A data-driven and practice-based approach to identify risk factors associated with hospital-acquired falls: Applying manual and semi- and fully-automated methods.

Autor: Lucero RJ; University of Florida, College of Nursing, United States; University of Florida, Center for Latin American Studies, United States; University of Florida, Florida Blue Center for Health Care Quality, United States; University of Florida, Informatics Institute, United states. Electronic address: rlucero@ufl.edu., Lindberg DS; University of Florida, College of Liberal Arts and Sciences, United States., Fehlberg EA; RTI International, Research Park Triangle, NC, United States., Bjarnadottir RI; University of Florida, College of Nursing, United States; University of Florida, Informatics Institute, United states., Li Y; Emory University, Nell Hodgson Woodruff School of Nursing, Atlanta, GA, United States., Cimiotti JP; Emory University, Nell Hodgson Woodruff School of Nursing, Atlanta, GA, United States., Crane M; UF Health-Shands Hospital, Gainesville, FL, United States., Prosperi M; University of Florida, College of Public Health and Health Professions, United States; University of Florida, College of Medicine, United States.
Jazyk: angličtina
Zdroj: International journal of medical informatics [Int J Med Inform] 2019 Feb; Vol. 122, pp. 63-69. Date of Electronic Publication: 2018 Nov 27.
DOI: 10.1016/j.ijmedinf.2018.11.006
Abstrakt: Background and Purpose: Electronic health record (EHR) data provides opportunities for new approaches to identify risk factors associated with iatrogenic conditions, such as hospital-acquired falls. There is a critical need to validate and translate prediction models that support fall prevention clinical decision-making in hospitals. The purpose of this study was to explore a combined data-driven and practice-based approach to identify risk factors associated with falls.
Procedures: We conducted an observational case-control study of EHR data from January 1, 2013 to October 31, 2013 from 14 medical-surgical units of a tertiary referral teaching hospital. Patients aged 21 or older admitted to medical surgical units were included in the study. Manual and semi- and fully-automated methods were used to identify fall risk factors across four prediction models. Sensitivity, specificity, and the Area under the Receiver Operating Characteristic (AUROC) curve were calculated for all models using 10-fold cross validation.
Findings: We confirmed the significance of a set of valid fall risk factors (i.e., age, gender, fall risk assessment, history of falling, mental status, mobility, and confusion) and identified set of new risk factors (i.e., # of fall risk increasing drugs, hemoglobin level, physical therapy initiation, Charlson Comorbity Index, nurse skill mix, and registered nurse staffing ratio) based on the most precise prediction approach, namely stepwise regression.
Conclusions: The use of semi- and fully-automated approaches with expert clinical knowledge over expert or data-driven only approaches can significantly improve identifying patient, clinical, and organizational risk factors of iatrogenic conditions, including hospital-acquired falls.
(Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.)
Databáze: MEDLINE
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