Source-linked AI summary
Foundation Metrics for Evaluating Effectiveness of Healthcare Conversations Powered by Generative AI
Mahyar Abbasian, Elahe Khatibi, Iman Azimi, David Oniani, Zahra Shakeri Hossein Abad, Alexander Thieme, Ram Sriram, Zhongqi Yang, Yanshan Wang, Bryant Lin, Olivier Gevaert, Li-Jia Li, Ramesh Jain, Amir M. Rahmani
TL;DR
Healthcare chatbot evaluation lacks medical understanding and important user-centered dimensions despite the life-critical nature of healthcare applications. This paper reviews existing approaches and proposes a comprehensive user-centered framework organized around four metric categories, while identifying implementation challenges and benchmark needs.
Problem
Existing evaluation metrics lack understanding of medical concepts and patient well-being while overlooking user-centered concerns such as trust, ethics, personalization, empathy, comprehension, and emotional support.
Method
The paper reviews state-of-the-art healthcare chatbot evaluation metrics and proposes a user-centered framework spanning accuracy, trustworthiness, empathy, and performance.
Results
The paper presents a comprehensive set of healthcare chatbot metrics and identifies associations among metrics, including positive links between up-to-dateness and groundedness.
Takeaways & Limitations
Healthcare chatbot assessment should combine language, clinical-task, interaction, and computing-performance perspectives while accounting for relevant confounding variables.
Takeaways & Limitations
Metric definition and evaluation are affected by confounding factors including target audience, evaluation methods, and prompt techniques.
Abstract
from arXiv · showhide
Generative Artificial Intelligence is set to revolutionize healthcare delivery by transforming traditional patient care into a more personalized, efficient, and proactive process. Chatbots, serving as interactive conversational models, will probably drive this patient-centered transformation in healthcare. Through the provision of various services, including diagnosis, personalized lifestyle recommendations, and mental health support, the objective is to substantially augment patient health outcomes, all the while mitigating the workload burden on healthcare providers. The life-critical nature of healthcare applications necessitates establishing a unified and comprehensive set of evaluation metrics for conversational models. Existing evaluation metrics proposed for various generic large language models (LLMs) demonstrate a lack of comprehension regarding medical and health concepts and their significance in promoting patients' well-being. Moreover, these metrics neglect pivotal user-centered aspects, including trust-building, ethics, personalization, empathy, user comprehension, and emotional support. The purpose of this paper is to explore state-of-the-art LLM-based evaluation metrics that are specifically applicable to the assessment of interactive conversational models in healthcare. Subsequently, we present an comprehensive set of evaluation metrics designed to thoroughly assess the performance of healthcare chatbots from an end-user perspective. These metrics encompass an evaluation of language processing abilities, impact on real-world clinical tasks, and effectiveness in user-interactive conversations. Finally, we engage in a discussion concerning the challenges associated with defining and implementing these metrics, with particular emphasis on confounding factors such as the target audience, evaluation methods, and prompt techniques involved in the evaluation process.
Introduction
Healthcare chatbots could support diverse patient and provider tasks, but their life-critical use requires comprehensive evaluation beyond generic language metrics. The paper proposes user-centered metrics and discusses implementation challenges involving audiences, evaluation methods, and prompting.
- Healthcare chatbots could support symptom assessment, health education, mental health support, lifestyle coaching, scheduling, reminders, triaging, and resource allocation.
- Life-critical healthcare applications require unified foundation metrics to evaluate performance, identify errors, and enable effective feedback mechanisms.
- Generic metrics often lack standardized agreement for healthcare chatbot evaluation and rely mainly on language-specific perspectives and surface-form similarity.
- BLEU and ROUGE overlook medical concepts, their relationships, patient well-being, semantic nuance, context, critical ordering, and human-centered perspectives.
- Existing evaluations also overlook user-centered concerns such as empathy, trust, ethics, personalization, comprehension, emotional support, hallucinations, and practical performance.
- The paper reviews existing metrics, presents comprehensive user-centered categories spanning language, clinical tasks, and interaction, and discusses confounding factors in metric implementation.
Review of Existing Evaluation Metrics for LLMs
Existing LLM evaluation uses intrinsic and extrinsic approaches, but healthcare assessment remains limited by surface-form metrics and fragmented coverage. The review motivates broader, user-centered evaluation across accuracy, trustworthiness, empathy, and practical performance.
- Intrinsic Evaluation Metrics: Intrinsic evaluation measures generated-language proficiency and includes general automatic and dialogue-based metrics.
- Intrinsic Evaluation Metrics: Intrinsic metrics are computationally simple but inadequately capture semantics, context, distant dependencies, critical ordering changes, and human perspectives.
- Intrinsic Evaluation Metrics: BLEU and ROUGE scored semantically similar cardiovascular-health sentences 0.39 and 0.13, respectively, on a 0-to-1 scale.
- Extrinsic Evaluation Metrics: Extrinsic metrics incorporate user perspectives and real-world contexts through human participation, covering general-purpose and health-specific evaluations.
- Extrinsic Evaluation Metrics: Existing extrinsic studies address context and semantics but remain confined to distinct metric sets rather than comprehensive healthcare coverage.
- Multi-metric Measurements: Prior multi-metric work covers dimensions such as accuracy, calibration, robustness, fairness, bias, toxicity, privacy, ethics, and efficiency, but omits several healthcare-relevant user-centered metrics.
Essential Metrics for Evaluating Healthcare Chatbots
The paper proposes a user-centered framework for evaluating healthcare chatbots through metrics organized around accuracy, trustworthiness, empathy, and performance. Evaluation accounts for user, domain, and task types while supporting comparison through metric scores and leaderboards.
- Healthcare chatbot evaluation scores models from users’ perspectives across metrics and can support comparative ranking through a leaderboard.Evaluators interact with conversational models while considering confounding variables.
- User type, domain type, and task type shape evaluation requirements, including safety, privacy, recommendations, and the relevance of specialized metrics.Patients, nurses, primary care providers, specialists, and users of general or specialized healthcare chatbots may require different evaluations.
- The framework organizes metrics into accuracy, trustworthiness, empathy, and performance groups according to their dependencies on confounding variables.Accuracy depends on domain and task types; trustworthiness depends on user type; empathy considers patient needs; performance considers all three variables.
- Accuracy: Accuracy metrics assess grammar, syntax, semantics, and response structure through automatic and human-based evaluations tailored to domain and task types.Examples include linguistic and relevance metrics, SSI, robustness, generalization, conciseness, and up-to-dateness.
- Trustworthiness: Trustworthiness metrics cover safety, privacy, bias, and interpretability, with definitions tailored to the user type.Safety evaluations consider harmful content, answer confidence, response detail, and potential risks; professional and patient interactions may require different caution levels.
- Empathy and Performance: Empathy metrics cover emotional support, health literacy, fairness, and personalization, while performance metrics address usability, latency, model size, memory use, and computation.Reducing parameters often decreases memory usage and FLOPs, which is likely to improve usability and latency.
Challenges in Evaluating Healthcare Chatbots
Evaluating healthcare chatbots requires accounting for metric associations, evaluation-method choices, user and domain confounders, and configurable prompting and inference parameters. Existing automatic and human-based approaches face coverage, subjectivity, and consistency challenges.
- Metrics association: Metric associations can be positive or negative both within categories and across categories, complicating interpretation of individual scores.Examples include positive links between up-to-dateness and groundedness, and tensions between empathy, personalization, privacy, and bias.
- Metrics association: Model parameter counts may affect accuracy, trustworthiness, and empathy metrics in either positive or negative ways.Low parameter counts may limit knowledge acquisition, while increased parameters may introduce complexity.
- Evaluation methods: Existing benchmarks may not test robustness across target user types, domains, and task types, requiring more diverse benchmark coverage.This limits automatic evaluation when confounding variables are not comprehensively represented.
- Evaluation methods: Human evaluation is challenged by subjectivity and the need for multiple annotators with expertise across healthcare domains.Multiple annotators help capture normative judgments and reduce bias.
- Scoring and configuration: Scores depend on whether metrics are assessed per answer or after an entire conversation session.Intrinsic metrics may be better suited to per-answer assessment.
- Scoring and configuration: Prompting techniques and inference parameters, including beam search and temperature, can change chatbot behavior and metric scores.The paper identifies zero-shot, few-shot, evidence-based chain-of-thought, and persona-based prompting as relevant choices.
Toward an Effective Evaluation Framework
The proposed framework organizes healthcare chatbot evaluation around configurable models, environments, interfaces, users, and leaderboards. It combines healthcare-specific benchmarks, human guidelines, evaluation methods, and standardized scoring to support comparison across models.
- Framework overview: The framework is intended as foundational code for future healthcare chatbot benchmarks and guidelines, with components adapted to healthcare-specific needs.Its high-level representation includes models, environment, interface, interacting users, and leaderboard.
- Framework components: The environment configures confounding variables, prompting techniques and parameters, and evaluation methods according to research objectives.Confounding variables cover users, domains, and task types.
- Evaluation component: Healthcare-specific benchmarks should align with metric categories and cover users, domains, and task types to test target metrics thoroughly.Combined with automatic methods such as ROUGE and BLEU, these benchmarks can score extrinsic metrics.
- Evaluation component: Human evaluation guidelines support manual scoring by healthcare professionals and feedback from intended users about answer relevance and helpfulness.The guidelines are designed to evaluate chatbot performance from final-user perspectives.
- Evaluation component: Precise scoring guidelines and predefined evaluator questions promote more consistent and standardized evaluation practices.Standardized questions can improve uniformity in scoring behavior across metric categories and submetrics.
- Evaluation component: A unified evaluation method should combine benchmark-based and supervised approaches into a final score weighted by metric-category priorities.The paper gives trustworthiness outweighing accuracy in a task as an example of priority-sensitive scoring.
- Future evaluation methods: BERT-based models trained for healthcare categorization and scoring are identified as an unexplored avenue for calculating individual metric scores.This could augment the evaluation process while treating the evaluation component as a black box.
- Framework components: The interface lets evaluators configure models, parameters, users, guidelines, methods, and benchmarks, while research teams create models and evaluation resources.The leaderboard supports ranking and filtering chatbot models by selected criteria such as accuracy.
Conclusion
The paper introduces four categories of user-centered metrics for healthcare chatbots and discusses confounding variables that affect their definition and evaluation. Future work will implement the framework through benchmarks and case studies across medical fields.
- Conclusion: The proposed user-centered metrics are grouped into accuracy, trustworthiness, empathy, and computing performance.The paper presents these categories as a comprehensive approach tailored to healthcare chatbots.
- Conclusion: The study highlights confounding variables as factors affecting metric definition and evaluation, alongside challenges in developing and implementing the metrics.The stated aim is to support more reliable and higher-quality healthcare chatbot systems and improved patient experience.
- Future directions: Future work will implement the evaluation framework, establish unified healthcare chatbot benchmarks, and conduct case studies across mental and physical health domains.The planned studies will consider challenges and parameters specific to different medical fields.
Competing Interests
The authors report affiliations, consulting, and ownership interests for two authors; all other authors declare no competing financial or non-financial interests.
- Competing interests: Y.W. collaborates with HealthUnity, consults for Pfizer Inc., and holds ownership or equity interests in BonafideNLP, LLC.
- Competing interests: D.O. collaborates with HealthUnity, while all other authors declare no competing financial or non-financial interests.
Disclaimer
The paper identifies certain commercial systems without endorsing them or implying they are the best available options. Its opinions, findings, conclusions, and recommendations belong to the authors and may not represent NIST or other supporting organizations.
- Identifying commercial systems does not imply NIST recommendation or endorsement.
- The identified products are not necessarily the best available for their intended purpose.
- The paper’s opinions, findings, conclusions, and recommendations are those of the authors and may not reflect NIST or other supporting organizations.