Data shines light on opioid victims, potential solutions
- By Matt Leonard
- Jun 20, 2018
As the opioid epidemic continues to ravage communities across the country, health and policy experts are beginning to look at more granular information to try and understand who is most at risk and how the flow of opioids can be slowed.
The Agency for Healthcare Research and Quality has been analyzing data on changes in the nation’s health care system and found “this crisis is not the same across the country," AHRQ's Chief Medical Officer David Meyers said at a June 20 event on the opioid epidemic hosted by The Hill.
It may be a surprise to learn that in most states, the most common demographic hospitalized for opioid overdoses is women, he said. Another unexpected finding from the data is the increase in the number of people over the age of 65 that struggle with opioid dependency.
“Those kinds of statistics, those kinds of data, when given to state policy makers, and more importantly even to local policy makers, help them make decisions about what’s happening in their communities,” Meyers said.
West Virginia is at the center of the opioid epidemic. According to the most recent statistics available, the state had the highest rate of deaths associated with prescription opioids in 2016. This led to the state conducting a "social autopsy" in which officials examined every death in 2016 caused by an overdose, according to the state's Bureau for Public Health Commissioner Rahul Gupta.
This effort filtered the data by deceased's employment, education, marital and incarceration history along with whether they had visited the doctor recently.
“We combed through hundreds and thousands of these records and came up with some very interesting findings,” Gupta said.
The data showed that prescription drugs are still playing a large role is overdose deaths, that only a third of people who died received a Naloxone injection to try to reverse the overdose and over half had a history of incarceration. These findings informed policy that garnered bipartisan support in the state legislature. The new law limits the prescribing of opioids and makes it possible for every first responder to carry Naloxone, he said.
Using artificial intelligence to identify trends in these datasets could help officials spot problem areas sooner, suggested Rep. Tom MacArthur (R-N.J.).
“There is an enormous amount of data already available" related to opioid prescriptions in the Medicare and private insurance claims systems, MacArthur said. "I want to try to get a coordinated concentration of all of that data so that doctors can get alert -- red alerts -- about their patients, without violating privacy.” he said.
MacArthur saw a similar, smaller system at Hartford Insurance offices. There, he said, the company uses AI to analyze conversations between staff and claimants to spot potential “red flags.” If a problem is detected, a team of nurses and doctors can be called to intervene.
Although another data-driven tool to spot drug abuse, Prescription Drug Monitoring Programs, have been implemented across the country, they have been criticized by physicians because the data entry takes time away from a patient visit, according to Nassima Ait Daoud Tiouririne, an associate professor of psychiatry and neurobehavioral sciences at University of Virginia Health System. But in the UVA Health System, PDMP “is now embedded with electronic medical records, so when I pull the medical records to check anything, the PDMP is there, and it's a click away,” she said.
Matt Leonard is a reporter/producer at GCN.
Before joining GCN, Leonard worked as a local reporter for The Smithfield Times in southeastern Virginia. In his time there he wrote about town council meetings, local crime and what to do if a beaver dam floods your back yard. Over the last few years, he has spent time at The Commonwealth Times, The Denver Post and WTVR-CBS 6. He is a graduate of Virginia Commonwealth University, where he received the faculty award for print and online journalism.
Leonard can be contacted at firstname.lastname@example.org or follow him on Twitter @Matt_Lnrd.
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