Architectural spatial layout design for hospitals: A review

Abstract

The design of hospital spatial layouts is a critical aspect of healthcare architecture, directly influencing patient outcomes, staff efficiency, and the overall quality of care. A well-designed hospital layout is essential for ensuring smooth operations, minimizing errors, and improving both patient and staff experiences. This paper reviews the significant advances in the field, particularly focusing on the transition from traditional design methods to the integration of computational techniques and machine learning (ML) in hospital layout planning. Despite these technological advancements, there remains a notable gap in the full adoption and optimization of these methods to effectively address the inherent complexities of healthcare environments. This review identifies that while computational methods and machine learning-driven approaches have brought precision and innovation to hospital design, the challenge lies in balancing these technologies with the expertise and insights of human designers. Moreover, the need for interdisciplinary collaboration between architects, healthcare professionals, and engineers is emphasized as crucial for the successful implementation of advanced design strategies. Insights from this review highlight the potential of future research to bridge the existing gaps, proposing directions for the continuous integration of technology in hospital layout design. • Explores the evolution of architectural layout design methodologies with new technologies. • Reviews broader architectural methods that inform hospital spatial layout design. • Analyses the transition from traditional to machine learning-driven hospital spatial layout design. • Highlights the impact of emerging technologies like machine learning on hospital layout design methodologies, addressing both advantages and disadvantages.

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