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. 

Fraud, Waste & Abuse need not be a major concern with Jvion’s RevEgis

Healthcare fraud waste and abuse affects everyone either directly or indirectly where billions of dollars are lost annually. These losses in turn lead to skyrocketing healthcare costs and ever increasing insurance premiums. The sheer size of the healthcare sector and the money involved facilitates the need for a robust hospital fraud waste and abuse solution to reduce costs associated with the healthcare system.

Use an Intelligent Healthcare System

Unstructured Data use within healthcare holds the key to the development of intelligent healthcare systems. Clinical data is innately complex and 80 percent of all data remains unstructured. However, the important part is realizing value from big data. Healthcare fraud is a reality where individuals and organizations perpetrating fraud are a constantly evolving group looking to make the most of loopholes in the healthcare system. Equally important is healthcare abuse where a fraction of providers believe they can beat the system and earn profits through unwanted surgeries and exaggerated claims. Then again, waste is another major factor that indicates inefficiencies in administrative and operational levels.