jismart2024087: Data Management Paradigms Based on Real-world Study for Evidence-based Design in Healthcare Environments

Abstract

Evidence-Based Design emphasizes design decision-making based on current best evidence to enhance the effectiveness and functionality of built environments. In healthcare buildings, Evidence-Based Design explores the influence of the physical environment on user demands primarily through a large amount of data from clinical experiments, with Randomized Controlled Trials as a common method. However, the accuracy of the Randomized Controlled Trails outcomes is challenged in complex environments and massive amounts of data. Real-World Study theories, which focus on data from patients in actual environments, provide a way to improve outcome accuracy under such conditions. This study proposes a data management paradigm to enhance the health performance of healthcare environments based on Real-World Study and Evidence-Based Design. The paradigm consists of three stages: data collection, association analysis and evidence utilization. Real-time data on physical factors and user physiological indicators is collected by Internet of Things equipment and then stored in building information models. Machine Learning models can be used to analyze correlations and impact levels of these various types of data. Additionally, under the conditions of multiple objectives and constraints, parametric design tools are used to generate optimal design solutions effectively. The findings of this study propose a paradigm that can be used to inform the design of healthcare environments and promote the intelligent development of construction industries.