Financial penalties and the accelerated move toward value-based models of care and reimbursement have further incentivized providers to lower readmission rates. But all too often when a hospital sets out to reduce the readmission rate within their patient population, current processes fell short. They lack the ability to accurately identify and target patients at high-risk of readmission, require hours of researching to understand patient care gaps, do not provide visibility into the most effective interventions based on an individual patient’s risk profile, and lack the ability to identify and reroute high-utilizers to more appropriate care paths.
Predictive analytic solutions that leverage machine learning and artificial intelligence are key to lowering readmissions rates in the at-risk/value-based world we live in because they:
The applicability of predictive outputs is realized across the clinical workflow:
At admit: Initial readmissions predictions are fired and risk reports are generated.
Case manager huddle/shift change: risk report is reproduced and used to help prioritize action plans and patient visits.
Case manager consults: predicted risk is used to help drive clinician/case manager conversations.
At patient discharge: tailored plans are communicated to family members or the transitional facility.
With accurate, patient-level predictions, a provider is better able to target activities and direct resources to those most in need. Moreover, mature predictive analytic solutions provide insights into which intervention is most effective and which patients are most likely to engage with which intervention. For more information on predictive analytics and 30-day readmissions, please visit: https://www.jvion.com/financial-prediction.html
- By Jvion Health