ArticleDigital health
Implementing artificial intelligence (AI)-supported communication tools in healthcare: System-level perspectives.
Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Quality patient-clinician communication is critical to ensure optimal patient outcomes. Recent advancements in artificial intelligence (AI) have increased the potential to develop technologies to evaluate and support patient-clinician communication. One example is CommSense, an AI-supported communication technology designed by our team to record and analyze clinical conversations and provide clinicians with actionable, tailored feedback to enhance communication performance. Objective: To explore system-level considerations to successfully implement communication technologies into healthcare contexts, using CommSense as an example case. Methods: This descriptive qualitative study used a purposive sampling strategy to recruit participants with communication expertise across four categories: administrators; industry; policymakers; and scientists. Semi-structured interviews focused on: 1) general barriers to patient-clinician communication; and 2) implementation considerations for integrating communication technologies into healthcare systems. Interviews were audio-recorded, transcribed verbatim, verified, and then analyzed using a combination of inductive and deductive coding strategies to identify broader patterns and themes. Results: Participants (n=13) discussed general barriers to quality communication between patients and clinicians, including: inadequate clinician communication training; generalized versus tailored patient communication; and hesitancy to initiate conversations with patients regarding difficult or sensitive issues, such as advance care planning. System-level implementation considerations focused on: the importance of integrating AI-supported communication technologies within existing health system infrastructure, clinical workflows, and policies and procedures; engagement of champions throughout the implementation process; and intentionality with the design and delivery of communication performance feedback. Participants also emphasized the particular benefit of communication technologies to support trainees and their learning. Conclusion: AI-supported communication technologies have the potential to address persistent barriers to quality communication within healthcare. Our findings demonstrate the importance of including decision-makers and outside experts across multiple domains to ensure successful implementation of AI-supported communication tools within complex healthcare systems.
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Registered trials
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