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 .
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

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

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