Heart Failure

The Heart Failure (HF) analytics accelerator provides data and visualizations to improve patient care—supporting teams in their efforts to decrease mortality, length of stay, avoidable readmissions, and costs, and increase quality of life.

This accelerator drives focus on eliminating unwarranted care variation to improve clinical and financial outcomes.

The problem

HF impacts 6.2 million adults in the United States, and presents a considerable burden to society, diminishing patients’ quality of life and raising healthcare costs. Organizations choose to focus on HF because they see:

• Significant variation in care, despite well established guidelines.
• Opportunity to improve care across the continuum.

Our approach

The HF accelerator supports a disciplined, data-driven approach to the evaluation and care of patients with heart failure. Typical implementations focus on readmission risk stratification, adherence to GDMT, and care transitions—areas that are especially meaningful to improving quality and cost.

Benefits and features

• Data visualization and drill-down capabilities. Dynamic dashboards highlight outcome and process metrics to explore, filter data in real-time, drill-down to patient-level detail, and identify trends as they develop. Organizations are empowered to evaluate details such as medication orders, readmission rates, and costs by location, provider, patient risk cohort, etc.
• Rapid time to value. The accelerator eliminates months of work through pre-configured cohorts, definitions, and process measures that are clinically relevant, standard, and meaningful across domains, ready for customization or adoption in your organization.
• Drive focus on what matters most. Outcome metrics typically include mortality, readmission rate, LOS, and cost per case. Popular process metrics include documentation of ejection fraction, and compliance with care protocols related to GDMT and care transitions.
• Identify and manage comorbidities. Enrich provider awareness of patients with a secondary diagnosis of HF to optimize compliance with GDMT.
• Continually refine your ability to recognize risk and improve treatment. Augmented Intelligence (AI) stratifies risk based on population-specific variables and optimizes care process algorithms, while predicting LOS and readmissions.

Use cases

• The executive team in a large health system would like to improve HF readmission and length of stay performance to move from 70th to 90th percentile performance nationally. They use the app to devise and implement a plan that addresses their unique opportunity.
• The Chief Medical Officer champions a NP-run heart failure clinic would help lower readmissions and improve patient satisfaction. The application is used to monitor outcomes, understand the factors that affect the results, and adjust local processes and interventions for best results.
• The Quality Team wants to understand the effectiveness of their current risk-stratification process and it’s accurately in identifying high-risk patients. They use dashboards to explore the care processes used by the best-performing unit, and the potential impact of widespread adoption of those best practices for the organization.