Understanding and preventing the spread of both endemic and emerging healthcare-associated infectious diseases throughout hospitals and nursing homes is a national priority. Our work has shown that the many disparate inpatient healthcare facilities in a region can form a complex healthcare ecosystem connected by both direct and indirect patient sharing allowing pathogens in one health care facility to readily spread to other facilities. Our goal is to further develop RHEA (Regional Healthcare Ecosystem Analyst) into a user-friendly software tool that decision makers (e.g., policy makers, funders, product developers and manufacturers, healthcare administrators, infection prevention specialists, researchers, and educators) can readily use to help healthcare ecosystems prevent and control the spread of an endemic pathogen, methicillin- resistant Staphylococcus aureus (MRSA), and an emerging pathogen, carbapenem-resistant Enterobacteriaceae (CRE). This next generation of RHEA will bring multiple innovations by: 1) further elucidating the interconnectedness of the ecosystem and showcasing the value of cooperation among facilities versus the current individual facility approach to infectious disease control, 2) incorporating new healthcare ecosystem responses for endemic MRSA and emerging CRE, 3) continuing our work integrating economic and operational models into a framework of infectious disease epidemiological models, 4) imbuing virtual patients with characteristics linked to MRSA and CRE transmission and outcome risk, 5) building a comprehensive "virtual laboratory" to help address many existing and future healthcare ecosystem infectious disease questions, and 6) building a user-friendly modeling tool that decision makers can use. The project will continue our team's longstanding modeling work and developing computational tools for decision makers via three specific aims. First, completing Specific Aim 1 will expand RHEA by adding the following capabilities: i) integrated clinical outcome and economic models, ii) more extensive and detailed patient characteristics linked to changes in infection risk and costs, iii) expanded HAI control measures, and iv) stochastic and adaptive disease parameters to represent emerging and evolving diseases. Next, Specific Aim 2 will use our newly expanded RHEA framework to model a sample endemic pathogen, MRSA, and a sample emerging pathogen, CRE to identify optimal control strategies even when pathogen characteristics are evolving. Finally, Specific Aim 3 will involve developing a user-friendly interface for RHEA and deploying it as a healthcare ecosystem computational modeling tool that various stakeholders (e.g., policy makers, healthcare administrators, infection control specialists, funders, and product manufacturers) can readily use to make decisions regarding the control of healthcare-associated infections.

Public Health Relevance

Understanding and preventing the spread of healthcare-associated infectious diseases throughout hospitals and nursing homes is a national priority. Simulation models can serve as virtual laboratories to help identify best practice solutions for containing common [such as methicillin-resistant Staphylococcus aureus (MRSA)] and emerging [e.g., carbapenem-resistant Enterobacteriaceae (CRE)] causes of healthcare associated infections (HAIs). Our goal is to develop our software RHEA (Regional Healthcare Ecosystem Analyst) into a computational modeling tool that can be used directly by decision makers to identify, develop, and evaluate strategies and interventions to reduce HAIs across a large geographic region.

National Institute of Health (NIH)
Research Project (R01)
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Gray, Darryl T
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Johns Hopkins University
Public Health & Prev Medicine
Schools of Public Health
United States
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