Predictive analytics in healthcare uses statistical techniques and models to analyze historical and current data to make predictions about future outcomes. In the healthcare context, it often involves analyzing medical records and patient data to predict trends and outcomes, such as the risk of disease, hospital readmissions, or the effectiveness of treatments.
Published in Chapter:
The Future of Healthcare and Patient-Centric Care: Digital Innovations, Trends, and Predictions
Shaik Aminabee (V.V. Institute of Pharmaceutical Sciences, Gudlavalleru, India)
Copyright: © 2024
|Pages: 23
DOI: 10.4018/979-8-3693-1214-8.ch012
Abstract
The future of healthcare is a dynamic landscape characterized by rapid advancements, evolving patient needs, and transformative technologies. This chapter explores key trends and predictions shaping the industry. It covers the integration of AI, telemedicine, genomics, and patient empowerment. These shifts promise a healthcare ecosystem that is more efficient, accessible, and personalized than ever before. However, they also present challenges, including data privacy, ethical considerations, and equitable access. Navigating this evolving healthcare landscape will require a thoughtful balance of innovation and ethical practice, ensuring that the future of healthcare benefits all segments of society. The chapter aims to equip stakeholders with insights and strategies to navigate this complex landscape, advocating for a healthcare future that prioritizes patient-centricity while embracing technological progress in a way that is inclusive and beneficial for all.