Prescriptive Analytics for Preventable Harm: Reducing Healthcare Associated Infections
Every provider is trying to do more with less. And when it comes to lowering Healthcare Associated Infections (HAIs), many are struggling to balance cost and resources with program goals.
Across the nation, there is significant variation in the effectiveness of infection control programs (Stone, et al. 2014). This is in large part due to understaffed infection preventionists, inconsistent policies, underutilized technologies, and a lack of clinician adherence to infection prevention policies. Taken together, these trends along with heavy resource constraints add up to missed prevention opportunities, patient deterioration, and a hard hit to a provider's bottom line.
Using the prescriptive analytics enabled by the Jvion Machine, providers can see the future state of each patient within their walls and pin point those who are at an increased risk of infection or adverse event. And the machine extends this vision to deliver the recommended actions that will drive to the best health outcome for an individual patient. Using the Jvion Machine, providers are pin-pointing--with unmatched precision--the patients who are at risk; prioritizing activities to stop potential infections; and applying Jvion's recommended actions to achieve drastic infection control goals.
The engine that powers the Jvion Machine's risk trajectory insights and recommendations is addresses the pitfalls of common predictive and machine learning model solutions. The machine accounts for the entire patient including the exogenous factors such as socioeconomic status, behavioral variables, and environmental elements that impact health. It leverages this capability to more precisely identify individual patients who are on a trajetory toward an HAI, determine if that trajectory can be change, and the individualized interventions that will improve a patient's outcome. What it delivers is a full and precise portrait of the future patient that empowers clinicians with the exact actions that will reduce risk and improve health across all HAI events including CLABSI, VAP, SSI, MRSA, Sepsis, and C. Difficile.
Providers take this information and use it to better focus their efforts and resources, and apply recommended actions to most effectively reduce the probability that someone will get an infection. And they are using the machine across care settings including high-risk transition points to drive better health outcomes throughout the entire episode of care.
Learn how hospitals across the country are using the Jvion Machine to stop patient illness, improve intervention effectiveness, and drive toward value-based models of care and reimbursement.