Hospital or Healthcare acquired infections (HAIs), such as central-line associated bloodstream infections (CLABSIs) are a huge problem for patients and providers. Hospital-acquired infections are a frequent complication in hospitalized patients. An estimated one in 10 patients acquires an infection while hospitalized. This solution can monitor hospital acquired infections, and with this the physicians and clinical managers can monitor their efforts and enable better results.
This solution would enable providers to monitor the development of hospital-acquired infections and development reports over time at the hospital, departmental and sectional levels.
Using AI driven models, providers can predict which patients are most likely to develop central-line infections by looking and a variety of data including patient information, treatment history and staff history. With this prediction, clinicians can monitor high-risk patients and intervene to reduce risk. AI driven models can also identify the reasons for increased risk and provide reason codes that point clinicians to recommended treatments and preventative measures for future patients.
This solution is powered by the H2O AI Cloud Driverless AI AutoML, H2O-3, and H2O.ai Wave. The data science approaches include genetic algorithm, advanced feature engineering, classification, GLM, GBM, XGBoost, ensemble stacking, and Shapley value estimation, among others.