From Data to Care: Applying Machine Learning Strategies in Geriatric Nursing for Better Health Outcomes

Abstract

Advances in machine learning present new opportunities to transform geriatric nursing care and improve outcomes for older adults. This study demonstrates a practical application of predictive modeling to a diverse clinical dataset from 384 geriatric patients. Various machine learning algorithms were developed to forecast adverse events, with neural networks exhibiting the highest accuracy (AUC 0.856). Statistical analyses also revealed significant associations between age, comorbidities, functional status, and negative health trajectories. While promising, thoughtfully addressing ethical concerns around algorithmic bias and patient empowerment remains imperative. Interpretability, accountability, and human-centered design can help safeguard vulnerable populations. Nurses must take a leadership role in guiding responsible innovation. If machine learning is applied judiciously to amplify clinical expertise and reveal individual needs, immense benefits are possible. Technologies can uncover life-saving insights, extend overburdened nurses’ reach, enhance early diagnosis, and help customize care plans. The COVID-19 pandemic has only accelerated the urgency around data-driven approaches. Overall, this study provides a strong foundation for future research while highlighting practical and ethical considerations for implementing machine learning to enhance geriatric nursing practice.