Insight into Health Care Outcomes for Persons Living with Heart Failure Using Health Data Analytics

Area of Science:

• Health Services Research
• Health Data Analytics
• Cardiology

Background:

• Congestive Heart Failure (CHF) is a leading cause of hospital admissions.
• Effective management of CHF is crucial for patient outcomes and healthcare system efficiency.
• Bundled care models are increasingly explored to improve patient care coordination and reduce costs.

Purpose of the Study:

• To evaluate the impact of the Integrated Funding Model (IFM) on healthcare outcomes for Congestive Heart Failure (CHF) patients.
• To determine the effectiveness of a bundled care pathway intervention for CHF management.
• To utilize Health Data Analytics for assessing intervention efficacy.

Main Methods:

• The study employs the Integrated Funding Model (IFM) to measure the impact of a bundled care approach.
• Data analysis focuses on a 60-day period post-discharge for Congestive Heart Failure (CHF) patients.
• Health data analytics are utilized to assess healthcare outcomes.

Main Results:

• The Integrated Funding Model (IFM) provides a framework for measuring the impact of bundled care interventions.
• Analysis of health data is key to understanding the effectiveness of the IFM pathway.
• Outcomes for Congestive Heart Failure (CHF) patients under the IFM were assessed.

Conclusions:

• The Integrated Funding Model (IFM) offers a method to evaluate bundled care for Congestive Heart Failure (CHF).
• Health data analytics are essential for quantifying the effectiveness of healthcare interventions.
• Further insights into CHF patient outcomes can be gained through pathway analysis.