- Case Studies
The Human Touch, Amplified by AI: A Multi-Location Medical Group's Path to Insurance Eligibility Verification
AI+Human insurance verification helped a multi-location group cut costs, reach 60% automation, 97% accuracy, and boost ROI 10x within the first 12 months.
Table of Contents
Introduction
This case study examines how a multi-location medical group significantly improved its patient insurance eligibility verification process by implementing an AI+Human Driven approach, leveraging insurance verification AI. The integration led to substantial gains in accuracy, automation, and operational efficiency, resulting in reduced claim denials and improved revenue cycle management.
Challenge
A large multi-location medical group faced significant challenges with its traditional patient insurance eligibility verification process. This often involved manual portal checks or direct calls to payers, leading to:
- High operational costs: Labor-intensive manual verification required a substantial in-house RCM team or reliance on offshoring billing.
- Inaccurate benefits data: Manual processes were prone to errors in determining plan active/inactive status, enefits data, and identifying payer carve-outs/TPAs.
- Increased claim denials: Inaccuracies in eligibility and Verification of Benefits (VoB) frequently resulted in insurance claim denials, impacting revenue growth.
- Time-consuming process:The time taken for verification was often extensive, delaying patient intake and subsequent appointments.
Solution: Implementing Insurance Verification AI+Human Driven Platform
The medical group partnered with us to deploy an AI-first and Webhooks-based machine learning system designed to predict accurate benefits data. This solution integrated seamlessly into their existing Revenue Cycle Management (RCM) journey at multiple points, including patient intake, initial appointments, repeat appointments, and claim submission.
The core components of the solution included:
- Automated Insurance Discovery: Leveraging AI to quickly identify insurance coverage.
- Eligibility Check and Verification of Benefits (VoB): Automating the verification of patient eligibility and detailed benefits information.
- Network Status Determination: Identifying the patient’s in-network or out-of-network status.
- Real-time Data Exchange: Facilitating the exchange of data from Real-Time Eligibility (RTEs) and Electronic Remittance Advice (ERA) to continuously improve accuracy.
- Human Oversight and Feedback Loop: Integrating human teams for feedback, error reporting, and continuous accuracy metric monitoring.
Implementation & Onboarding (Two-Phase Approach)
We recommended a structured two-phase onboarding process for the medical group’s engineering and development teams:
- Phase One (Up to 30 days): This phase involved a project launch meeting, account configuration, sharing of credentials and API token keys, setting up communication channels, weekly tech check-ins, and completion of the technical integration.
- Phase Two (Up to 3 months): This phase focused on providing ERA claims data to complete the feedback loop, baseline accuracy determination, monthly customer success calls to report on errors and accuracy metrics payer by payer, and a transition to quarterly customer success calls for ongoing monitoring.
Results & Impact
The AI+Human Driven approach yielded significant improvements across key metrics, demonstrating the effectiveness of insurance verification AI:
- Improved Accuracy and Automation Rates: A sample study conducted in September 2024, after three months in production, revealed impressive performance across major payers:
- Aetna: 99.30% API Success Rate, 98.08% Copay Accuracy
- Blue Cross Blue Shield: 95.72% API Success Rate, 95.44% Copay Accuracy
- Cigna: 96.20% API Success Rate, 99.07% Copay Accuracy
- United Healthcare: 94.80% API Success Rate, 94.59% Copay Accuracy
- The P90 for API requests was consistently 3 seconds.
- Reduced Operational Costs: The medical group was able to significantly reduce manual portal checks and direct calls to payers, leading to a shift away from large in-house RCM teams or offshoring billing.
- Enhanced Revenue Growth: The reduction in insurance claim denials, attributed to improved eligibility and VoB accuracy, resulted in an increase in revenue by up to 40%.
- High ROI: The partnership demonstrated a return on investment (ROI) of greater than 10x within the first 12 months.
- Increased Automation: Up to 94% automation of eligibility verification was achieved, with verification times under 30 seconds.
- High Accuracy: The platform delivered up to 97% accuracy per payer across copay/coinsurance and identified up to 93% of carve-outs.
Conclusion
This case study highlights the transformative impact of an AI+Human Driven approach to patient insurance eligibility verification for a multi-location medical group. By integrating our insurance verification AI, the medical group not only streamlined its RCM processes and reduced operational burdens but also significantly improved financial outcomes through higher accuracy and reduced claim denials. This demonstrates a successful model for modern healthcare organizations seeking to optimize their revenue cycle management and enhance patient experience.
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Highlights
- 94% Quality on Verification
- 10x ROI in 12 Months
- Revenue Up by 40%
Client Specs
- Specialty: Multi-location Medical Group
- EHR: eClincalWorks
- Average collections: $250M+
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