Do payor-based outreach programs reduce medical cost and utilization?

Area of Science:

• Health Services Research
• Applied Health Economics
• Predictive Analytics in Healthcare

Background:

• Growing interest in predictive analytics for reducing avoidable healthcare utilization.
• High-risk patients with congestive heart failure represent a significant cost burden.
• Need for evidence-based interventions to manage chronic conditions effectively.

Purpose of the Study:

• To evaluate the impact of a predictive analytics-driven intervention on healthcare utilization and costs.
• To assess the effectiveness of targeted outreach for high-risk Medicare Advantage patients with congestive heart failure.
• To quantify changes in hospitalizations, emergency room visits, primary care access, and medical costs.

Main Methods:

• Utilized claims data (2013-2017) for Medicare Advantage patients with congestive heart failure.
• Developed a predictive algorithm to generate risk scores for health plan members.
• Employed a difference-in-differences model to analyze intervention effects around arbitrary cutoff points.
• Compared healthcare utilization and costs between intervened and non-intervened patient groups.

Main Results:

• Intervention enrollment decreased hospitalizations by 43% and 50% (probability and number).
• Emergency room visits reduced by 10% (probability) and 14% (number).
• Time to primary care visit decreased by 8.2 days.
• Total medical costs reduced by $716 per member per month within 6 months post-outreach.

Conclusions:

• Predictive analytics-driven interventions are effective in reducing healthcare utilization for high-risk congestive heart failure patients.
• Targeted outreach programs can lead to significant cost savings and improved care coordination.
• This approach demonstrates a viable strategy for managing chronic conditions within Medicare Advantage populations.