The Centers for Medicare and Medicaid Services (CMS) readmission penalties are a significant concern for healthcare organizations, with over 2,500 hospitals being penalized each year, resulting in CMS withholding more than $500 million in payments.
For Westchester Medical Center Health Network (WMCHealth), hospital readmissions carried more than financial consequences. Care managers had to use multiple systems and time-consuming, manual processes to identify recently discharged patients at risk for readmission. These processes limited the effectiveness of the care management team, as care managers lost valuable time searching patient records for data needed to prioritize their workload and choose the right interventions.
To address this problem, the data analytics teams at WMCHealth and network member Bon Secours Charity Health System leveraged artificial intelligence and machine learning to develop a more accurate readmission risk prediction model that would enable care managers to use their time coordinating and engaging with patients more effectively. Results include:
A risk prediction model that is 17 percent more accurate than widely used readmission risk models in identifying patients at high-risk and low-risk for readmission within 30-days.
Care managers obtain follow-up appointments faster, usually within seven days, and connect patients with the services needed to prevent unnecessary visits to the emergency department and readmissions to the hospital.
1,327 hours per year saved, freeing up care managers to spend more time with patients.