Abstract

Project Summary The US opioid epidemic has put a significant burden on Veterans and the VA. Veterans often suffer from chronic pain syndromes due to war injuries, toxic exposures, and deployment-related psychiatric comorbidities and are vulnerable to opioid use/misuse. Chronic pain syndromes occur in 65.4% of U.S. veterans, 9.1% of it severe, as against 56 and 6.4% in non-veterans respectively. Both opioid misuse and unrelieved pain have been linked to a higher risk of suicide among Veterans, greater among women. To address the opioid epidemic in the VA, in August 2013, the VA deployed the Opioid Safety Initiative (OSI) to ensure that opioids are used in a safe, effective, and judicious manner and the Stratification Tool for Opioid Risk Management (STORM). Although the implementation of OSI has substantially reduced risky and other opioid prescriptions in VHA and increased use of non-opioid treatments for pain, there remain major gaps in evidence to formulate comprehensive policy as current data is almost exclusively derived from Veterans receiving care within the VHA. This is important as ~80% of the Veterans have private health insurance. It has been reported that Veterans who receive dual VHA and non-VHA care received more opioid prescriptions and more risky prescriptions, that mono VHA users. Also, while opioid overdose rates have been increasing in VHA enrollees VHA Opioid prescriptions in these veterans declined. To address the prescription drug misuse problem, states use Prescription Drug Monitoring Programs (PDMPs), which are electronic databases that collect and track prescription data on controlled substances to reduce their abuse and diversion. However, despite access to these data via Health Information Exchanges (HIE), the guideline-discordant unsafe and concurrent prescriptions and fillings of opioids continue. Also, PDMP data alone are not suitable for policy decisions and practice recommendations as they lack the detailed clinical information necessary to make a comprehensive evaluation of underlying factors associated with non-guideline-concordant prescriptions. Our preliminary data show a decline in Opioid prescriptions with less decline in the diagnosis of Opioid Use Disorder. The absence of community data is also mentioned as a major deficiency in the study and analyses of the opioid misuse crisis in a 2017 VA Office of Inspector General Report. In this VHA HSR&D Merit Review Application we propose to examine factors associated with prescription opioid misuse, specifically the guideline-discordant use of opioids, in 3 Veterans groups, (1) VHA mono-users, (2) VHA paid dual users of both VHA and non-VHA care, and (3) non-VHA paid dual users. We also propose to conduct an interview/focus group study of VA and non-VA community health providers perspectives on: a) barriers and facilitators in providing guideline- concordant care to the dual users, and b) coordination strategies to reduce opioid misuse in the dual user groups. These aims will be achieved by analyzing the complex data using novel deep learning and natural language processing methods in addition to the state-of-the-art statistical methods. The data involved will include the VHA and MedStar Health (largest healthcare system in the Mid-Atlantic region) electronic health record (EHR), the Chesapeake Regional Information System for Patients (CRISP) and Medicare databases. We will also bring together VA and non-VA community health providers, including clinicians, administrators, policy makers, and patients. We have conducted preliminary studies and collected preliminary data to demonstrate the feasibility of the proposed deep learning and natural language processing methods as well as our access to VA and non-VA EHR data. The results of the proposed study will be shared with our VHA and community operational partners. Our ultimate goal is to evaluate and improve care coordination and reduce opioid misuse in Veterans who are dual users of VA and community care.

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DRA: Health Systems

DRE: TRL - Applied/Translational

Keywords: Care Coordination, Models of Care, Outcomes - System

MeSH Terms: None at this time.