A review of the use of machine learning in predictive analytics for patient health outcomes in pharmacy practice

Abstract

Predictive analytics, empowered by machine learning, has emerged as a transformative force in healthcare, offering unparalleled opportunities for enhancing patient outcomes. The primary focus is on understanding the implications, applications, and challenges associated with the use of machine learning algorithms in predicting patient health outcomes. The paper begins by establishing the context with an overview of predictive analytics in healthcare and its evolution. Emphasis is placed on the critical role of patient health outcomes in pharmacy practice. The review explores the current landscape of predictive analytics in pharmacy practice, detailing traditional approaches, their limitations, and the advantages that machine learning brings to the forefront. An in-depth examination of applications follows, focusing on areas such as medication adherence prediction, disease progression modeling, and personalized medication regimens. Real-world case studies and success stories illustrate the practical impact of machine learning on patient outcomes. Addressing the importance of data sources, the paper discusses the diverse types of data employed in predictive analytics, ranging from electronic health records to patient-generated data and wearables. Ethical and privacy concerns are thoroughly explored, emphasizing the need for responsible data usage. The implications for pharmacists and healthcare providers are discussed, highlighting the evolving role of pharmacists in predictive analytics and the potential benefits and challenges for healthcare providers. The conclusion summarizes key findings and issues a call to action, encouraging further research and adoption of machine learning in pharmacy practice to harness its potential for improving patient outcomes.