There has been extensive documentation of the current opioid epidemic in the United States. Despite the attention of media, government, and the healthcare industry, the problem continues to get worse. The Centers for Disease Control and Prevention (CDC) estimates that, in 2016, 42,000 people died of opioid overdoses in the U.S. (a 14.4% rise from the previous year); and 40% of those overdoses were the result of prescription opioids. More than 11 million people abused prescription opioids in 2016; and only 20% of patients with opioid misuse disorders are in treatment programs and not all of them are in programs that follow the best evidence-based guidelines.
These statistics are staggering, and yet they don’t tell the whole story. Almost everyone in the country has been touched in some personal way by this epidemic (overdose death of family or friends, family or societal damage from addiction, crime, etc.). In addition to the social costs, opioid misuse results in significant economic impacts, including criminal justice costs, expenses related to inappropriately prescribed opioid drugs, medical costs wasted in drug-seeking behavior or to treat addiction, and lost economic productivity as a result of overdoses and addiction.
To date there has been insufficient progress in battling this epidemic; but new opportunities to combat opioid abuse are on the horizon. Data and technology tools exist that can be harnessed to increase the ability of healthcare payers to help control both the human and economic costs.
Early identification of patients at risk of opioid misuse disorders allows payers and providers to engage proactively in education and effective treatment. Barriers to early identification include information silos, poor delivery of actionable information to front line healthcare workers (prescribers and case managers), insufficient resources for education, and directing evidence-based treatment.
Overcoming data barriers is achievable by promoting data sharing within consortiums of all payers in a geographic area. This allows tracking and managing of high-risk members and prescribers as they move through various health plans and coverage types. Such data sharing can enable a comprehensive understanding of health plan members and their potential opioid risk factors as early as upon their enrollment in a plan.
Advanced analytics engines are able to process multiple data streams and prioritize patients by assessing opioid misuse risk factors. This is far superior to standard pharmacy reporting available to most payers. These advanced analytics are most useful when translated into actionable information for those who work directly with high-risk members. A key component of making data actionable is a software platform that is accessible to front line care managers and prescribers. Platforms that present prioritized lists of members who might need additional care or outreach for potential opioid issues puts focus where it’s needed and best leverages scarce resources. Analytics can also identify prescribers who do not follow evidence based prescribing recommendations, allowing appropriate prescriber education and monitoring.
How does this work? Previous claims data pre-populates Health Risk Assessment documents with opioid misuse risk factors, increasing the ability of care managers to work with members to explore their risk. Data-driven member engagement and outreach efforts use analytics to segment populations and drive targeted, multi-modal outreach messaging, utilizing the most effective communication method for the individual member. Specific member engagement might include providing opioid education for those about to undergo surgical procedures, administering opioid risk screening tools, following-up on member adherence to chronic pain treatment plans, or providing medication-assisted opioid treatment plans.
The opioid epidemic remains one of the country’s most challenging healthcare problems, but one that is not insurmountable. Healthcare payers have the opportunity to positively affect the human suffering and economic costs of the epidemic by applying existing data, analytics and technology solutions to this challenge.