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

New Methods (1134) Clinical Information and Decision Making (1915) Natural Language Processing (917) Tools, Programs and Algorithms (376) Falls Prevention in the Elderly (101)

Published on in Vol 22, No 9 (2020): September

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/19516, first published .
Automated Fall Detection Algorithm With Global Trigger Tool, Incident Reports, Manual Chart Review, and Patient-Reported Falls: Algorithm Development and Validation With a Retrospective Diagnostic Accuracy Study

Automated Fall Detection Algorithm With Global Trigger Tool, Incident Reports, Manual Chart Review, and Patient-Reported Falls: Algorithm Development and Validation With a Retrospective Diagnostic Accuracy Study

Automated Fall Detection Algorithm With Global Trigger Tool, Incident Reports, Manual Chart Review, and Patient-Reported Falls: Algorithm Development and Validation With a Retrospective Diagnostic Accuracy Study

Authors of this article:

Elisa Dolci1 Author Orcid Image ;   Barbara Schärer2 Author Orcid Image ;   Nicole Grossmann3, 4 Author Orcid Image ;   Sarah Naima Musy2, 4 Author Orcid Image ;   Franziska Zúñiga4 Author Orcid Image ;   Stefanie Bachnick4 Author Orcid Image ;   Michael Simon2, 4 Author Orcid Image
Article Authors Cited by (11) Tweetations (9) Metrics

Journals

  1. Leurs W, Lammers L, Compagner W, Groeneveld M, Korsten E, van der Linden C. Text mining in nursing notes for text characteristics associated with in-hospital falls in older adults: A case-control pilot study. Aging and Health Research 2022;2(2):100078 View
  2. Nilsson L, Lindblad M, Johansson N, Säfström L, Schildmeijer K, Ekstedt M, Unbeck M. Exploring nursing-sensitive events in home healthcare: A national multicenter cohort study using a trigger tool. International Journal of Nursing Studies 2023;138:104434 View
  3. Hildebrand A, Jacobs P, Folsom J, Mosquera-Lopez C, Wan E, Cameron M. Comparing fall detection methods in people with multiple sclerosis: A prospective observational cohort study. Multiple Sclerosis and Related Disorders 2021;56:103270 View
  4. Cheligeer C, Wu G, Lee S, Pan J, Southern D, Martin E, Sapiro N, Eastwood C, Quan H, Xu Y. BERT-Based Neural Network for Inpatient Fall Detection From Electronic Medical Records: Retrospective Cohort Study. JMIR Medical Informatics 2024;12:e48995 View
  5. Osman M, Cooper R, Sayer A, Witham M. The use of natural language processing for the identification of ageing syndromes including sarcopenia, frailty and falls in electronic healthcare records: a systematic review. Age and Ageing 2024;53(7) View
  6. Groeneveld M, Leurs W, Bouwman A, Schenk J, Lammers L, Dierick A, Korsten E, van der Linden C. Text-based fall prediction in hospital: Development and internal validation of a model to predict in-hospital falls in older patients using free text from daily nursing records. Applied Nursing Research 2025;82:151923 View
  7. Cho I, Park H, Park B, Lee D. Enhancing Adverse Event Reporting With Clinical Language Models: Inpatient Falls. Journal of Advanced Nursing 2025;81(11):8016 View
  8. Xu M, Xiong S, Chen G, Li G, He S. Active surveillance of adverse drug events in hospitalized patients with pulmonary arterial hypertension based on the global trigger tool. Frontiers in Pharmacology 2025;16 View
  9. Gutiérrez-Mendoza L, Manias E, Nicholson P, Gupta P. Predictive values of trigger tools for identifying adverse events in hospitalized patients using a medical record review: a systematic review. International Journal For Quality In Health Care 2025;37(4) View

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  1. Kuske S, Hecker R, Geraedts M. Versorgungsforschung. View

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  1. Mohammed M, Elleithy K, Elmannai W. 2021 4th International Conference on Bio-Engineering for Smart Technologies (BioSMART). KMSAFE APP: Campus Safety Mobile App View