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Machine learning algorithms have accurately predicted the risk of opioid overdose, which could allow physicians to combat the opioid epidemic by optimizing their resources to people at the same time. high risk, according to a study published in the journal JAMA.
The opioid epidemic in the United States has peaked in recent years: it has cost hundreds of billions of dollars in health care in the United States and has claimed the lives of more than 40,000 people in the United States in 2017. intelligence (AI) could offer some light at the end of the tunnel.
Here is what it means:Researchers have developed a machine learning tool that has been shown to be powerful in badessing the risk of overdose.
- The algorithms have made it possible to accurately identify people at high risk of opioid overdose. The algorithms have clbadified 560,000 Medicare beneficiaries into risk-based groups. And the groupings of the machine learning tool were almost entirely accurate: over 90% of the overdose episodes occurred in the high-risk group.
- The tool powered by AI has proven to be a better predictor than traditional methods. For example, the traditional method used by the Centers for Medicare and Medicaid Services (CMS) ranked 70% of people with a low-risk overdose.
The largest image:AI-based tools can help healthcare companies steer their spending on addictions where they are most needed and limit the consequences of the opioid epidemic.
- Machine learning could help healthcare companies better allocate valuable opioid prevention resources to the patients who need them most. Three-quarters of recipients had low risk of overdose. Providers and payers could develop more cost-effective opioid prevention programs by targeting resources on 25% of prescribed opioid patients with 90% overdoses.
- And targeting opioid prevention resources to high-risk patients could increase the chances of preventing costly overdoses. If opioid prevention expenditures are concentrated in high-risk populations – rather than in the general population – payers and providers may have a greater chance of preventing overdoses. And some US health care companies are already deploying AI on the front lines of the opioid epidemic: Payer Cigna, for example, uses a predictive model to badyze behavioral claims, the history of chronic disease and the behavior of pharmacists of consumers overdoses in the next month; a behavioral case manager then addresses the clients to help them change their behavior and prevent an overdose.
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