Heart & lung : the journal of critical care · 2026
Artificial intelligence applications for enhancing patient self-care education following sternotomy: Development and initial evaluation.
СтатьяНазвание и аннотация: Перевод в очереди
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Wung Shu-Fen, Noboa James A, Wung Zoe, Bartlett Courtney
Аннотация
BACKGROUND: Research indicates that over 12% of patients undergoing coronary artery bypass grafting and more than 14% of patients undergoing surgical aortic valve replacement experience rehospitalization within 30 days of discharge. Many of these re-admissions are potentially preventable. Comprehensive discharge education is essential, and AI applications can deliver tailored recommendations that encourage adherence to recovery self-care.
OBJECTIVE: The purpose of this project was to design an artificial intelligence (AI) application capable of addressing common patient questions about self-care after hospital discharge following a sternotomy. The intended outcome is to provide patients with pertinent information to mitigate the risk of postoperative complications that could lead to readmission.
METHODS: A Beta version of a novel AI application was developed to enhance the understanding of discharge instructions for patients undergoing sternotomy. Seventy-five potential patient inquiries were developed and entered into the AI application. For each inquiry, the application generated responses based on 50 scholarly articles. Although intended for future patient use, this initial beta version was not deployed to real patients; rather, it was developed and evaluated by the research team, who reviewed each AI response and assigned it as "thumbs up," "thumbs down," or "neither."
RESULTS: The AI application successfully answered all 75 questions. Of these, 65 responses (86.7%) received "thumbs-up" ratings, indicating that they were comprehensive, while 10 responses (13.3%) were marked as "neither" due to convoluted or incomplete information.
CONCLUSION: Tailored patient education is critical for reducing postoperative complications after discharge. An AI application can serve as an effective tool for personalized education. Initial evaluations should be conducted by a cardiac surgery team to assess accuracy and relevance, followed by testing on actual patients undergoing sternotomy to assess usability before wider implementation.
OBJECTIVE: The purpose of this project was to design an artificial intelligence (AI) application capable of addressing common patient questions about self-care after hospital discharge following a sternotomy. The intended outcome is to provide patients with pertinent information to mitigate the risk of postoperative complications that could lead to readmission.
METHODS: A Beta version of a novel AI application was developed to enhance the understanding of discharge instructions for patients undergoing sternotomy. Seventy-five potential patient inquiries were developed and entered into the AI application. For each inquiry, the application generated responses based on 50 scholarly articles. Although intended for future patient use, this initial beta version was not deployed to real patients; rather, it was developed and evaluated by the research team, who reviewed each AI response and assigned it as "thumbs up," "thumbs down," or "neither."
RESULTS: The AI application successfully answered all 75 questions. Of these, 65 responses (86.7%) received "thumbs-up" ratings, indicating that they were comprehensive, while 10 responses (13.3%) were marked as "neither" due to convoluted or incomplete information.
CONCLUSION: Tailored patient education is critical for reducing postoperative complications after discharge. An AI application can serve as an effective tool for personalized education. Initial evaluations should be conducted by a cardiac surgery team to assess accuracy and relevance, followed by testing on actual patients undergoing sternotomy to assess usability before wider implementation.