Conversational AI for Automated Patient Questionnaire Completion: Development Insights and Design Principles
David Fraile Navarro, Mor Peleg

TL;DR
This paper presents the development of a GPT-5 based conversational agent for collecting patient-reported data on back pain, emphasizing innovative design principles for health data collection through dialogue.
Contribution
It introduces a topic-based conversational approach and key design principles extending clinical guidelines for effective patient questionnaire automation.
Findings
Engages users in topic-based conversations for efficient data collection
Identifies key design principles for health data collection CAs
Addresses challenges like conversation length and ambiguity handling
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…
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Taxonomy
TopicsArtificial Intelligence in Healthcare and Education · Digital Mental Health Interventions · Machine Learning in Healthcare
