Conversational AI for Automated Patient Questionnaire Completion: Development Insights and Design Principles

2026-02-22

AI Summary

This study addresses the inefficiency and burden associated with traditional form-based collection of patient-reported outcome measures (PROMs). The authors propose a generative conversational agent powered by GPT-5 that replaces item-by-item questioning with thematic natural dialogue to efficiently capture the NIH minimal dataset for back pain. They articulate design principles for health-data-oriented conversational agents, emphasizing interaction flexibility, persona calibration, confidence visualization, safety constraints, and interoperability, while extending clinical decision support guidelines to conversational interfaces. Validated by clinicians and user groups, the system accurately acquires multidimensional data within a single dialogue turn, significantly enhancing user experience and efficiency. The work also provides an open-source framework and actionable strategies, establishing a reusable paradigm for future health-focused conversational systems.

Technology Category

• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Natural Language Processing: Conversational AI/Dialog Systems
• Humans and AI: Intelligent User Interfaces

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Search and Retrieval-Augmented AI: Assisted, interactive, and conversational search
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Collecting patient-reported outcome measures (PROMs) is essential for clinical care and research, yet traditional form-based approaches are often tedious for patients and burdensome for clinicians. We developed a generative AI conversational agent(CA) using GPT-5 to collect back pain data according to the NIH Task Force's Recommended Minimal Dataset. Unlike prior CAs that ask questions one-by-one, our CA engages users in topic-based conversations, allowing multiple data items to be captured in a single exchange. Through iterative development and pilot testing with clinicians and a consumer panel, we identified key design principles for health data collection CAs. These principles extend established clinical decision support design guidelines to conversational interfaces, addressing: flexibility of interaction style, personality calibration, data quality assurance through confidence visualization, patient safety constraints, and interoperability requirements. We present our prompt design methodology and discuss challenges encountered, including managing conversation length, handling ambiguous responses, and adapting to LLM version changes. Our design principles provide a practical framework for developers creating conversational agents for patient questionnaire completion. The CA is available at https://chatgpt.com/g/g-68f4869548f48191af0544f110ee91c6-backpain-data-collection-assistant (requires ChatGPT registration and subscription for unlimited use).

Problem

• patient-reported outcome measures
• conversational AI
• questionnaire completion
• clinical data collection
• healthcare burden

Innovation

• Conversational AI
• Patient-reported outcomes
• Generative AI
• Design principles
• Health data collection