us important information about prescription opioids — like which physicians are writing opioid prescriptions at a rate that far exceeds their peers; how many of a doctor’s patients died within 60 days of an opioid prescription; the average age of the patients receiving these prescriptions; pharmacies that are dispensing disproportionately large amounts of opioids; and regional hot spots for opioid issues.”
Even though every state has an electronic prescription drug tracking system, Sessions said collecting and using opioid-related data to reduce overdoses, limit access, and prevent addiction continues to be difficult due to data silos and jurisdiction limits, combined with poor interoperability and fragmented data collection.