Using machine learning models to predict falls in hospitalised adults

•Falls in healthcare settings can result in significant patient harm and expense.•Identification of patients at risk of falls is necessary for effective fall prevention initiatives.•Information systems present an opportunity to analyse large quantities of data to inform fall prediction.•In this study, patient, workforce and organisational characteristics data were included in machine learning models.•Random forest and deep neural network models accurately predicted patient falls. Identifying patients at high risk of falling is crucial in implementing effective fall prevention programs. While the integration of information systems is becoming more widespread in the healthcare industry, it poses a significant challenge in analysing vast amounts of data to identify factors that could enhance patient safety. To determine fall-associated factors and develop high-performance prediction tools for at-risk patients in acute and sub-acute care services in Australia. A retrospective study of 672,400 patients admitted to acute and sub-acute care services within a large metropolitan tertiary health service in Victoria, Australia, between January 1, 2019, and December 31, 2021. Data were obtained from four sources: the Department of Health Victorian Admitted Episodes Dataset, RiskManTM, electronic health records, and the health workforce dataset. Machine learning techniques, including Random Forest and Deep Neural Network models, were used to analyse the data, predict patient falls, and identify the most important risk factors for falls in this population. Model performance was evaluated using accuracy, F1-score, precision, recall, specificity, Matthew's correlation coefficient, and the area under the receiver operating characteristic curve (AUC). The deep neural network and random forest models were highly accurate in predicting hospital patient falls. The deep neural network model achieved an accuracy of 0.988 and a specificity of 0.999, while the RF achieved an accuracy of 0.989 and a specificity of 1.000. The top 20 variables impacting falls were compared across both models, and 12 common factors were identified. These factors can be broadly classified into three categories: patient-related factors, staffing-related factors, and admission-related factors. Although not all factors are modifiable, they must be considered when planning fall prevention interventions. The study demonstrated machine learning's potential to predict falls and identify key risk factors. Furt