This decision-support matrix of interior material performance characteristics moderates infection spread in multiple environments, ranging from hospitality to hospital care and more. Infections spread through surface transmission are common in many environments including hospitals and long-term health care environments. These infections are often preventable with an appropriate, executed strategy, but they account for $28-33 billion in excess health care costs annually. Key insights on infection-causing risks in the built environment can enable infection control teams to quickly intervene and reduce risk, but this information is not currently centralized or quickly accessible.
Researchers at the University of Florida have developed a decision-support matrix of interior material performance characteristics related to reducing bacterial bioburden,. The platform allows users to conduct basic keyword searches or use deep learning tools to extract material performance data that informs design decisions to mitigate infection risks.
This decision-support tool informs design decisions related to material choice to mitigate infection risks in the built environment
This matrix of interior material performance characteristics informs design decisions to mitigate infection risks in the built environment. This platform supports the use of artificial intelligence technologies including machine learning and data mining to reveal common patterns in interior material performance used for designing safety-critical environments such as hospitals. The platform will also be searchable using keywords and can quickly provide material performance data. Interior material performance data are based on a hierarchy of technical performance measures, including robustness, recovery, graceful extensibility, and sustained adaptability.
