A University of Maryland-led research team will give key insights and next steps in an effort using big data and machine learning to target a U.S. opioid epidemic that claimed 42,000-plus lives in 2016.
The discussion will be via a 3-4 p.m. Tuesday, April 10 webinar, "Promoting Better Pain Management Outcomes: Precision Decision Support for Opioid Prescribing." It’s hosted by the Center for Health Information and Decision Systems (CHIDS) at the University of Maryland’s Robert H. Smith School of Business. Register online.
The webinar will address the epidemic in terms of it driving an ongoing debate about how to prevent chronic opioid use and downstream adverse outcomes like addiction, while not needlessly causing patients to endure severe pain through rationing appropriate use.
The researchers say their findings, from a year-long study supported by the National Institute for Health Care Management, support an approach that involves “more precise risk prediction to both prevent negative outcomes and not overly restrict access to medication.”
The researchers have developed simple scoring tools for such risk prediction and will discuss these in the webinar that’s structured according to the following subtopics:
- The accuracy of state-of-the-art prediction methods for chronic opioid use risk
- The minimal number of important features needed for a DSS to provide accurate risk prediction
- Limitations of a DSS for chronic opioid risk
- The potential economic impact of the DSS
- Ritu Agarwal, Robert H. Smith Dean's Chair of Information Systems & Senior Associate Dean for Faculty and Research, Smith School of Business
- Margrét Bjarnadóttir, Assistant Professor, Smith School of Business
- Kislaya Prasad, Research Professor, Smith School of Business
- David Anderson, Assistant Professor, Zicklin School of Business (Baruch College)
- Kenyon Crowley, Deputy Director, CHIDS
Opioids are a class of drugs including heroin as well as powerful pain relievers available by prescription, such as oxycodone, hydrocodone, codeine, morphine and others. Prescription opioids were involved in about 40 percent of the U.S. opioid-related deaths in 2016.
In addition to addiction and death, chronic opioid therapy also is associated with constipation, sleep-disordered breathing, fractures, behavioral dysfunction and increased sensitivity to pain.
“Not only are policymakers and citizens deeply concerned about the prevalence of opioid abuse, health insurance claims associated with opioid dependence have grown in excess of 3,000 percent from 2007 to 2014 and treatment costs continue to surge,” says Agarwal.
Crowley adds, “We aim to translate these results into practice and look forward to collaborations that support the further research, development and use of precision prescribing using advanced analytics.”
The Center for Health Information and Decision Systems (CHIDS) is an academic research center based in the Decision, Operations & Information Technologies (DO&IT) department in the Robert H. Smith School of Business, which collaborates closely with industry, government, and other key health system stakeholders. The research at CHIDS seeks to understand how digital technologies can be more effectively deployed to address outcomes such as quality, efficiency in healthcare delivery, patient safety, and a reduction in health disparities. CHIDS offers the benefit of a world-class research staff and renowned scholars in healthcare analytics and modeling, and health information technology design, adoption, and evaluation. CHIDS is a pioneer in the study of digitally enabled health system transformation, widely known for its thought leadership and research collaborations.