The Smart Solution to Health Insurance Verification: AI-Powered Intelligence
Overview
Among the top complaints that people have about insurance is the denial of coverage. Most (70%) of healthcare providers have reported increasing denials. Over a quarter (27%) of these denials are blamed on registration and eligibility issues. Our AI dental insurance verification system addresses these challenges by automating the verification process, potentially saving practices $12.8 billion across the industry while reducing verification time by 14 minutes per transaction.
Current Industry Challenges
Approximately 90% of claim denials are preventable, yet the average denial rates range between 8-10%. Denials account for a significant portion of practice expenses, approximately 20%. Unfortunately, 65% of denied claims are never resubmitted. Additionally, 69% of practices have reported an increase in denials, with an average increase of 17%.
Staff often spend considerable time on hold with insurance companies, which leads to delayed verification results. Manual verification processes can consume up to 14 minutes per transaction. These procedures are resource-intensive, impacting overall efficiency.
Each denied claim incurs a cost ranging from $25 to $118. Lost revenue from claims that are not resubmitted contributes to delayed cash flow and increased administrative overhead costs.
A significant portion of denials - about 27% - is due to incorrect patient demographics. Other common issues include missing or outdated coverage information, network status verification failures, prior authorization oversights, and administrative errors in data entry.
All of those add up to a great deal of frustration on the part of the patients and providers when a claim is denied. Imagine eliminating those errors and the time-consuming process of manually obtaining the verification required to avert denial of claims. Now it’s possible, thanks to AI.
Architecture of Insurance Verification AI Agent
The following outlines the complete architecture of an AI agent designed for dental insurance verification. Artificial patient data, retrieved from a PostgreSQL database hosted on AWS Redshift is used in this demonstration. The AI agent will initiate a verification call. For demonstration purposes, we will run the main script (main.py) using Python main.py in a bash terminal or similar environment. In a real-world scenario, the service of TwilioGPT would be used to make the phone call. At the end of the conversation, the AI agent will output the verification responses, formatted as a JSON file, ready to be pushed into PostgreSQL.
The verification process begins with the AI agent making a call to the insurance provider. The agent uses speech recognition technology, specifically OpenAI Whisper, to accurately transcribe spoken information from the provider into text. This transcription is crucial for ensuring that all necessary details are captured accurately.
Next, the text is processed by the Gemini Large Language Model (LLM), which understands the context and generates appropriate responses. This ensures that the conversation flows naturally and all queries are addressed efficiently. The agent then uses natural language generation techniques to craft these responses, which are subsequently converted back into speech using Coqui TTS, providing clear and natural-sounding outputs.
Throughout the conversation, the AI agent extracts and compiles the verification data. This data is formatted into a JSON file, making it ready for insertion back into the PostgreSQL database. The structured data processing ensures that the verification information is complete and correctly formatted, minimizing errors and administrative overhead.
The final step involves updating the database with the verified information. This process not only streamlines the verification workflow but also ensures that the dental practice has access to accurate and up-to-date insurance information, enhancing operational efficiency and patient service quality.
Overcoming the Challenge of Understanding Diverse Accents
To address the challenge of understanding different accents, we create a vector database for semantic search using FAISS. The process begins with initializing the correction system by loading a CSV file containing misheard texts and their corrections. We employ the HuggingFace Instruct Embeddings model to generate embeddings for these texts. These embeddings capture the semantic meaning of each misheard text, making it easier to find similar terms.
The misheard texts are then stored in a FAISS (Facebook AI Similarity Search) index. FAISS is a library developed by Facebook's AI Research team that enables efficient similarity search and clustering of dense vectors. By using FAISS, we can quickly search for the closest matching term when a misheard phrase is detected.
When a query is received, the find similar term function uses the FAISS index to search for similar terms. It compares the query against the stored misheard texts, taking into account the context of the conversation. If a match is found, the system provides the corrected term along with a confidence score. This approach allows us to handle different accents and correct misheard terms effectively, ensuring accurate communication and data entry.
The Benefits and Implementation of an AI Agent
The automated verification process includes real-time eligibility checks, allowing practices to verify patient insurance status instantly. The system can perform 7-day advance verification, ensuring that practices are prepared ahead of patient visits. Network status confirmation and benefit validation are automated, reducing the likelihood of errors and improving the accuracy of information. Additionally, the system tracks prior authorizations, ensuring that all necessary approvals are in place before treatments are administered.
The error prevention system is another significant benefit. Predictive error detection identifies potential issues before they occur, reducing the number of denied claims. The system performs data validation checks to ensure that all information is accurate and complete. Coverage requirement verification and code accuracy confirmation further enhance the reliability of the verification process. Documentation completeness validation ensures that all necessary information is included, reducing the need for follow-up calls and additional paperwork.
In a hypothetical mid-size practice scenario, the AI agent can significantly reduce the financial impact of claim denials. With an average monthly claim volume of 500 and a denial rate of 9%, practices face significant losses. The AI system can reduce the denial rate significantly, translating into substantial cost savings and increased revenue recovery opportunities. By automating the verification process, practices can avoid losses associated with denied claims and improve cash flow.
Operational improvements are also expected with the implementation of the AI agent. Manual tasks, which currently take up 4-6 hours daily for insurance verification to all patients seen in a day, can be minimized, allowing staff to focus on more critical activities. The accuracy of eligibility checks is significantly increased through automated validation, and the system can operate 24/7, providing around-the-clock verification capabilities compared to the current limitation of business hours.
Potential Future Improvements
While the AI agent currently handles accents for some regions effectively, there is a need for more data to improve its accuracy across a broader range of accents. This will involve collecting and incorporating more diverse speech samples into our vector database for FAISS correction. Additionally, we can experiment with various versions of conversations and misheard contexts to fine-tune the agent's responses and error-correction capabilities.
Conclusion
In conclusion, the AI Dental Insurance Verification System addresses critical industry challenges by automating and optimizing the verification process. With potential savings of 14 minutes per transaction through AI automation and the ability to prevent significant amount of claim denials, the system offers significant ROI while improving operational efficiency and patient satisfaction. We have implemented different accent handling to ensure the agent can recognize and interpret various accents accurately. Robust data extraction capabilities ensure that the necessary information is retrieved and processed, regardless of semantic variations. Additionally, the system enables faster real-time processing by normalizing audio data and removing background noise. This combination of advanced AI technology and automated communication capabilities positions this solution as a transformative tool for modern dental practices, enhancing workflows, reducing costs, and providing better service to patients.
Acknowledgements
I would like to express my sincere gratitude to those who made this project possible:
First and foremost, thank you to GoDental.ai for sponsoring this project and providing the opportunity to work on this innovative solution.
Special thanks to my mentor, Cole Ingraham, whose generous guidance, expertise, and dedication were instrumental in shaping this project. Your thoughtful insights and support were invaluable throughout this journey.
I am also deeply grateful to Vivian Zhang for her exceptional leadership and support, which helped make this project a success.
This project represents not just technical achievement, but the power of collaboration and mentorship in driving innovation.
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