The ReAdmit module prioritizes the highest risk patients for optimized care coordination, pre-discharge planning, and transitional care. It denotes appropriate interventions that lower readmission risk.
Readmissions are costly
Preventable readmissions cost health systems billions of dollars annually and significantly impact the quality of life of patients. Reducing readmission rates translates to lower health care costs, better patient outcomes, and an overall healthier society.
Predict the risk
Built on Pieces’ proprietary machine learning architecture, ReAdmit predicts the risk of readmission for a patient within 48 hours of admission. It provides insights about the patient’s risk factors which allows prioritizing high-risk patients and gives actionable steps.
How ReAdmit helps
ReAdmit facilitates improved outcomes by identifying the underlying reasons for readmission, allowing for appropriate prioritization and risk mitigation.
The Pieces ReAdmit module consists of a proprietary All-Cause Readmission Risk score, Readmission Contextual Information, and Care Pathways.
Identify patients at highest risk of readmission.
Understand the high-risk clinical and social conditions that influence readmission risk.
Focus on the factors that are influencing a patient?s readmission risk.
Intervene to address identified factors and reduce readmission risk.
40% relative reduction in readmission rate when transitional care staff provided specific interventions on patients identified as having medication non-compliance.
Over 25% reduction in readmission rate among high/very high-risk patients when transitional care interventions were provided.
25% reduction in readmission rate with increased scheduling of pre-discharge follow-up appointments.
System VP Chronic Care Continuum and Quality Improvement Baylor Scott and White
“Pieces has allowed us to identify the right population and right intervention that works in reducing readmission”