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Quality Improvement (363) Clinical Informatics (2102) Clinical Information and Decision Making (3455) Decision Support for Health Professionals (2074) Nursing (158) Electronic Health Records (1632) Nursing in a Hospital Setting (209)

Published on in Vol 9, No 11 (2021): November

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/26456, first published .
Doctor assists elderly patient with walker, rehabilitation

Clinical Impact of an Analytic Tool for Predicting the Fall Risk in Inpatients: Controlled Interrupted Time Series

Clinical Impact of an Analytic Tool for Predicting the Fall Risk in Inpatients: Controlled Interrupted Time Series

Authors of this article:

Insook Cho1, 2 Author Orcid Image ;   In sun Jin3 Author Orcid Image ;   Hyunchul Park4 Author Orcid Image ;   Patricia C Dykes2, 5 Author Orcid Image
Article Authors Cited by (14) Tweetations (2) Metrics

Journals

  1. Thapa R, Garikipati A, Shokouhi S, Hurtado M, Barnes G, Hoffman J, Calvert J, Katzmann L, Mao Q, Das R. Predicting Falls in Long-term Care Facilities: Machine Learning Study. JMIR Aging 2022;5(2):e35373 View
  2. O'Connor S, Gasteiger N, Stanmore E, Wong D, Lee J. Artificial intelligence for falls management in older adult care: A scoping review of nurses' role. Journal of Nursing Management 2022;30(8):3787 View
  3. Al Abiad N, van Schooten K, Renaudin V, Delbaere K, Robert T. Association of Prospective Falls in Older People With Ubiquitous Step-Based Fall Risk Parameters Calculated From Ambulatory Inertial Signals: Secondary Data Analysis. JMIR Aging 2023;6:e49587 View
  4. Cho I, Cho J, Hong J, Choe W, Shin H. Utilizing standardized nursing terminologies in implementing an AI-powered fall-prevention tool to improve patient outcomes: a multihospital study. Journal of the American Medical Informatics Association 2023;30(11):1826 View
  5. Cho I, Kim M, Song M, Dykes P. Evaluation of an approach to clinical decision support for preventing inpatient falls: a pragmatic trial. JAMIA Open 2023;6(2) View
  6. Piñeiro M, Araya D, Ruete D, Taramasco C. Low-Cost LIDAR-Based Monitoring System for Fall Detection. IEEE Access 2024;12:72051 View
  7. De Micco F, Di Palma G, Ferorelli D, De Benedictis A, Tomassini L, Tambone V, Cingolani M, Scendoni R. Artificial intelligence in healthcare: transforming patient safety with intelligent systems—A systematic review. Frontiers in Medicine 2025;11 View
  8. Saito C, Nakatani E, Sasaki H, E Katsuki N, Tago M, Harada K. Predictive Factors and the Predictive Scoring System for Falls in Acute Care Inpatients: Retrospective Cohort Study. JMIR Human Factors 2025;12:e58073 View
  9. 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
  10. Wang X, Liu Y, Deng Y, Li Y, Gao Y, Chen X, Zhou J, Zhang W. Development and evaluation of a risk prediction model for unscheduled transfers to the resuscitation unit in emergency observation patients. Hong Kong Journal of Emergency Medicine 2026;33(1) View
  11. Cho I, Shim S, Park H. Cognitive readiness of nurses regarding artificial intelligence predictions: understanding through the dual lens of verbatim and gist knowledge. JAMIA Open 2026;9(1) View
  12. Ball Dunlap P, Marquard J, Delaney C, Aliferis C, Chappell K, Coleman T, Klein P, Yakusheva O, Wolfe I, Michalowski M. Artificial intelligence can replace nursing tasks, but not nurses: Examining artificial intelligence's supports and threats to nursing practice through the lens of the 2025 Nursing Code of Ethics. Nursing Outlook 2026;74(4):102808 View

Conference Proceedings

  1. Zungor O, Uludag Y, Celikel O, Pinarer O. 2024 IEEE International Conference on Big Data (BigData). Enhancing Healthcare Services through User-Centered Data Collection and Analysis View

Dissertations

  1. . Evidence-Based Selection of a Fall Risk Assessment Tool: A Program Evaluation Review. View