ArticleJournal of medical Internet research2019
Novel Approach to Inpatient Fall Risk Prediction and Its Cross-Site Validation Using Time-Variant Data.
Article in Journal of medical Internet research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- PANDIA: Personalized neuro-symbolic multimodal fusion for interpretable neonatal pain assessment.PLOS digital health · 2026Article
- Enhancing Adverse Event Reporting With Clinical Language Models: Inpatient Falls.Journal of advanced nursing · 2025Observational
- Predictive Factors and the Predictive Scoring System for Falls in Acute Care Inpatients: Retrospective Cohort Study.JMIR human factors · 2025Article
- 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 : JAMIA · 2023Article
- Evaluation of an approach to clinical decision support for preventing inpatient falls: a pragmatic trial.JAMIA open · 2023Article
- The U-shaped association between hospitalization time and fall incidence in inpatients using publicly available data: A cross-sectional study in Japan.Nursing open · 2023Article
- Development and validation of interpretable machine learning models for inpatient fall events and electronic medical record integration.Clinical and experimental emergency medicine · 2022Article
- Artificial intelligence for falls management in older adult care: A scoping review of nurses' role.Journal of nursing management · 2022Article
- Patient-Level Fall Risk Prediction Using the Observational Medical Outcomes Partnership's Common Data Model: Pilot Feasibility Study.JMIR medical informatics · 2022Article
- Clinical Impact of an Analytic Tool for Predicting the Fall Risk in Inpatients: Controlled Interrupted Time Series.JMIR medical informatics · 2021Article
- Digital Health Transformation of Integrated Care in Europe: Overarching Analysis of 17 Integrated Care Programs.Journal of medical Internet research · 2019Article
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5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundElectronic medical records (EMRs) contain a considerable amount of information about patients. The rapid adoption of EMRs and the integration of nursing data into clinical repositories have made large quantities of clinical data available for both clinical practice and research.
objectiveIn this study, we aimed to investigate whether readily available longitudinal EMR data including nursing records could be utilized to compute the risk of inpatient falls and to assess their accuracy compared with existing fall risk assessment tools.
methodsWe used 2 study cohorts from 2 tertiary hospitals, located near Seoul, South Korea, with different EMR systems. The modeling cohort included 14,307 admissions (122,179 hospital days), and the validation cohort comprised 21,172 admissions (175,592 hospital days) from each of 6 nursing units. A probabilistic Bayesian network model was used, and patient data were divided into windows with a length of 24 hours. In addition, data on existing fall risk assessment tools, nursing processes, Korean Patient Classification System groups, and medications and administration data were used as model parameters. Model evaluation metrics were averaged using 10-fold cross-validation.
resultsThe initial model showed an error rate of 11.7% and a spherical payoff of 0.91 with a c-statistic of 0.96, which represent far superior performance compared with that for the existing fall risk assessment tool (c-statistic=0.69). The cross-site validation revealed an error rate of 4.87% and a spherical payoff of 0.96 with a c-statistic of 0.99 compared with a c-statistic of 0.65 for the existing fall risk assessment tool. The calibration curves for the model displayed more reliable results than those for the fall risk assessment tools alone. In addition, nursing intervention data showed potential contributions to reducing the variance in the fall rate as did the risk factors of individual patients.
conclusionsA risk prediction model that considers longitudinal EMR data including nursing interventions can improve the ability to identify individual patients likely to fall.
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