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Auto Response Generation in Online Medical Chat Services.

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Summary
This summary is machine-generated.

This study introduces an AI-powered auto-response system to improve doctor efficiency in telehealth chat sessions. The smart mechanism helps manage patient messages and suggest timely responses, enhancing the virtual care experience.

Keywords:
AI and healthcareDeep learningMedical servicesNatural language processingSmart chat reply

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare
  • Natural Language Processing

Background:

  • Telehealth adoption has surged, increasing demand for efficient remote medical services.
  • The COVID-19 pandemic accelerated the need for virtual consultations.
  • Effective communication in telehealth chat sessions is crucial for quality patient care.

Purpose of the Study:

  • To develop an intelligent auto-response generation mechanism for doctor-patient chat sessions.
  • To enhance the efficiency and quality of medical conversations in telehealth.
  • To assist doctors in managing consultation requests during peak times.

Main Methods:

  • Utilized a dataset of over 900,000 anonymous doctor-patient messages.
  • Applied clustering algorithms to identify frequent doctor responses and manually labeled data.
  • Trained machine learning models, including BERT, for response generation.
  • Implemented a two-step process: a filtering model and a response generator suggesting top-3 replies.

Main Results:

  • The developed auto-response system effectively filters patient messages and suggests relevant doctor responses.
  • The BERT model achieved 85.41% precision@3, demonstrating high accuracy and parameter robustness.
  • The system aids doctors in responding more efficiently during busy telehealth sessions.

Conclusions:

  • The smart auto-response mechanism significantly improves the efficiency of telehealth consultations.
  • AI-driven tools like this are vital for scaling virtual healthcare services.
  • This technology supports healthcare professionals in delivering timely and effective remote patient care.