Deconstructing the One-Size-Fits-All Model: A Systematic Review of Personalized Acute Care Strategies

Abstract

This review examines the emergence of personalized medicine in acute care settings, where traditional standardized protocols often fail to meet individual patient needs. Personalized medicine aligns diagnosis and treatment with unique patient characteristics by incorporating data-driven methods, such as genomics, big-data analysis, and patient-centered care models, to improve outcomes. A systematic overview of recent studies (2024–2025) from Springer, Science Direct, BMC Health Services Research, and NIH reveals that personalized approaches enhance diagnostic accuracy, shorten treatment delays, and increase patient satisfaction. The integration of AI and ML-based technologies further refines intervention precision and effectiveness. However, challenges in data integration, ethical considerations, and resource constraints necessitate robust infrastructure, interdisciplinary collaboration, and supportive policy initiatives for broad adoption. Addressing these barriers is critical to ensuring that personalized acute care delivers more effective healthcare and better patient outcomes.

Keywords: personalized medicine, acute care, precision healthcare, patient-centered care, big data analytics