Novel Visualization of Clostridium difficile Infections in Intensive Care Units Authors: Sean Yu, MS2, Courtney Hebert, MD, MS1, Albert M. Lai, PhD2, Po-Yin Yen, RN, PhD1, Justin Smyer, MLS(ASCP)CM, MPH, CIC1, Jennifer Flaherty, RN, MPH, CIC1, Julie Mangino, MD1, Susan Moffatt-Bruce MD, PhD, MBA1; 1Institutions: Ohio State University Wexner Medical Center, Columbus, OH, USA; 2Washington University, St. Louis, MO, USA Background Results Approximately 1 in 25 inpatients in U.S. acute care hospitals develops at least one healthcare-associated infection (HAI), Clostridium difficile being the most common pathogen1. Estimates of the total annual financial burden of HAI Clostridium difficile infection (CDI) range from about $1.5 billion to $3.2 billion2,3. Currently, Infection Preventionists (IP) in the Clinical Epidemiology department perform manual chart review to identify and classify cases of CDI. Our objective was to create a novel visualization tool for CDI to assist in the identification of clusters within hospital ICU units by leveraging existing Electronic Health Records (EHR), Geographic Information Systems (GIS), and IT infrastructure at the Ohio State University Wexner Medical Center (OSUWMC). Our simple algorithm correctly identified all 265 cases of Hospital-Acquired Clostridium difficile Infections throughout OSUWMC from 01/01/2015 to 11/30/2015 with a Positive Predictive Value of 96.3%. On the survey for the initial Tableau prototype, all users “strongly agreed” that the dashboard would be a positive addition to Clinical Epidemiology and would allow them to present HAI information to others more effectively. All “agreed” or “strongly agreed” that they felt confident in manipulating the dashboard to demonstrate to others and that it was easy to learn and use. Comments during the think-aloud sessions were centered around specific features such as improving the intuitiveness of changing the date ranges, adding enteric contact isolation information, and showing POA CDI cases as well as HAI CDI cases. These features were or will be considered for future iterations of the visualization tool. Methods Conclusion EHR data was used to classify patients as HAI CDI using the CDC NHSN (National Healthcare Safety Network) guidelines, then validated against the IP-curated surveillance database. Hospital floor plans, provided by GIS team within the OSU Planning and Real Estate department, were manually annotated then used to construct the dashboard. Several options were considered for the base in which the visualization tool would be developed including ArcGIS and Web Application. Tableau was chosen due to its quick prototyping capabilities. A formal usability study was performed with IPs using the think-aloud protocol given specific tasks, followed up by a Likert-scale survey based on the Health IT Usability Evaluation Scale. Using integrated data, we were able to automate hospital-acquired CDI surveillance and display results on an interactive indoor map of the hospital. Overlaying clinical information on interactive hospital floor plan maps can assist in not only the surveillance of other HAIs, but also in detecting hot spots for various risk factors including, but not limited to fall risk. Development and operationalization of integrated clinical decision support tools require the support of key stakeholders, which in our case included the Clinical Epidemiology department, Facilities Information and Technology Services’ GIS department, Hospital Administration, and Hospital IT teams. OSUWMC IT Information Warehouse OSU GIS Department ADT Data Clinical Lab Results Hospital Floor Plans CDC NHSN Guidelines and Validation from Infection Preventionists Healthcare-Associated Clostridium difficile Infections Interactive Healthcare-Associated Infection Dashboard Screenshot of visualization tool using fabricated data Data processing pathway References Magill SS, Edwards JR, Bamberg W, Beldavs ZG, Dumyati G, Kainer MA, et al. Multistate Point-Prevalence Survey of Health Care–Associated Infections. N Engl J Med. 2014 Mar 27;370(13):1198–208. Zimlichman E, Henderson D, Tamir O, Franz C, Song P, Yamin CK, et al. Health Care–Associated Infections. JAMA Intern Med. 2013 Dec 9;173(22):2039. O’Brien JA, Lahue BJ, Caro JJ, Davidson DM. The Emerging Infectious Challenge of Clostridium difficile-Associated Disease in Massachusetts Hospitals: Clinical and Economic Consequences. Infect Control Hosp Epidemiol. 2007 Nov 2;28(11):1219–27. Davis GS, Sevdalis N, Drumright LN. Spatial and temporal analyses to investigate infectious disease transmission within healthcare settings. J Hosp Infect. 2014 Apr;86(4):227–43. This project was supported by the Institute for the Design of Environments Aligned for Patient Safety (IDEA4PS) at The Ohio State University which is sponsored by the Agency for Healthcare Research & Quality (P30HS024379). The authors’ views do not necessarily represent the views of AHRQ.