Dominic

Problem Description: 
 
In a healthcare claims negotiation & arbitration process, when Out-of-network providers feel that they have been underpaid, they raise negotiation requests. In negotiation stage, claims adjustment team reviews the additional records shared by provider and based on that, they pay extra or stick to the initial payment made. They also explain why the payment amount was decided. If the provider is not happy after that, they raise an arbitration request. The arbitration team selects external arbitrators (from a wide-range of available federal govt-certified arbitrators). The arbitrators are needed to be paid a huge amount (between $500-$1500) by the arbitration team. Arbitrators' decision on the payment amount often goes in favor of the provider.
 
Goal:
 
The goal is to create an AI model, which will be able to suggest a claim payment amount along with an explanation behind selecting the amount in the negotiation stage itself, so that the process can save arbitration fee costs. Historical data from claims adjustment (non-negotiated claims, negotiated claims and arbitrated claims) will be used to build the model.
 
Recommended AI Solution Building Approach:
 
Fine-tuning an existing LLM will be the best fit.
 
Why Fine-Tuning over other available options?
 
Because, this can help us leverage the advanced features of MED-BERT, which is a contextualized embedding model designed for healthcare applications and can be fine-tuned to meet the requirement of the process
 
Below are few key advantages of Fine-Tuning:
Efficiency: Less data and computational resources requirement, given the complexity and volume of historical claims data. Performance: When fine-tuned for specific tasks, LLMs can achieve high accuracy and adaptability. Domain Adaptability: The model can be allowed to specialize in the domain, leveraging pre-existing knowledge, while adapting to specific negotiation and arbitration contexts. Speed: Faster to deploy than training a new model from the scratch, even with iterative feedback-based improvements. Limitations of other models:
 
Conventional AI-models:
May struggle with the complexity and variability of healthcare claims data. Often lack flexibility and adaptability needed for nuanced decision-making. Lower accuracy and capability of generating detailed explanations. Training a New AI model:
Requires extensive computational resources, large datasets. Requires significantly high time and cost investment. High risk of overfitting and poor performance. Flow and Prompt-based Design:
May not achieve the required level of accuracy and depth in understanding complex claims data.