Identifying Risk Factors for Readmissions with More Complete Data
Our client is a unified health system with more than 100 locations and 11,000 employees.
80% OF DATA WAS UNAVAILABLE TO EXISTING SYSTEMS
Per the Center for Healthcare Quality & Payment Reform, “one of the best ways for communities to reduce healthcare costs quickly and improve patient care in the process is to implement initiatives to reduce hospital readmissions.”
The U.S Department of Health & Human Services estimates that avoidable hospital readmissions make up more than $17 billion in Medicare expenses. To help address these rising expenses, the Hospital Readmissions Reduction Program adjusts Medicare payments for hospitals with higher-than-expected 30-day readmission rates for targeted clinical conditions such as heart attacks, heart failure, and pneumonia. By reducing payments, hospitals and healthcare providers are financially incented to improve the quality of care and identify patients or cases with a high likelihood of readmission.
To meet the goals set by the Hospital Readmissions Reduction Program, this unified health system needed a better way to predict the propensity of a patient to readmit. Unfortunately, 80% of data was invisible to legacy systems because it was unstructured. Being able to uncover insights in that data would prove invaluable when predicting triggers for readmissions.
After identifying readmissions indicators, the health system would be in a better position to develop action plans to improve clinician and patient engagements.