Conversational AI for Automated Patient Questionnaire Completion: Development Insights and Design Principles
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866910030014447616 |
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| author | Navarro, David Fraile Peleg, Mor |
| author_facet | Navarro, David Fraile Peleg, Mor |
| contents | 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). |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2602_19507 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Conversational AI for Automated Patient Questionnaire Completion: Development Insights and Design Principles Navarro, David Fraile Peleg, Mor Human-Computer Interaction 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). |
| title | Conversational AI for Automated Patient Questionnaire Completion: Development Insights and Design Principles |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2602.19507 |