Nosocomial infection modeling and simulation using fine-grained healthcare data

Abstract

Simulation has long been used in healthcare settings to study a range of problems, such as determining ideal staffing levels, allocating patient beds, and assisting with medical decision making. Some of this work naturally focuses on the spread of infection within hospitals, where the importance of hospitals as loci and amplifiers of infection was demonstrated during the 2002-2003 SARS outbreak. Increasingly, fine-grained healthcare data is being collected (e.g., patient care data stored in electronic medical record systems, and healthcare worker data from sources including nurse locator badges), presenting an opportunity to develop models that can drive more realistic simulations. We seek to build a realistic hospital simulator that can be used to answer a wide variety of questions about infection prevention, the allocation and placement of expensive resources, and issues surrounding patient care. Our simulation framework requires three primary inputs: architectural, healthcare worker, and patient data. We used data from the University of Iowa Hospitals and Clinics to build our virtual hospital. We manually constructed a weighted, directed, 19,000 node graph-theoretic representation of the facility based on printed architectural drawings. Using timestamped location information from electronic medical record system logins and algorithms inspired by prior work on location-aware search, each healthcare worker is modeled by one or more "centers" of activity. Centers are determined using a maximum likelihood approach to fit a location and appropriate decay parameters that best describe the observed data. Finally, we developed