Augmenting project ECHO for opioid use disorder with data-informed quality improvement

Abstract Background National opioid-related overdose fatalities totaled 650,000 from 1999 to 2021. Some of the highest rates occurred in New Hampshire, where 40% of the population lives rurally. Medications for opioid use disorder (MOUD; methadone, buprenorphine, and naltrexone) have demonstrated effectiveness in reducing opioid overdose and mortality. Methadone access barriers disproportionally impact rural areas and naltrexone uptake has been limited. Buprenorphine availability has increased and relaxed regulations reduces barriers in general medical settings common in rural areas. Barriers to prescribing buprenorphine include lack of confidence, inadequate training, and lack of access to experts. To address these barriers, learning collaboratives have trained clinics on best-practice performance data collection to inform quality improvement (QI). This project sought to explore the feasibility of training clinics to collect performance data and initiate QI alongside clinics’ participation in a Project ECHO virtual collaborative for buprenorphine providers. Methods Eighteen New Hampshire clinics participating in a Project ECHO were offered a supplemental project exploring the feasibility of performance data collection to inform QI targeting increased alignment with best practice. Feasibility was assessed descriptively, through each clinic’s participation in training sessions, data collection, and QI initiatives. An end-of-project survey was conducted to understand clinic staff perceptions of how useful and acceptable they found the program. Results Five of the eighteen health care clinics that participated in the Project ECHO joined the training project, four of which served rural communities in New Hampshire. All five clinics met the criteria for engagement, as each clinic attended at least one training session, submitted at least one month of performance data, and completed at least one QI initiative. Survey results showed that while clinic staff perceived the training and data collection to be useful, there were several barriers to collecting the data, including lack of staff time, and difficulty standardizing documentation within the clinic electronic health record. Conclusions Results suggest that training clinics to monitor their performance and base QI initiatives on data has potential to impact clinical best practice. While data collection was inconsistent, clinics completed several data-informed QI initiatives, indicating that smaller scale data collection might be more attainable.