Aetna's AI Platform Cuts Medical Record Review Time by 65%
Aetna's new AI Medical Chart Review platform is revolutionizing the way it processes medical records, drastically cutting down manual review time and enhancing patient care outcomes. Discover how this technology is streamlining operations and addressing healthcare challenges.
Key Facts
- Aetna's AI platform reduced manual review by 65%, saving 50,000 work weeks annually.
- Processing 14 million records in two weeks highlights AI's efficiency over traditional methods.
- Improved gap closure rates enhance Star Ratings, boosting reimbursement potential for Aetna.
- Collaborative design with subject matter experts accelerates innovation and reduces bureaucracy.
- Transitioning manual efforts to quality control indicates strategic resource reallocation for efficiency.
Summary
Summary
Aetna, a major health insurance provider, faced the challenge of conducting an extensive annual review of over 10 million medical records to identify gaps in care. By deploying a generative AI-driven Medical Chart Review platform, Aetna reduced the need for manual review by 65%, significantly speeding up the process from weeks or months to just days.
Background
Aetna operates in the health insurance industry and is a large national payer. Prior to the deployment of the AI platform, the company relied heavily on a manual review process for its annual Healthcare Effectiveness Data and Information Set (HEDIS) review, which is essential for identifying gaps in patient care.
Challenge
Aetna needed to address the immense scale of its annual HEDIS review, which typically required around 50,000 work weeks, equivalent to nearly 1,000 full-time employees. The manual review process was time-consuming and inefficient, taking over 20 years for a team of 50 reviewers to complete a single annual review.
Solution
Aetna developed the AI Medical Chart Review platform, leveraging cloud services and generative AI to automatically extract clinically relevant data from medical records. The platform prioritizes records based on the likelihood of measure closure and the strength of clinical evidence. Within six months, the team ideated, designed, and trained a proof of concept that processed millions of records in just two weeks.
Results
The AI Medical Chart Review platform has processed 14 million documents, leading to a 65% reduction in manual review efforts. This efficiency allows Aetna to reallocate resources to quality control and ensure the automated chart review functions correctly. The implementation has also improved gap closure rates, enhancing Star Ratings and increasing reimbursement rates.
Key Insights
Collaboration between engineers and subject matter experts is crucial for rapid development and successful outcomes. Designing with security, compliance, and responsible AI use as core principles from the outset can streamline the deployment process. Small, agile teams can navigate bureaucratic hurdles more effectively, leading to faster innovation.
Customer Testimonial
“We have a large group of amazing trained medical coders who do this every day. This is about making it easier for them by speeding up the process. Something that might have taken weeks or months we can now do in days.” — Nathan Frank, Chief Digital and Technology Officer, Aetna.
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Key Concepts
Definitions
- HEDIS
- The Healthcare Effectiveness Data and Information Set (HEDIS) is a set of performance measures for the managed care industry, developed by the NCQA.
- gen AI
- Generative AI (gen AI) refers to algorithms that can generate new content or data based on training from existing data.
- document intelligence
- Document intelligence is the use of AI technologies to automatically extract and analyze information from unstructured documents.
- cloud services
- Cloud services are computing resources and services delivered over the internet, allowing for scalable and flexible IT solutions.
- large language models
- Large language models are AI systems trained on vast amounts of text data to understand and generate human-like language.
Use Cases
- →Streamlining annual medical record reviews
- →Identifying gaps in patient care
- →Improving patient outcomes
- →Reducing manual review time
- →Automating data extraction from medical records
- →Prioritizing records for human review
Frequently Asked Questions
What is the purpose of Aetna's AI Medical Chart Review platform?
The platform aims to streamline the review of medical records to identify gaps in care, improve patient outcomes, and significantly reduce the time spent on manual reviews.
How much manual review time has Aetna's platform saved?
Aetna's AI Medical Chart Review platform has reduced the need for manual review by 65%, saving thousands of hours annually.
What are HEDIS measures?
HEDIS measures are a set of performance metrics used in the managed care industry to evaluate the quality of care provided by health plans.
What role do large language models play in the platform?
Large language models help the platform decipher medical charts, identify high-value codes, and build correlations necessary for effective data extraction.
Why is collaboration with subject matter experts important?
Collaboration with subject matter experts ensures that the development process is informed by practical insights, leading to better outcomes and faster implementation of the AI solutions.