ArticleHealthcare (Basel, Switzerland)2023
Framework for Guiding the Development of High-Quality Conversational Agents in Healthcare.
Article in Healthcare (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Review
- Large language models for the mental health community: framework for translating code to care.The Lancet. Digital health · 2025Review
- Think FAST: a novel framework to evaluate fidelity, accuracy, safety, and tone in conversational AI health coach dialogues.Frontiers in digital health · 2025Article
- Person-based design and evaluation of MIA, a digital medical interview assistant for radiology.Frontiers in artificial intelligence · 2024Article
- The ethical aspects of integrating sentiment and emotion analysis in chatbots for depression intervention.Frontiers in psychiatry · 2024Article
- Potential and pitfalls of conversational agents in health care.Nature reviews. Disease primers · 2023Article
- Assessing the Potential Risks of Digital Therapeutics (DTX): The DTX Risk Assessment Canvas.Journal of personalized medicine · 2023Article
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1 author.
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Abstract
Evaluating conversational agents (CAs) that are supposed to be applied in healthcare settings and ensuring their quality is essential to avoid patient harm and ensure efficacy of the CA-delivered intervention. However, a guideline for a standardized quality assessment of health CAs is still missing. The objective of this work is to describe a framework that provides guidance for development and evaluation of health CAs. In previous work, consensus on categories for evaluating health CAs has been found. In this work, we identify concrete metrics, heuristics, and checklists for these evaluation categories to form a framework. We focus on a specific type of health CA, namely rule-based systems that are based on written input and output, have a simple personality without any kind of embodiment. First, we identified relevant metrics, heuristics, and checklists to be linked to the evaluation categories through a literature search. Second, five experts judged the metrics regarding their relevance to be considered within evaluation and development of health CAs. The final framework considers nine aspects from a general perspective, five aspects from a response understanding perspective, one aspect from a response generation perspective, and three aspects from an aesthetics perspective. Existing tools and heuristics specifically designed for evaluating CAs were linked to these evaluation aspects (e.g., Bot usability scale, design heuristics for CAs); tools related to mHealth evaluation were adapted when necessary (e.g., aspects from the ISO technical specification for mHealth Apps). The resulting framework comprises aspects to be considered not only as part of a system evaluation, but already during the development. In particular, aspects related to accessibility or security have to be addressed in the design phase (e.g., which input and output options are provided to ensure accessibility?) and have to be verified after the implementation phase. As a next step, transfer of the framework to other types of health CAs has to be studied. The framework has to be validated by applying it during health CA design and development.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.