Limo utilizes proprietary AI to perform fully autonomous pre-bill reviews of medical charts, identifying missed coding opportunities and potential claim denials with 98.2% cost efficiency compared to human auditors. This technology increases revenue integrity by enhancing coding accuracy and preventing up to 70% of claim denials, ultimately generating measurable revenue growth for healthcare providers.
Funding
Funding not disclosed
Founders
Product
Problem
Healthcare providers face challenges in maximizing revenue integrity due to missed coding opportunities and claim denials, often relying on costly and time-consuming manual chart reviews. Incomplete or inaccurate coding can lead to underbilling, while preventable claim denials impact cash flow and administrative overhead.
Solution
Limo offers an AI-powered pre-bill review solution that autonomously analyzes medical charts to identify missed coding opportunities and potential claim denials. The platform reviews every chart, ensuring comprehensive coverage and flagging potential issues before claims are submitted. By augmenting existing coding workflows, Limo increases coding accuracy, prevents claim denials, and ultimately drives measurable revenue growth for healthcare providers. The AI identifies revenue opportunities and coding mistakes, and then automatically generates any necessary addendums or billing changes.
Target Audience
Limo targets healthcare providers, including primary care providers, hospitals, and billing companies, seeking to improve revenue integrity, reduce claim denials, and optimize coding accuracy.
Features
- Autonomous pre-bill review of medical charts using proprietary AI algorithms
- Identification of missed coding opportunities to increase revenue per patient
- Prevention of up to 70% of claim denials by proactively addressing potential issues
- 100% audit coverage at a fraction of the cost of manual auditing
- AI-powered audit results that enable auditors to review 20x more claims
- Integration with existing technology and workflows, augmenting rather than replacing current processes
- Physician-validated AI algorithms for clinical accuracy