Automated survey collection with LLM-based conversational agents
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View abstract on PubMed
Summary
This summary is machine-generated.Conversational Large Language Models (LLMs) can power scalable AI phone surveys. This approach reduces human effort in healthcare data collection, achieving 98% accuracy in response extraction.
Area Of Science
- Artificial Intelligence
- Health Informatics
- Natural Language Processing
Background
- Traditional phone surveys for health data are costly and difficult to scale.
- Limitations include expense, time consumption, and scalability challenges.
Purpose Of The Study
- To propose and evaluate a novel survey collection method using conversational Large Language Models (LLMs).
- To overcome the limitations of traditional phone surveys through AI-powered automation.
Main Methods
- Developed a framework using an LLM conversational agent for survey conduction and transcription.
- Employed GPT-4o for extracting survey responses from transcripts.
- Evaluated transcription errors, response accuracy, and user experience with 40 responses from 8 participants using LLM personas.
Main Results
- GPT-4o achieved 98% average accuracy in extracting survey responses.
- Transcription word error rate averaged 7.7%.
- Participants noted occasional conversational agent errors but appreciated its comprehension and engagement.
Conclusions
- LLM agents demonstrate significant potential for scalable, AI-powered phone surveys.
- This technology can reduce human effort and enhance healthcare data collection efficiency.
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