Currently submitted to: JMIR Aging
Date Submitted: Mar 29, 2026
Open Peer Review Period: Mar 30, 2026 - May 25, 2026
(currently open for review)
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Artificial Intelligence in Nursing Care for Older Adults in Long-Term Care Facilities: An Umbrella Review
ABSTRACT
Background:
The integration of artificial intelligence (AI) in long-term care (LTC) has been driven by workforce challenges and the potential to enhance resident-centered care and improve care quality and safety. However, the evidence on AI in LTC remains fragmented across various intervention types and outcome domains.
Objective:
This umbrella review aims to synthesize evidence from systematic reviews regarding the use of AI in nursing care for older adults in LTC facilities, focusing on care quality, safety, nursing workflow, decision support, implementation barriers, and ethical concerns.
Methods:
The review followed the Joanna Briggs Institute guidelines and was registered in PROSPERO (CRD420251244061). A comprehensive search of Chinese and international databases was conducted up to February 2026. Eligible studies were systematic reviews (with or without meta-analysis) that focused on AI interventions in LTC for adults aged 60 years or older. AMSTAR 2 was used for methodological quality assessment.
Results:
Six systematic reviews published between 2019 and 2025 were included. AI applications in LTC nursing were categorized into five areas: social and companion robots, environmental sensors, wearable devices, fall detection, and robot-assisted medication management. The reviews most frequently reported positive outcomes in psychosocial health, particularly in reducing depression, loneliness, and improving social engagement. AI was also associated with benefits in fall surveillance, medication-related care, and nursing workflow. Common barriers included technical limitations, false alarms, privacy concerns, and workflow disruption. AMSTAR 2 quality assessment indicated that 3 reviews were of low quality, and 3 were critically low.
Conclusions:
AI holds promise as an adjunct to resident-centered care in LTC, rather than replacing direct nursing care. It can improve care quality and safety, optimize nursing workflows, and support decision-making. Further studies with rigorous designs and a focus on implementation are needed to strengthen the evidence base and address the identified challenges. Clinical Trial: PROSPERO CRD420251244061; https://www.crd.york.ac.uk/PROSPERO/view/CRD420251244061.
Citation
Request queued. Please wait while the file is being generated. It may take some time.
Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.