Many hospitals employ ambulatory care coordinators to coordinate the care of recently discharged patients and prioritize attention to those at high risk of 30-day readmission. But a lack of timely information and software to accurately predict readmission risk means ambulatory care managers often lack the tools to identify and prioritize high-risk patients. Valuable care management resources are spent manually pulling data from patient records to prioritize workloads and determine interventions.
A regional health system in New York remedied this by applying machine learning to multiple data sources and creating a risk model that identifies high- and low-risk patients more effectively. The payoff, as attendees will learn, has been improved organizational efficiency by enabling care managers to spend more time with those patients most in need of their care.
• Evaluate your organization’s ability to use machine learning tools to build a model for predicting readmission risk.
• Describe how the risk scores from such a model can be made actionable in the workflow of care managers.
• Demonstrate how a discharge platform that includes discharge lists and risk scores can be combined with EHR data to help care managers determine priorities and select interventions.