Machine learning could help curb opioid overdoses
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Machine learning algorithms have proven to accurately predict opioid overdose risk, which could allow doctors to tackle the opioid epidemic by honing their resources to high-risk people, according to research published in JAMA.
Here's what it means: Researchers developed a machine learning tool that proved to be powerful in assessing overdose risk.
- The algorithms accurately identified individuals at high risk of an opioid overdose. The algorithms sorted 560,000 Medicare beneficiaries into risk-based groups. And the machine learning tool's groupings were almost entirely accurate: More than 90% of overdose episodes occurred in the high-risk group.
- The AI-powered tool proved a better predictor than traditional methods. For example, the traditional method used by the Centers for Medicare and Medicaid Services (CMS) classified 70% of people who experienced an overdose as low-risk.
The bigger picture: AI-powered tools can help health firms to channel their drug abuse spending where it's needed most and curb the opioid epidemic's consequences.
- Machine learning could guide health firms to better allocate precious opioid prevention resources to patients who need them most. Three-quarters of beneficiaries were at low risk of experiencing an overdose. Providers and payers could develop more cost-efficient opioid prevention programs by targeting resources at the 25% of patients prescribed opioids who suffer 90% of overdoses.
- And targeting opioid prevention resources at high-risk patients could increase the likelihood of preventing costly overdoses. If opioid prevention spending is concentrated among high-risk populations - versus spread out across the population - payers and providers may have a higher chance of preventing overdoses. And some US health firms are already deploying AI on the front lines of the opioid epidemic: Payer Cigna, for example, uses a predictive model to comb through customers' behavioral health claims, chronic disease history, and pharmacy behavior to flag consumers who are likely to overdose within the next month; a behavioral case manager then reaches out to the customers to help change their behavior and preempt an overdose.
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