Filling the Gap in EBD Approaches by Integration of Empirical Data: A Data-Driven EBD Framework and Implementation

Despite the theoretical promises of integrating life-cycle considerations into healthcare facility design, evidence-based design (EBD) remains constrained in practice because of its reliance on subjective insights from a limited number of experts and case studies. This narrow scope restricts the generalizability and strategic utility of EBD in planning and engineering. To address this gap, this study contributes to the body of knowledge in management within the engineering domain by introducing a novel, data-driven framework for EBD that enhances strategic planning and decision-making in healthcare facility design. The proposed data-driven evidence-based design (DD-EBD) framework responds to long-standing critiques in the literature regarding the limitations of traditional EBD approaches, which often depend on subjective insights, small-scale case studies, and expert consensus. By integrating large-scale, historical, and objective data into the design process, this research advances the methodological rigor of EBD and offers a replicable model for deriving environmental strategies grounded in empirical evidence. The DD-EBD framework is structured in three phases: (1) identification of theoretical parameters and contributing factors from existing literature; (2) operationalization and analysis of large-scale empirical data; and (3) prioritization of environmental strategies based on data-informed analytical findings and insights. The framework is demonstrated through a proof-of-concept focused on workplace violence in healthcare facilities, a critical issue with significant operational and safety implications. Using more than 12,000 incident reports from the Injury Tracking Application, the study applies natural language processing and statistical analysis to extract patterns across temporal, spatial, occupational, and organizational dimensions. These findings are then matched with existing EBD strategies to generate a prioritized matrix of interventions. Theoretically, the study contributes a replicable, context-agnostic model for augmenting EBD with empirical evidence, enhancing its internal validity and strategic relevance. Practically, the DD-EBD framework provides a transferable approach to managing complexity in built environment projects and enables healthcare planners and designers to derive actionable insights from national-scale data, improving the reliability and scalability of environmental strategies. This approach offers a pathway to overcome the limitations of traditional EBD and supports more informed, data-driven decision-making in healthcare facility design.