Leveraging human-centered design and causal pathway diagramming toward enhanced specification and development of innovative implementation strategies: a case example of an outreach tool to address racial inequities in breast cancer screening.

BACKGROUND: Implementation strategies are strategies to improve uptake of evidence-based practices or interventions and are essential to implementation science. Developing or tailoring implementation strategies may benefit from integrating approaches from other disciplines; yet current guidance on how to effectively incorporate methods from other disciplines to develop and refine innovative implementation strategies is limited. We describe an approach that combines community-engaged methods, human-centered design (HCD) methods, and causal pathway diagramming (CPD)-an implementation science tool to map an implementation strategy as it is intended to work-to develop innovative implementation strategies. METHODS: We use a case example of developing a conversational agent or chatbot to address racial inequities in breast cancer screening via mammography. With an interdisciplinary team including community members and operational leaders, we conducted a rapid evidence review and elicited qualitative data through interviews and focus groups using HCD methods to identify and prioritize key determinants (facilitators and barriers) of the evidence-based intervention (breast cancer screening) and the implementation strategy (chatbot). We developed a CPD using key determinants and proposed strategy mechanisms and proximal outcomes based in conceptual frameworks. RESULTS: We identified key determinants for breast cancer screening and for the chatbot implementation strategy. Mistrust was a key barrier to both completing breast cancer screening and using the chatbot. We focused design for the initial chatbot interaction to engender trust and developed a CPD to guide chatbot development. We used the persuasive health message framework and conceptual frameworks about trust from marketing and artificial intelligence disciplines. We developed a CPD for the initial interaction with the chatbot with engagement as a mechanism to use and trust as a proximal outcome leading to further engagement with the chatbot. CONCLUSIONS: The use of interdisciplinary methods is core to implementation science. HCD is a particularly synergistic discipline with multiple existing applications of HCD to implementation research. We present an extension of this work and an example of the potential value in an integrated community-engaged approach of HCD and implementation science researchers and methods to combine strengths of both disciplines and develop human-centered implementation strategies rooted in causal perspective and healthcare equity.

Implementation Fidelity of Chatbot Screening for Social Needs: Acceptability, Feasibility, Appropriateness.

OBJECTIVES: Patient and provider-facing screening tools for social determinants of health have been explored in a variety of contexts; however, effective screening and resource referral remain challenging, and less is known about how patients perceive chatbots as potential social needs screening tools. We investigated patient perceptions of a chatbot for social needs screening using three implementation outcome measures: acceptability, feasibility, and appropriateness. METHODS: We implemented a chatbot for social needs screening at one large public hospital emergency department (ED) and used concurrent triangulation to assess perceptions of the chatbot use for screening. A total of 350 ED visitors completed the social needs screening and rated the chatbot on implementation outcome measures, and 22 participants engaged in follow-up phone interviews. RESULTS: The screened participants ranged in age from 18 to 90 years old and were diverse in race/ethnicity, education, and insurance status. Participants (n = 350) rated the chatbot as an acceptable, feasible, and appropriate way of screening. Through interviews (n = 22), participants explained that the chatbot was a responsive, private, easy to use, efficient, and comfortable channel to report social needs in the ED, but wanted more information on data use and more support in accessing resources. CONCLUSION: In this study, we deployed a chatbot for social needs screening in a real-world context and found patients perceived the chatbot to be an acceptable, feasible, and appropriate modality for social needs screening. Findings suggest that chatbots are a promising modality for social needs screening and can successfully engage a large, diverse patient population in the ED. This is significant, as it suggests that chatbots could facilitate a screening process that ultimately connects patients to care for social needs, improving health and well-being for members of vulnerable patient populations.