RehaBot – AI as an interface between patients and reha professionals
This project explores how patients can be supported in rehabilitative aftercare by a chatbot based on artificial intelligence. The project is part of the Care@Home initiative of the Canton of Bern.
Factsheet
- Lead school School of Health Professions
- Additional schools School of Social Work
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Institute
Nutrition and Dietetics
Institute on Ageing
Academic-Practice-Partnership Insel Gruppe/ BFH - Funding organisation BFH
- Duration (planned) 01.05.2024 - 31.01.2025
- Project management Prof. Dr. Kai-Uwe Schmitt
- Head of project Prof. Dr. Kai-Uwe Schmitt
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Project staff
Rachel Strahm
Gesche Sabrina Gleichner
Dr. Karin Haas
Prof. Dr. Undine Lehmann
Prof. Dr. Matthias Wilhelm
Dr. Thimo Marcin
Dr. Rafael Wampfler
Prof. Dr. Barbara Solenthaler -
Partner
Berner Reha Zentrum
Inselspital Bern
ETH Zürich - Keywords Care@Home, sarcopenia, rehabilitation, care model
Situation
After discharge from inpatient rehabilitation, everyday life is challenging for many patients. Failure to adjust to the new situation and, if necessary, adapt their lifestyle can lead to a worsening of the underlying disease or rehospitalisation. A chatbot based on artificial intelligence ("RehaBot") linked to health data would give patients the opportunity to receive targeted answers to their individual questions relating to their personal situation. The RehaBot is intended to help patients reintegrate at home, actively promote a healthy lifestyle and, if necessary, network with the relevant players in aftercare to ensure a seamless transition from secondary to tertiary prevention. The improved and more sustainable care should reduce re-admissions. It should also ensure the resource-efficient deployment of specialists.
Course of action
This pilot project explores the use of artificial intelligence and large language models such as GPT-4 in the context of a highly individualised eHealth application. A new model of care for rehabilitative aftercare is being addressed. The needs of those affected (patients and relatives) and professionals in rehabilitative aftercare are recorded, a RehaBot prototype for an exemplary use case "Management of malnutrition and sarcopenia" is created and piloted, and the quality is evaluated. A new RehaBot is intended to provide patients with needs-based, customised information according to their individual health history, regardless of the appointment, without direct consultation with a specialist. However, patients' specific questions and interactions with the RehaBot could recommend or trigger the involvement of appropriate specialists and thus support interdisciplinary aftercare. The opportunities and limitations of the application will also be explored.
