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Artificial Intelligence System That Forecasts Infection-Causing Risks in Healthcare Environments

Predicts Infection Risks and Tests Infection Moderation Strategies in Inpatients Healthcare Environments

This Human-in-the-Loop (HITL) approach to applied Artificial Intelligence predicts infection-causing risks in inpatient healthcare environments and assesses the effectiveness of infection moderation strategies. One in 20 hospital patients contracts a healthcare-associated infection; 1.6-3.8 million healthcare-associated infections occur annually in long-term care facilities. These infections increase the need for secondary treatments and account for $28-33 billion in excess health care costs annually. Healthcare-associated infections are often preventable when appropriate intervention strategies are applied. Insights on infection-causing risks in the inpatient environment and assessments of potential moderation strategies can enable infection control teams to apply intervention procedures quickly and effectively.


Researchers at the University of Florida have developed an artificial intelligence-driven predictive process model that assesses infection-causing risks and tests infection moderation strategies in healthcare environments to support infection control team decision making.




HITL artificial intelligence that provides insights to infection control team decision-making, enabling them to implement effective strategies for moderating infection risks in inpatient settings



  • Evaluates risk associated with individual components of an inpatient healthcare environment, allowing infection control teams to implement targeted risk moderation strategies
  • Predictively models infection moderation strategy outcomes, identifying the most effective strategy prior to implementation
  • Provides environment-specific risk assessments and predictions, enabling infection control teams to apply strategies that will be effective in a specific inpatient environment
  • Uses a translatable framework, making this approach versatile and broadly applicable to assessing infection risks in other systems such as public transportation or university campuses


Healthcare-associated infections are dangerous and costly, but can be preventable when appropriate intervention strategies are applied. This trademarked artificial intelligence system, the Resilience Inference System for Performance Safety (RISPSTM), is a data-driven, decision support tool that uses risk analysis and resilience assessment to generate performance safety outcomes that provide information on the infection risk associated with individual components of the inpatient healthcare infrastructure. The predictive algorithms it is composed of help to assess the efficacy of potential infection moderation strategies within the context of that specific healthcare environment.


Dr. Lisa Platt, the inventor, developed her technology while attending The State University of New York at Binghamton.


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