The Role of Predictive Analytics in Hospital Acquired Infection Reduction

To avoid unnecessary costs and patient suffering, healthcare providers need to consider infection control solutions and have zero tolerance for hospital acquired conditions (HACs) that only result in patients suffering from infections that result in longer hospital stays. Predictive analytics is one of the ideal ways to help in hospital acquired infection reduction and reduce readmission rates. Infection control prevention solutions from healthcare technology provider Jvion utilize big data and deep machine algorithms to predict and prevent HACs and reduce patient suffering. 

Reduce Patient Suffering and Save Lives

An evidence based approach to infection control prevention is highly effective in reducing infection rates. Patient-centered care can be improved significantly with the use of predictive analytics so that preventative measures can be implemented. Jvion’s RevEgis is one of the solutions that can help predict disease and infection by analyzing phenotypes. The objective is to not only optimize the cost of care but reduce patient suffering and save lives. The well being of patients after they are discharged is now a key element of the Healthcare Reform Act. This is designed to prevent 30 day readmissions, which is something that hospitals are now well aware of since the Centers for Medicare and Medicaid Services (CMS) are set to reduce payments to hospitals with high readmission rates.