Promoting Better Pain Management Outcomes: Precision Decision Support for Opioid Prescribing
Efforts to prevent opioid addiction often include standardized dosing and supply limits for prescriptions. While beneficial at a population level, these limits have also caused concern that they may restrict appropriate opioid use by those in severe pain. Could a tool for predicting risk for individual patients help to prevent opioid dependence without overly restricting access to medication?
With grant support from NIHCM Foundation, researchers at the University of Maryland have used big data and machine learning to identify risk factors for chronic opioid use. Using those risk factors, they developed precision decision-support tools for prescribing physicians. On this webinar, the researchers discussed their work, its applications, and next steps for bringing these tools into practice.
The discussion covered:
- the accuracy of state-of-the-art methods for predicting risk of chronic opioid use
- what a streamlined, easy-to-administer screening tool would look like
- the limitations of a decision-support system for opioid prescribing
- the economic costs and benefits of using such a system
This webinar is a project of the Center for Health Information and Decision Systems at the University of Maryland’s Robert H. Smith School of Business. Support for the featured research was provided through NIHCM Foundation’s Investigator-Initiated Research Grant Program.