Low-value health care, de-implementation, and implications for nursing research: A discussion paper.

Globally, the nursing profession constitutes the largest proportion of the health workforce; however, it is challenged by widespread workforce shortages relative to need. Strategies to promote recruitment of the nursing workforce are well-established, with a lesser focus on strategies to alleviate the burden on the existing workforce. This burden may be exacerbated by the impact of low-value health care, characterised as health care that provides little or no benefit for patients, or has the potential to cause harm. Low-value health care is a global problem, a major contributor to the waste of healthcare resources, and a key focus of health system reform. Evidence of variation in low-value health care has been identified across countries and system levels. Research on low-value health care has largely focused on the medical profession, with a paucity of research examining either low-value health care or the de-implementation of low-value health care from a nursing perspective. The objective of this paper is to provide a scholarly discussion of the literature around low-value health care and de-implementation, with the purpose of identifying implications for nursing research. With increasing pressures on the global nursing workforce, research identifying low-value health care and developing approaches to de-implement this care, is crucial.

Using machine learning models to predict falls in hospitalised adults.

BACKGROUND: 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. OBJECTIVE: To determine fall-associated factors and develop high-performance prediction tools for at-risk patients in acute and sub-acute care services in Australia. METHODS: 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). RESULTS: 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. CONCLUSION: The study demonstrated machine learning's potential to predict falls and identify key risk factors. Further validation across diverse populations and settings is essential for broader applicability.