GeoHAI: A novel geographic tool for Hospital Acquired Infection visualization and assessment

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PROJECT SUMMARY Hospital Acquired Infections are common, affecting 3.2% of acute care hospital admissions. Recent reports have shown an improvement in overall HAI rates, primarily driven by improvements in surgical site (SSI) and catheter associated urinary tract infections (CAUTI). Transmissible infections, such as Clostridium difficile (CDI), have not shown the same decrease over time. This may be because prevention of CDI requires a comprehensive hospital-wide approach addressing environmental and patient-level risk factors. Geographic Information Systems (GIS) and spatial analysis techniques have become an important tool in public health informatics because they can integrate a vast number of data sources and explore associations and patterns in the data not visible using traditional biostatistical methods. Applications of GIS and spatial analysis are wide ranging but have largely been ignored in the hospital setting. The objective of this research is to develop a HAI assessment tool, which incorporates geographic data on the hospital and patient-level data from the electronic health record system, that is useful for hospital infection preventionists in better identifying clusters of HAI and assessing potential risk. We bring together a multidisciplinary team of clinical, operational, and academic investigators with expertise in GIS and spatial analysis, patient safety, public health informatics, usability assessment, and mixed- methods evaluation. Our aims are to: 1) create a dynamic, spatially referenced, GIS-ready database, and a set of statistical algorithms for HAI outbreak and risk detection, which are designed for easy implementation at other hospitals; 2) create a Geographic HAI visualization and assessment tool (GeoHAI) that is both usable and useful in supporting infection preventionists in detecting emerging clusters of HAI and high risk areas of the hospital; and 3) Conduct a mixed methods analysis to determine how implementation of GeoHAI influences behaviors, processes, and HAI outcomes. The tool will use spatio-temporal Bayesian models to identify clusters of National Healthcare Safety Network (NHSN)-defined hospital onset CDI and multidrug resistant organisms (MDRO) and predict potential high risk areas given hospital and patient risk factors. Comprehensive studies of the tool's usability and usefulness will be integrated with tool development. Tool development will focus on reproducibility to enable similar work at other hospital systems without local expertise in geographic methods. Unique to our approach is an evaluation strategy that focuses on the reduction of hospital acquired infection, but also seeks to understand how the tool and the information derived from the tool impacts patient safety practices in the hospital. We expect the implementation of this tool to radically change the workflow and speed of response of infection preventionists, greatly improving their ability to prevent HAI instead of reacting after they have occurred.