Source: MirageNews | September , 2019
Project bridges compute staff, resources at ORNL and VA health data to speed suicide risk screening for US veterans
More than 6,000 veterans died by suicide in 2016, and from 2005 to 2016, the rate of veteran suicides in the United States increased by more than 25 percent.
Suicide prevention is the US Department of Veterans Affairs‘ (VA’s) highest priority-so much so that in recent years, the VA has started using predictive models and advanced informatics to identify at-risk veterans. One such model, called the medication possession ratio algorithm, creates individualized summaries of veterans’ medication patterns, such as which medications a veteran is prescribed and how often those prescriptions are filled. The model helps clinicians pinpoint veterans with inconsistent medication usage patterns-these veterans are known to have a higher risk of attempting suicide in the next month.
In a collaborative project with the VA, a team at the US Department of Energy‘s (DOE’s) Oak Ridge National Laboratory (ORNL) has taken the model and engineered the expanded version of it to run 300 times faster, gaining an unprecedented speedup that might have a profound effect on the VA’s ability to reach susceptible veterans quickly.
“Veterans should be following the medication regimens prescribed by their doctors,” said Edmon Begoli, principal investigator on the project and director of the Scalable Protected Data Facilities (SPDF) at the National Center for Computational Sciences at ORNL. “If they are attempting to increase certain medications, they could be at risk of substance dependency, and if they stop taking medications like antidepressants or opioids, it could put them at risk of withdrawal or other adverse effects that could, in the worst case, lead to suicide.”
Until now, the medication possession ratio calculations have been limited in scope. The model has typically included only active psychotropic medications, such as narcotics or mental health medications, and covered a narrow class of the total veteran population in the Veterans Health Administration (VHA) database. This simplified version of the algorithm took 4 hours to run on the VA’s resources.