We present a comprehensive approach to using electronic medical records (EMR) for constructing contact networks of healthcare workers in a hospital. This approach is applied at the University of Iowa Hospitals and Clinics (UIHC) – a 3.2 million square foot facility with 700 beds and about 8,000 healthcare workers – by obtaining 19.8 million EMR data points, spread over more than 21 months. We use these data to construct 9,000 different healthcare worker contact networks, which serve as proxies for patterns of actual healthcare worker contacts. Unlike earlier approaches, our methods are based on large-scale data and do not make any a priori assumptions about edges (contacts) between healthcare workers, degree distributions of healthcare workers, their assignment to wards, etc. Preliminary validation using data gathered from a 10-day long deployment of a wireless sensor network in the Medical Intensive Care Unit suggests that EMR logins can serve as realistic proxies for hospital-wide healthcare worker movement and contact patterns. Despite spatial and job-related constraints on healthcare worker movement and interactions, analysis reveals a strong structural similarity between the healthcare worker contact networks we generate and social networks that arise in other (e.g., online) settings. Furthermore, our analysis shows that disease can spread much more rapidly within the constructed contact networks as compared to random networks of similar size and density. Using the generated contact networks, we evaluate several alternate vaccination policies and conclude that a simple policy that vaccinates the most mobile healthcare workers first, is robust and quite effective relative to a random vaccination policy.

Many efforts to automatically measure hand hygiene activity depend on radio-frequency identification equipment or similar technology that can be expensive to install. We have developed a method for automatically tracking the use of hand hygiene dispensers before healthcare workers enter (or after they exit) patient rooms that is easily and quickly deployed without permanent hardware.Monitoring the hand hygiene adherence of healthcare workers (HCWs) and providing performance feedback to HCWs is recommended by the Centers for Disease Control and Prevention, 1 the World Health Organization,2 and the Joint Commission.3 Currently, adherence is commonly measured by direct observation; this approach is considered the gold standard for determining adherence.2 ,4 However, observational surveys are labor-intensive and expensive. 4-6 Also, results are susceptible to observer effects, 7 and their reliability can be affected by sporadic sampling. 8 A number of electronic monitoring systems for hand hygiene have been reported, 4 with more under development. Many efforts to directly measure adherence (ie, as opposed to measuring product usage) depend on radio-frequency identification (RFID) infrastructure or similar technology. Unfortunately, these approaches can be prohibitively expensive, because they require the installation of radio antennas or some other equipment (eg, motion sensors) in areas under study. We have developed a relatively low-cost method for automatically tracking the use of hand hygiene dispensers before HCWs enter (or after they exit) patient rooms that is easily deployed without installation of any permanent hardware or wiring. METHODSOur system consists entirely of small credit-card-sized devices called motes. Motes are active, battery-powered, programmable devices consisting of a small processor, flash memory, and an Institute of Electrical and Electronics Engineers (IEEE) 802.15.4-compliant wireless radio. Each mote is programmed to broadcast a message (ie, a time-stamped identity packet) to other motes. Each message can be received by other motes; from a message one can derive the following information: (1) the identifier of the mote that sent the NIH Public AccessAuthor Manuscript Infect Control Hosp Epidemiol. Author manuscript; available in PMC 2011 December 1. NIH-PA Author ManuscriptNIH-PA Author Manuscript NIH-PA Author Manuscript message, (2) the received signal strength, and (3) the time the message was received. These data are recorded on the receiving mote. The motes communicate over unused space in the WiFi spectrum and do not interfere with medical devices.We program our motes to perform 1 of 3 different roles, which we call badges, beacons, and triggers ( Figure 1). Badges are worn by HCWs and are contained in recycled pager cases. Beacons are placed in patient rooms, and triggers are attached to off-the-shelf hand hygiene dispensers. Each of the 3 components is capable of sending wireless messages to the other components and receiving wireless messages from the other component...

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This paper describes a spatial model for healthcare workers' location in a large hospital facility. Such models have many applications in healthcare, such as supporting timeand-motion efficiency studies to improve healthcare delivery, or modeling the spread of hospital-acquired infections. We use our model to estimate spatial distributions for healthcare workers in The University of Iowa Hospitals and Clinics (UIHC), a 700-bed comprehensive academic medical center spanning a total of 3.2 million square feet and employing about 8,000 healthcare workers. We model the UIHC as a metric space induced by walking distance between pairs of rooms, and with each room having a level of attractiveness representing the activity level in that room. We combine this with a model in which each healthcare worker has a center of activity and a probability density function that decays polynomially as we move away from the center. Using 12 million Electronic Medical Record (EMR) logins collected over 22 months, we solve for the model parameters for each room and each healthcare worker using heuristic techniques to make the problem computationally tractable. We then validate the model parameters obtained by comparing realworld expectations of healthcare worker behavior for several job categories to our model predictions (e.g., we verify that Unit Clerks are much more stationary than Respiratory Therapists). Finally we present solutions to two important applications. First, we use healthcare worker spatial distributions to generate random walks representing their movement through the hospital. We use these random walks to construct healthcare worker contact networks in order to study the spread of hospital-acquired infections. Second, using the healthcare worker spatial distributions, we find a near-optimal placement of hospital resources which minimizes the average distance a healthcare worker has to travel to access that resource.

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