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Empowering Preventive Care with GECA Chatbot.

Eva Maia1, Pedro Vieira1, Isabel Praça1

  • 1GECAD-Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development, School of Engineering of the Polytechnic of Porto (ISEP), 4249-015 Porto, Portugal.

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Summary

GECA, a novel chatbot for preventive care, offers personalized health information and monitoring for patients at home. It achieved a 97% accuracy rate, with a pilot study planned for further evaluation.

Keywords:
COVID-19artificial intelligencechatbotdementiapreventive care

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Area of Science:

  • Digital Health
  • Artificial Intelligence in Healthcare
  • Preventive Medicine

Background:

  • Growing adoption of chatbots in healthcare for patient engagement.
  • Need for personalized, accessible, and cost-effective preventive care solutions.
  • Challenges in maintaining patient well-being and preventing chronic conditions through home treatment.

Purpose of the Study:

  • To introduce GECA, a specialized chatbot for preventive care.
  • To highlight GECA's features: personalized information, advice, monitoring, and adaptability.
  • To emphasize GECA's role in supporting home treatment and enhancing user experience.

Main Methods:

  • Development of GECA, a chatbot with adaptable architecture and bilingual capabilities.
  • Integration of external resources for enhanced personalization, with adherence to security protocols.
  • Testing of GECA's accuracy and precision in interactions.

Main Results:

  • GECA demonstrated a 97% success rate in delivering accurate responses.
  • The chatbot's design supports cost-effectiveness, personalization, and user engagement.
  • Bilingual features and external resource integration improve accessibility and user experience.

Conclusions:

  • GECA presents a promising, accurate, and accessible solution for preventive care.
  • Its adaptable design allows for seamless extension to various diseases and conditions.
  • A pilot project is planned to evaluate GECA's effectiveness, efficiency, and user satisfaction in a clinical setting.