ABSTRACT
BACKGROUND: The United States Preventive Services Task Force recommends annual alcohol screening and brief behavioral intervention (alcohol SBI) with general adult and pregnant populations. Implementation of alcohol SBI in primary care has encountered numerous barriers to adapting procedures and infrastructure to support its routine delivery. This collection of case studies describes the implementation strategies used by 4 academic health system teams that were funded by the Centers for Disease Control and Prevention to implement alcohol SBI within healthcare systems to prevent alcohol-exposed pregnancies. METHODS: We used constructs from the Framework for Reporting Adaptations and Modifications-Expanded (FRAME) to describe planned and unplanned adaptations to implementation strategies, and the SBIRT (Screening, Brief Intervention, and Referral to Treatment) Program Matrix to identify key questions, challenges, and recommendations for improving alcohol SBI implementation. Participating systems were 2 regional affiliates of a national reproductive healthcare organization, an integrated non-profit healthcare system, and an urban medical center and its affiliated network of community health centers. RESULTS: Planned adaptations included expanding the target population for brief interventions to include patients drinking at low levels who could become pregnant, modifying workflows and systems to support routine screening, and customizing training content and logistics. Unplanned adaptations included varying site recruitment and pre-implementation awareness-building strategies to enhance local receptivity of systems with decentralized management, and pivoting from in-person to virtual training during the COVID-19 pandemic. Fewer unplanned adaptations were observed for health systems with centralized management structures and practice teams that were fully engaged in implementation planning, training, roll-out, and problem-solving. CONCLUSIONS: Unplanned adaptations were observed across the 4 cases and emphasized the importance of flexible, adaptive designs when implementing evidence-based practice in dynamic settings. Participation of the health system in planning, including decisions to modify electronic health records and workflows, supported adapting to unplanned circumstances to achieve implementation goals.
ABSTRACT
Research suggests participant engagement is a key mediator of mHealth alcohol interventions' effectiveness in reducing alcohol consumption among users. Understanding the features that promote engagement is critical to maximizing the effectiveness of mHealth-delivered alcohol interventions. The purpose of this study was to identify facilitators and barriers to mHealth alcohol intervention utilization among hazardous-drinking participants who were randomized to use either an app (Step Away) or Artificial Intelligence (AI) chatbot-based intervention for reducing drinking (the Step Away chatbot). We conducted semi-structured interviews from December 2019 to January 2020 with 20 participants who used the app or chatbot for three months, identifying common facilitators and barriers to use. Participants of both interventions reported that tracking their drinking, receiving feedback about their drinking, feeling held accountable, notifications about high-risk drinking times, and reminders to track their drinking promoted continued engagement. Positivity, personalization, gaining insight into their drinking, and daily tips were stronger facilitator themes among bot users, indicating these may be strengths of the AI chatbot-based intervention when compared to a user-directed app. While tracking drinking was a theme among both groups, it was more salient among app users, potentially due to the option to quickly track drinks in the app that was not present with the conversational chatbot. Notification glitches, technology glitches, and difficulty with tracking drinking data were usage barriers for both groups. Lengthy setup processes were a stronger barrier for app users. Repetitiveness of the bot conversation, receipt of non-tailored daily tips, and inability to self-navigate to desired content were reported as barriers by bot users. To maximize engagement with AI interventions, future developers should include tracking to reinforce behavior change self-monitoring and be mindful of repetitive conversations, lengthy setup, and pathways that limit self-directed navigation.