In 2017, the US Justice Department brought charges against dozens of health care professionals for writing over 350,000 illegal prescriptions for painkillers in Kentucky, Alabama, Ohio, Tennessee and West Virginia by analyzing prescription and billing data from state and federal sources. These data sources included Having access to opioid “big data” means faster and more reliable methods of building evidence to prosecute individuals and companies who are crossing the line and endangering the public.
According to a recently published article in the Wall Street Journal, a data scientist with a PhD leads a team at the Justice Department to mine the data that can be used to fuel traditional law enforcement practices such as undercover sting operations and identifying informants who can help gather further evidence. The team worked to charge more than 300 people (totaling $2 million) in 2018 – with more cases to come.
The use of big data to understand trends and monitor risk in opioid addiction is only going to expand. In July, the Washington Post made public on its website a dataset with information from the US Drug Enforcement Agency (DEA) on the sale of pain pills. The US Centers for Disease Control and Prevention (CDC) produces data maps that show the geographic distribution of opioid prescriptions by year. The application of artificial intelligence and machine learning techniques will only fuel this work, as expanding data sets can be rapidly analyzed.