Comprehensive Heart Failure Improvements Lead to Reduced Readmissions, Mortality, and Costs

Despite having an in-depth, individualized heart failure (HF) program, Billings Clinic’s 30-day readmission rate was higher than desired and negatively impacted the costs of care. The organization leveraged comprehensive data and analytics to create an analytical approach for evaluating and caring for patients with HF, successfully enhancing HF care and improving clinical outcomes.

Data Advances Improvements in Stroke Care Delivery, Reducing Mortality

Thibodaux Regional Health System had implemented evidence-based stroke care interventions in its emergency department. However, the organization was not meeting its established goals for early identification and treatment. With strong leadership support and the help of analytics, the organization’s stroke care transformation team was able to identify opportunities for improvement, culminating in improved care delivery through facility-wide automated alerts and a reduction in the need to transfer patients to other facilities for treatment.

Improving Percutaneous Coronary Intervention Reduces Adverse Events and Length of Stay

UnityPoint Health evaluated its percutaneous coronary intervention (PCI) performance and identified the opportunity to further improve. The health system decided to identify ways to improve its PCI outcomes. With its data operating system and a robust suite of analytics tools, UnityPoint Health took a data-driven approach to improving its PCI outcomes.

Artificial Intelligence Improves Accuracy of Heart Failure Readmission Risk Predictions

In the U.S., 5.7 million adults have heart failure (HF), costing the nation an estimated $30.7 billion each year. Learn how MultiCare leveraged AI and machine learning to more accurately predict the readmission risk for patients with HF.

Machine Learning, Predictive Analytics, and Process Redesign Reduces Readmission Rates by 50 Percent

The estimated annual cost of readmissions for Medicare is $26 billion, with $17 billion considered avoidable. Readmissions are driven largely by poor discharge procedures and inadequate follow-up care. Nearly one in every five Medicare patients discharged from the hospital is readmitted within 30 days.

The University of Kansas Health System had previously made improvements to reduce its readmission rate. The most recent readmission trend, however, did not reflect any additional improvement, and failed to meet hospital targets and expectations.

To further reduce the rate of avoidable readmission, The University of Kansas Health System launched a plan based on machine learning, predictive analytics, and lean care redesign. The organization used its analytics platform, to carry out its objectives.

The University of Kansas Health System substantially reduced its 30-day readmission rate by accurately identifying patients at highest risk of readmission and guiding clinical interventions:

• 39 percent relative reduction in all-cause 30-day.
• 52 percent relative reduction in 30-day readmission of patients with a principle diagnosis of heart failure.