Co-Pilot for Long-Term Care
How can AI and digitalization ease the burden on nursing care and improve working conditions? An interdisciplinary research project is developing innovative approaches for the future of long-term care.
The demographic changes in our society are leading to a growing need for care and posing significant challenges to the healthcare system. This is compounded by a growing shortage of skilled workers and an increasing amount of administrative work. This results in a heavy workload and unattractive working conditions.
The preliminary projects within the Databooster and Innoscheck programs have demonstrated great potential for supporting and automating the entire care process. Technologies such as large language models (LLMs), natural language processing (NLP), and robotic process automation (RPA) play a central role in the co-pilot project. LLMs support caregivers in documenting care services using the documentation assistant. The assessment and evaluation assistants automate and professionalize the creation of nursing diagnoses, the definition of care goals and interventions, and their ongoing review.
The consortium (Oase Health Solutions (OHS), Oase Service AG, Swisscom, Eastern Switzerland University of Applied Sciences (OST), and the University of Zurich (UZH)) possesses crucial expertise in nursing and medical informatics for the successful exploration and implementation of the co-pilot project. OST and UZH contribute complementary expertise in artificial intelligence, particularly LLMs and NLP. OHS combines nursing expertise with proven project management experience in the digitalization of nursing care. Oase Service AG has comprehensive industry knowledge and continuously tests project developments in practice. Swisscom provides a secure IT infrastructure for scaling and implementing the solution.
The goal is to create a social innovation through AI "made in Switzerland for Switzerland" to improve working conditions in long-term care. A user-centered approach enables the identification and implementation of the optimal technology mix to achieve the project goals.
Runtime: 01.11.2024 - 30.04.2027
Project funding: Innosuisse
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Theses
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