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

Clinical Information and Decision Making (1407) Decision Support for Health Professionals (1186) Electronic Health Records (1053) Falls Prevention in the Elderly (69)

Published on in Vol 21, No 2 (2019): February

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/11505, first published .
Novel Approach to Inpatient Fall Risk Prediction and Its Cross-Site Validation Using Time-Variant Data

Novel Approach to Inpatient Fall Risk Prediction and Its Cross-Site Validation Using Time-Variant Data

Novel Approach to Inpatient Fall Risk Prediction and Its Cross-Site Validation Using Time-Variant Data

Authors of this article:

Insook Cho1 Author Orcid Image ;   Eun-Hee Boo2 Author Orcid Image ;   Eunja Chung3 Author Orcid Image ;   David W. Bates4 Author Orcid Image ;   Patricia Dykes4 Author Orcid Image
Article Authors Cited by (18) Tweetations (2) Metrics

Journals

  1. Baltaxe E, Czypionka T, Kraus M, Reiss M, Askildsen J, Grenkovic R, Lindén T, Pitter J, Rutten-van Molken M, Solans O, Stokes J, Struckmann V, Roca J, Cano I. Digital Health Transformation of Integrated Care in Europe: Overarching Analysis of 17 Integrated Care Programs. Journal of Medical Internet Research 2019;21(9):e14956 View
  2. Hendrich A, Bufalino A, Groves C. Validation of the Hendrich II Fall Risk Model: The imperative to reduce modifiable risk factors. Applied Nursing Research 2020;53:151243 View
  3. Walker K, Jiarpakdee J, Loupis A, Tantithamthavorn C, Joe K, Ben-Meir M, Akhlaghi H, Hutton J, Wang W, Stephenson M, Blecher G, Buntine P, Sweeny A, Turhan B. Predicting Ambulance Patient Wait Times: A Multicenter Derivation and Validation Study. Annals of Emergency Medicine 2021;78(1):113 View
  4. Walker K, Jiarpakdee J, Loupis A, Tantithamthavorn C, Joe K, Ben-Meir M, Akhlaghi H, Hutton J, Wang W, Stephenson M, Blecher G, Paul B, Sweeny A, Turhan B. Emergency medicine patient wait time multivariable prediction models: a multicentre derivation and validation study. Emergency Medicine Journal 2022;39(5):386 View
  5. Cho I, Jin I, Park H, Dykes P. Clinical Impact of an Analytic Tool for Predicting the Fall Risk in Inpatients: Controlled Interrupted Time Series. JMIR Medical Informatics 2021;9(11):e26456 View
  6. Ladios‐Martin M, Cabañero‐Martínez M, Fernández‐de‐Maya J, Ballesta‐López F, Belso‐Garzas A, Zamora‐Aznar F, Cabrero‐Garcia J. Development of a predictive inpatient falls risk model using machine learning. Journal of Nursing Management 2022;30(8):3777 View
  7. Jung H, Yoo S, Kim S, Heo E, Kim B, Lee H, Hwang H. Patient-Level Fall Risk Prediction Using the Observational Medical Outcomes Partnership’s Common Data Model: Pilot Feasibility Study. JMIR Medical Informatics 2022;10(3):e35104 View
  8. 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
  9. Damoiseaux-Volman B, van Schoor N, Medlock S, Romijn J, van der Velde N, Abu-Hanna A. External validation of the Johns Hopkins Fall Risk Assessment Tool in older Dutch hospitalized patients. European Geriatric Medicine 2022;14(1):69 View
  10. Hsu Y, Kao Y. Can the Electronic Health Record Predict Risk of Falls in Hospitalized Patients by Using Artificial Intelligence? A Meta-analysis. CIN: Computers, Informatics, Nursing 2023;41(7):531 View
  11. Chen Z, Zhong Q, Chen Y, Chen L, Peng H. The U‐shaped association between hospitalization time and fall incidence in inpatients using publicly available data: A cross‐sectional study in Japan. Nursing Open 2023;10(3):1526 View
  12. Shim S, Yu J, Jekal S, Song Y, Moon K, Lee J, Yeom K, Park S, Cho I, Song M, Heo S, Hong J. Development and validation of interpretable machine learning models for inpatient fall events and electronic medical record integration. Clinical and Experimental Emergency Medicine 2022;9(4):345 View
  13. 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
  14. 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
  15. Jahandideh S, Hutchinson A, Bucknall T, Considine J, Driscoll A, Manias E, Phillips N, Rasmussen B, Vos N, Hutchinson A. Using machine learning models to predict falls in hospitalised adults. International Journal of Medical Informatics 2024;187:105436 View
  16. Sihag G, Delcroix V, Grislin-Le Strugeon E, Siebert X, Piechowiak S, Puisieux F. Combining real data and expert knowledge to build a Bayesian Network — Application to assess multiple risk factors for fall among elderly people. Expert Systems with Applications 2024;252:124106 View

Books/Policy Documents

  1. Matheny M, Ohno-Machado L, Davis S, Nemati S. Clinical Decision Support and Beyond. View