Using Predictive Analytics for Hospital Acquired Condition Prevention

Hospital-acquired condition prevention is essential to avoiding infections that cause serious problems for patients. Healthcare providers must adopt a zero tolerance for hospital-acquired conditions, which can result in patients spending a longer time in the hospital and increase the risk of more serious complications or even death. One of the best ways to reduce readmissions and ensure hospital-acquired condition prevention is through patient-level predictive analytics. Predictive analytics can help healthcare providers identify patient disease cohorts and pin point individual patients at risk of target illnesses. Solutions from healthcare technology providers like Jvion, are designed to predict and prevent hospital acquired conditions to reduce patient suffering and achieve better health outcomes.

The Significance of Predictive Analytics - Use Cases

Predictive analytics can play an integral role in septicicemia prevention and pressure ulcer prevention. Volumes of patient data can be searched to identify high-risk patients so that timely and effective interventions can be applied to prevent disease. Solutions offered by organizations like Jvion deliver predictive analytics that include risk stratification, the simulation of what-if-scenarios, and risk mapping. With the Centers for Medicare & Medicaid penalizing hospitals that have high hospital-acquired condition rates, it is essential for providers to implement solutions that promote disease intervention and ensure sepsis prevention along with other hospital acquired conditions.