Readmission Explorer

The Readmission Explorer analytics accelerator provides data and visualizations to help health systems understand and improve performance against a broad spectrum of readmission metrics—supporting teams in their efforts to improve patient care, increase satisfaction, and lower costs.

This app enables exploration of readmission factors for defined cohorts of patients.

The problem

• Readmissions have a significant impact on a patient’s quality of life and the hospital’s bottom line. Readmissions are a proxy measure for poor-quality care—and CMS imposes penalties based on readmission rates for specific conditions. For these reasons, hospital systems invest significant resources to report and reduce readmissions.
• Readmission reduction efforts should focus on at-risk patients, but defining these cohorts is challenging. Improvement teams need to analyze readmission risk, conditions associated with high readmissions, and other factors to define specific patient cohorts. It can be time-consuming and labor-intensive to create reports for this type of analysis.

Our approach

The Readmission Explorer application delivers an efficient and accurate means of evaluating your hospital readmissions. Providing a range of metrics for all-cause readmissions, the application allows users to select predefined CMS-based cohorts or to define their own cohorts. Users can access additional insight by refining cohorts via patient demographics, discharge status, provider, and other metrics. The result? A clear and credible account of where readmissions—a proxy for poor patient care—might be improved and a means of gauging progress toward goals.

Benefits and features

• Quickly identify the direction your performance is headed. The application highlights trends associated with readmissions and reveals areas where readmissions could be reduced. By addressing negative trends early on, your organization may be able to avoid CMS penalties.
• Focus where it matters most. The application supports a focus on CMS-specific readmission conditions—AMI, Heart Failure, Pneumonia, Total Hip/Total Knee, CABG and COPD—and also allows you to define other groups of interest. The application is flexible and allows you to look at a specialty cohort, use the Health Catalyst-supplied Clinical Hierarchy content, or define custom cohorts by ICD-10 diagnosis and/or procedure codes. And regardless of the starting point for analysis, each cohort and/or method can be further refined by patient demographics, discharge status, provider, and other metrics.
• Do more than monitor: understand. Besides providing a broad spectrum of readmission metrics for all-cause readmissions, the application allows you to perform meaningful investigation of factors (e.g., care variables, complications, pre-existing conditions) associated with readmission and their implications for specific groups of patients across the health system.
• Support data transparency and sharing. The application increases the efficiency and accuracy of readmission data reporting by consolidating data in near real-time and automating preferred visualizations. It provides a single source of truth regarding readmission data—and is simple enough for self-service analysis. By providing the right data at the right time to the right audience, Readmissions Explorer is a powerful tool for collaboration and improvement.

Use cases

• A cardiovascular clinical improvement team uses the accelerator to explore readmissions among their heart failure patient cohort. When are most readmissions occurring—how many days post-discharge? And what are the most common conditions patients are presenting with on readmission? Insight from this analysis helps the team identify ways in which they can alter care on the index admission to specifically address the risk of readmission.
• A pediatric team has recently revamped their asthma patient education processes. Have these efforts impacted readmission? The team uses the Readmission Explorer to gauge the overall impact of the new education program on readmission, identify patient subgroups that have relatively higher or lower readmissions, and explore possible reasons for the variation (for example, the age-appropriateness of education).