CDMS | Research Phase Completed and Beginning of Technical Definition

(English Version)

The CDMS – Claim Denials Management Solution project was born from the belief that innovation only creates real impact when applied to concrete problems with practical relevance. In this case, the goal is to rethink how healthcare organizations handle claim denials, billing disputes, and preventable revenue leakage. 

In this first stage, the focus was primarily on in-depth problem analysis. More than just validating an idea, we set out to answer a specific question: is there both space and urgency for an intelligent, Generative AI-based solution to manage claim denials? The answer was clear. 

Digital Maturity Varies, but the Problem is Universal 

The market analysis, conducted by co-promoter EFFY, focused on two key areas: market trends and technology monitoring and functional requirements gathering and user validation. 

This analysis revealed a consistent pattern across different regions: the financial impact of claim denials is substantial. Despite advances in HealthTech and digital health, the misalignment between clinical data and billing still leads to underpayments, rejected reimbursements, payer disputes, soaring operational costs, and billions in lost revenue annually. 

In Portugal, the United States, and the Gulf Cooperation Council (GCC) countries, we identified a common problem: current denial management systems are reactive, fragmented, and lack transparency. This underscores the need for proactive, automated, and data-driven solutions. 

Balancing Innovation and Compliance: Generative AI with Privacy-First Design 

The integration of Generative AI, particularly Large Language Models (LLMs), into healthcare has transformative potential to enhance patient care and operational efficiency. However, it must be balanced against stringent privacy regulations such as HIPAA and GDPR, which mandate the protection of sensitive health information. 

To address this challenge, project co-promoter RANDY LABS led the evaluation of best practices for developing and operating AI agents in clinical contexts. 

The approach spans from design to implementation of agents specialized in document triage, inconsistency detection, contextual analysis and decision support. 

To ensure compliance with the highest legal and ethical standards - including HIPAA - the project incorporates techniques such as de-identification and anonymization, synthetic data generation, differential privacy, federated learning, homomorphic encryption and secure multi-party computation, use of LLMs in on-premises or hybrid environments for greater control and auditability. 

This foundation enables the responsible, secure, and effective adoption of LLM-based agents, positioning AI as a trusted partner in the clinical ecosystem. 

However, these approaches come with trade-offs. They often require additional implementation work and have measurable impacts on latency, quality, computational cost, and energy consumption. That’s why the research conducted by co-promoter INESC TEC, focused on optimizing the cost-performance ratio of Generative AI, is critical to the project’s success. 

Compliance is not a barrier to innovation, but a catalyst for developing AI systems that are secure, robust, reliable, and efficient. Achieving this balance is essential to unlock the full potential of AI agents in healthcare while maintaining patient trust and regulatory alignment. 

User Experience and Operational Relevance 

This initial phase also involved active participation from partner CUF, who played a key role in identifying and analysing user stories aligned with functional prototypes. This was done through direct engagement with the Account Management teams. 

This collaboration made it possible to define not only data management and security requirements but also prioritize features with immediate operational impact, including document validation, authorization management, and clinical episode reconciliation. The result: a real-world, practical vision of how this solution can enhance operational efficiency. 

Next Steps: Turning Vision into a Functional Product 

With the technical and functional groundwork in place, the project now moves into the next phase: designing and testing the first CDMS prototypes. 

The focus will be on building a robust technology framework to: 

• Deploy AI agents for contextual analysis and decision-making 

• Orchestrate intelligent processes for data transformation, verification, and enrichment 

• Apply advanced summarization and explainability techniques tailored to end users 

• Ensure a scalable, secure, and cloud-native architecture 

System Architecture Definition: 

• Describe system components, focusing on specialized AI agents 

• Define technical requirements based on modularity and interoperability principles 

• Structure integration points with external systems and automated deployment strategies 

This journey is both technological and strategic. Managing claim denials is an opportunity to make the revenue cycle in healthcare more transparent, efficient, and sustainable. 

For more information about the project reach out to info@effybusiness.com