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Conversational AI Phone Calls to Support Patients With Atrial Fibrillation: Randomized Controlled Trial.

Ritu Trivedi1, Liliana Laranjo1, Simone Marschner1

  • 1Westmead Applied Research Centre, Faculty of Medicine and Health, The University of Sydney, Level 5, Block K, Westmead Hospital, Hawkesbury Road, Westmead, 2145, Australia, 61 2 8890 3125.

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|August 19, 2025
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Summary
This summary is machine-generated.

This study found conversational artificial intelligence (AI) feasible for supporting atrial fibrillation (AF) patients postdischarge. While not statistically significant between groups, the AI intervention improved patient quality of life.

Keywords:
atrial fibrillationconversational artificial intelligencedigital healthfeasibilitynatural language processingphone callsquality of liferandomized controlled trialself-management

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Area of Science:

  • Digital health
  • Artificial intelligence in healthcare
  • Patient self-management

Background:

  • Effective patient education and self-management are crucial for managing atrial fibrillation (AF).
  • Conversational artificial intelligence (AI) offers potential for personalized patient support but lacks evaluation in AF.
  • Previous research has not explored AI's role in supporting AF patients postdischarge.

Purpose of the Study:

  • To assess the feasibility of a conversational AI intervention for supporting patients with atrial fibrillation (AF) after hospital discharge.
  • To evaluate patient engagement and the effectiveness of AI-driven health monitoring and education.

Main Methods:

  • A 6-month, single-blinded, 4:1 randomized controlled trial involving 103 postdischarge AF patients.
  • Intervention group received automated conversational AI phone calls, online surveys, and access to an educational website.
  • Primary outcome: change in Atrial Fibrillation Effect on Quality-of-Life (AFEQT) questionnaire score.

Main Results:

  • 82 patients received the AI intervention; the target sample size was 385.
  • No significant difference in AFEQT scores between groups (P=.46).
  • Exploratory analysis showed improved AFEQT scores in the intervention group (P=.01); 88.4% of outreaches found useful.

Conclusions:

  • Conversational AI is a feasible tool for supporting patients with chronic conditions postdischarge.
  • The AI intervention demonstrated potential for improving quality of life in AF patients.
  • Further research with a larger sample size and longer duration is warranted to confirm efficacy.