Using Data and Machine Learning to Inform a Targeted Opioid Abuse/Misuse Reduction Strategy
Medicaid Enterprise Systems Conference
August 14, 2018 | 1:00pm | Portland, OR
Join Eric Widen, Co-founder and CEO of HBI Solutions and Jessica Knott, FEI Systems for a session exploring how you can use data and machine learning to address the continuously rising opioid abuse/misuse epidemic in the USA.
In 2013, opioid addiction affected 889 per 100,000 Medicaid beneficiaries; by 2015, 3 in 10. In the USA, opioid addiction is now the #1 cause of death for individuals under 50 and #10 overall. Dual eligible beneficiaries’ prescription drugs are largely covered by Medicare Part D; therefore, it is essential for states to acquire and use Prescription Drug Event (PDE) data to identify potential opioid misuse and harmful prescribing practices when examining this population. As an example of the analytics that can be completed, using 2014 Medicare PDE data, FEI Systems examined opioid fill and provider prescribing patterns, which can be used to identify dual eligible beneficiaries who may be at risk of opioid misuse. HBI Solutions shows how an early warning system (EWS) that applies predictive algorithms on integrated clinical, administrative and social determinant data can be an effective tool in identifying patients most at risk for opioid abuse or those in an abusive state who have not yet been diagnosed. HBI will show how machine learning better identifies those individuals at risk and reveals important risk factors within a user workflow to better integrate behavior and medical management, drive risk mitigation and prevention.