Tackling hospital-acquired infections with systems-based highly-detailed simulations

Background: Hospital acquired infections (HAIs) are a significant problem in healthcare systems around the world. In the United States, it is estimated that up to 1 in 20 hospitalized patients become infected with an HAI. HAIs increase morbidity and mortality, lead to drug-resistant infections, and increase the cost of healthcare. Objective/Purpose: To identify the risk factors for HAI and provide evidence-based best practices for hospitals. Methods: Patient information regarding activities, location, and human contacts was gathered through electronic records from two Virginia hospitals. Additionally, field studies included direct observation of over 25 healthcare disciplines inside the hospitals to develop accurate schedules of healthcare workers. The study utilized both sources of data to determine possible contacts between patients and healthcare workers. The study follows the framework of Macroergonomics, a model which analyses the relationships between work systems inside an organization and develops holistic interventions. The patients, hospital workers, locations, and objects inside the hospital are represented in a highly-resolved computer model (EpiSimdemics) which simulates the virtual spread of pathogens. Simulations are performed to estimate the reduction of HAI transmission for several systemic interventions. Results: The development of holistic interventions that take into consideration different work systems inside a hospital can better focus infection control resources and policies. Complex experiments at the hospital level that include patients, hospital workers, hospital infrastructure, and a specific pathogen are expensive and challenging to conduct. The use of highly-resolved simulation offers a novel and unique technique that can assess a wide range of interventions and provide guidance to infection control policies.