Robert James Lucero1, David S Lindberg2, Elizabeth A Fehlberg3, Ragnhildur I Bjarnadottir4, Yin Li5, Jeannie P Cimiotti5, Marsha Crane6, Mattia Prosperi7. 1. 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. 2. University of Florida, College of Liberal Arts and Sciences, United States. 3. RTI International, Research Park Triangle, NC, United States. 4. University of Florida, College of Nursing, United States; University of Florida, Informatics Institute, United states. 5. Emory University, Nell Hodgson Woodruff School of Nursing, Atlanta, GA, United States. 6. UF Health-Shands Hospital, Gainesville, FL, United States. 7. University of Florida, College of Public Health and Health Professions, United States; University of Florida, College of Medicine, United States.
Abstract
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.
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.