This week’s current CHIDS research highlight is Promoting Better Pain Management Outcomes: Precision Decision Support for Opioid Prescription
Chronic opioid therapy (COT) has been associated with serious adverse outcomes and the social and economic impact of continuing opioid treatment is sizeable. The net effect of COT on a given patient’s health – beneficial, adverse, or neutral – may be difficult to determine ex ante, and affected by many unobservable factors. Given the risks and adverse outcomes associated with COT, many consider COT a care choice of last resort. Thus, to the extent that clinicians need to make decisions regarding whether opioid use will begin or continue, decision support on the individual-patient probability of COT appears critical. Personalized guidelines, built on decision support systems (DSSs), have the potential to influence care at the point of service. Building models that can serve as the foundation of such systems can therefore contribute to changes in physician prescribing. As a result, CHIDS aims to study personalized pain management-related decision support for opioid prescribing. Specifically, CHIDS will apply state of the art machine learning algorithms, as well as more traditional models, to study the feasibility and potential impact of a COT DSS, using a large data set from the U.S. Army. CHIDS will further investigate the economic impact of such decision support. The National Institute for Health Care Management (NIHCM) is helping support this work through its Research Grants program.
For more about ongoing research at CHIDS, check out our website here:
Published by Margret Bjarnadottir, Ritu Agarwal, Kenyon Crowley, Al Nelson, Kislaya Prasad