Improving Infection Prevention Briefing Through Predictive Predesign: a Computational Approach to Architectural Programming by Evaluating Socioecological Risk Factors

This research aimed to use a computational approach to aid infection prevention strategies during the predesign phase of healthcare environment planning. The objective of this study was to decompose and analyze health system catchment area 'outside design basis factors' such as demographic, public health, and environmental characteristics that may contribute to infection causing pathogen spread in healthcare environments using state of Florida data as a context for analysis. Supervised Machine Learning methodologies were used to determine relationships between Socioecological System (SES) factors and observed incidence rates of specific Hospital Acquired Infections (HAI). Results of the analysis suggest that relevant regional population SES factors, when included as independent variables, can forecast the potential co-occurrence of specific types of HAI in regional health system service areas. Outcomes of the analysis suggest that demographic and regional characteristics are significantly related to the predictive incidence of Antimicrobial-resistant infections in healthcare settings in Florida Agency for Healthcare Administration (ACHA) regions. The outcomes of this research suggest the benefit of using SES computational modeling for decision-making in health system Architectural Programming and predesign. This process allows for manifesting meaningful patterns by applying quantitative methods to analyze patient population characteristics that may influence the prevalence of pathogen spread within the interior environments of healthcare inpatient settings. This information contributes to a deeper understanding of context-specific priorities for operationalizing human-centered Infection Prevention through Design (PtD) infrastructure and planning.