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This paper is in the following e-collection/theme issue:

Artificial Intelligence (4741) Nursing (162) Falls Prevention in the Elderly (204) Falls and Emergency Detection with Sensors and other Technologies (81) Accident and Injury Prevention (76) Injury Surveillance (47)

Published on in Vol 27 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/71034, first published .
Elderly man with white hair sitting in armchair, head bowed over cane

The Potential of AI in Nursing Care: Multicenter Evaluation in Fall Risk Assessment

The Potential of AI in Nursing Care: Multicenter Evaluation in Fall Risk Assessment

Authors of this article:

Ivana Nanevski1 Author Orcid Image ;   Sebastian Jäger1 Author Orcid Image ;   Matthias Schulte-Althoff2, 3 Author Orcid Image ;   Eva-Maria Behnke4 Author Orcid Image ;   Daniel Fürstenau2, 3 Author Orcid Image ;   Felix Biessmann1, 5 Author Orcid Image
Article Authors Cited by (4) Tweetations (11) Metrics

Journals

  1. Osonuga A, Osonuga A, Omeni D, Okoye G, Egbon E, Olawade D. Artificial intelligence in hospital fall Prevention: Current Applications, Challenges, and Future Directions. Safety Science 2026;196:107104 View
  2. Nanevski I, Jäger S, Mohebi M, Schulte-Althoff M, Pohle J, Chandler N, Gubser R, Nowak A, Prasser F, Fürstenau D, Balzer F, Biessmann F. Anonymized but Useful Synthetic Tabular Health Data for AI based Fall Risk Assessment. Scientific Data 2026;13(1) View
  3. Mordiffi S, Guo X, Goh M, Ngiam K, Wong N, Chua J, Furqan M, Chew H. Development and Validation of an Interpretable Machine Learning Model for Inpatient Fall Risk Using Electronic Health Record Data. Nursing Reports 2026;16(8):283 View
  4. Hong H, Kim S, Ryu B. Enhancing multimodal inpatient fall prediction via nursing statement integration within the OMOP common data model. Scientific Reports 2026;16(1) View