Funding Source
NIDA Clinical Trials Network (NIH HEAL Initiative), CTN-0103
Project Period
4/1/2019 – 5/31/2020
Principal Investigator
Lisa Marsch, PhD (PI, Northeast Node, Geisel School of Medicine at Dartmouth); Jeanne Ryer, MS, EdD (Lead Investigator, NHCHI Director, Citizens Health Initiative [CHI], University of New Hampshire College of Health and Human Services)
Other Project Staff
Marcy Doyle, MHA, DNP, RN, Clinical & Quality Measurement Director NHCHI, Jan Thomas, BS, RN, Project Director, NHCHI, Owen Murray, MS, LMFT Project Manager, (Northeast Node), Bethany McLeman, BA (Northeast Node), Corina Chao, BA, Research Associate, NHCHI
Project Summary
As part of an ongoing teleECHO learning collaborative (LC), this study will expand clinical research training in evidence-based quality improvement (QI) methods that were central to delivering and sustaining science-based medications for opioid use disorder (MOUD) treatment within the Vermont Hub-and-Spoke Model (HSM) with fidelity. To do so, participating primary care practices and community mental health clinics will be trained in the use of a study-developed toolkit of research and evaluation quality improvement methods intended to expand provider knowledge and performance in the delivery of evidence-based MOUD. TeleECHO (hosted within a telehealth platform) offers considerable promise for supporting a broad learning collaborative/training mechanism within the HSM among geographically-dispersed providers (e.g., in rural contexts). We will initially offer the toolkit to providers in a newly launched ECHO learning collaborative in New Hampshire (the state with the highest rate of fentanyl overdose deaths per capita), with the potential to further expand to additional states in the Northeast and elsewhere thereafter. In addition to the learning in evidence-based MOUD provided by the ECHO modules, the study will train participating practices to systematically track standardized outcome metrics from these trainings to inform Plan-Do-Study-Act (PDSA) QI cycles supported by ECHO LC facilitators. The study will also train practices to regularly share these standardized data with other LC members so that practices can use this empirical information to refine their care model over time, with a goal of improving fidelity to best practices for MOUD and promoting optimal patient outcomes. Over the course of the ECHO LC, the study will measure (both via the ECHO and toolkit QI data collection) changes in providers’ knowledge about best practices for MOUD, their comfort in caring for OUD patients with MOUD, and their performance on all the standardized outcome metrics. Overall, this initiative will support training in clinical research and evaluation methods to best implement, measure, and sustain evidence-based MOUD.
Public Health Relevance
The opioid overdose rate has increased significantly in recent years with more people in New Hampshire dying from opioid overdose in 2017 than by motor vehicle crashes, suicide, and homicide combined. New Hampshire has consistently ranked in the top five states in the nation for overdose death rates per capita and over 95% of these New Hampshire overdose deaths involved synthetic opioids or heroin. Since the year 2000, there has been a fivefold increase in the number of infants born with neonatal opioid withdrawal syndrome (NOWS) in the state of New Hampshire and the medical and criminal justice costs of opioid use in New Hampshire have surpassed $36 million a year. MOUD has repeatedly been associated with reduced mortality and is associated with reductions in symptom severity of NOWS, infectious disease transmission, and criminal activity. Providers in New Hampshire who are waivered to treat opioid use disorder (OUD) with buprenorphine or who treat with naltrexone typically do not have large patient panels and many waivered providers are not currently prescribing at all. Opportunities to increase provider knowledge and confidence may improve best practice and increase MOUD capacity. This study has the potential to inform best practice for treatment of OUD though tracking outcome data related to QI.