The Challenge

The client faced multiple challenges in navigating fragmented payer systems and diverse insurance plans, which led to complications in prior authorization (PA) and benefits verification. High rejection rates, delays in PA approvals, and inefficient manual processes resulted in operational bottlenecks, increased costs, and non-compliance risks. Also, inconsistent PA success rates due to varying payer requirements and patient demographics compounded the need for scalable solutions to address the increasing complexity of multi-payer and multi-product patient support scenarios.

Our Solution

Agilisium developed an AI-driven solution to streamline and enhance the PA process:

1
Democratized Data Access
Developed a non-SQL method for querying data within the EDF, eliminating the need for SQL knowledge.

AI-Powered PA Predictions

Implemented models to predict PA requirements, improving predictability and reducing approval delays.

2

Real-Time Benefits Integration

Integrated benefits investigation into existing workflows, boosting efficiency.

3

Machine Learning for PA Success Rates

Deployed algorithms to assess PA success probabilities dynamically.

4

Automated Insurance Coverage Predictions

Enabled faster patient onboarding with automated, accurate coverage insights.

5

Advanced Data Integration

Ensured high-quality, compliant data through sophisticated integration and processing techniques.

Key Impact
25%
Reduction in PA Processing Time
20%
Decrease in Operational Costs
The customer
Leading biotechnology company pioneering scientific breakthroughs for serious and life-threatening diseases.

The Outcomes

Improved Healthcare Operations & Faster Patient Access to Insurance Benefits

Faster, more efficient patient access to critical therapies, with reduced costs and higher prior authorization approval rates.

25% Reduction in PA Processing Time

Streamlined workflows accelerates decisions and improved patient access.

30% Increase in Approval Rates

Enhanced accuracy in predicting PA outcomes leads to higher insurance approval rates.

20% Decrease in Operational Costs

Automation of manual processes reduced labor costs and minimized errors.

15% Improvement in Patient Onboarding Efficiency

Faster and more accurate insurance coverage decisions accelerated the enrollment process.

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