How GenAI techniques can transform policy landscape analysis and monitoring

As an AI enthusiast, I recently had the privilege of advising on an ambitious project – Market4Health, a platform designed to explore the boundaries of GenAI techniques with extensive room for innovation. In my advisory role, I guided the team as they tackled complex technical challenges to create a platform that redefines how market access specialists engage with the multifaceted U.S. healthcare policy landscape. By harnessing advanced AI solutions, Market4Health simplifies the intricate tasks of policy access, interpretation, and interaction, creating a highly adaptable tool that transcends its original scope.

Building a Scalable Policy Search Engine with International Code Tagging

One of the primary technical challenges was to create a search engine capable of indexing over 100,000 U.S. healthcare policies, maintaining a healthy connection with the source to allow for frequent updates/amendments,  and tagging them with international codes like ICD-11. ICD coding remains a focal point in healthcare, facilitating standardization across diverse data sources, as discussed in recent literature (e.g. https://univagora.ro/jour/index.php/ijccc/article/view/6251, https://aclanthology.org/2022.clinicalnlp-1.2/  and previous gen models based on embeddings: https://arxiv.org/abs/2211.02519  ). However, there’s no universal solution, much less an accurate and validated open-source one, given that ICD tagging effectiveness depends heavily on document style and structural nuances. The Market4Health team rose to this challenge, achieving an impressive F1 score of 94% for ICD term labeling accuracy on a test set of 1,000 documents expertly annotated by physicians. This milestone reflects a finely-tuned balance of precision and recall, overcoming the inherent variability in document structures and terminology.

Integrating Multi-Level AI for In-Depth Document Analysis

The team tackled the complex task of combining various AI layers to achieve nuanced policy interpretations:

• Named Entity Recognition (NER): Identifying key medical terms across extensive documents presented a formidable challenge in ensuring consistency and accuracy. The team carefully selected and optimized NER models, enabling efficient and accurate retrieval of medical terms across the platform’s substantial corpus.
• Context-Derived Sentiment for Policy Stance: Integrating context-based sentiment analysis allowed the team to assess whether certain terms signaled inclusion or exclusion within coverage policies. This addition was pivotal for providing users with clear insights into policy coverage—a task that demanded careful calibration of models to detect subtle nuances in policy language.
• Semantic Embedding for Terminology Mapping: Ensuring consistency in terminology mapping, the team developed semantic embeddings that align extracted terms with standard healthcare terminologies. By leveraging open-access corpora, they achieved robust mappings that associate terms with precise descriptions, addressing the significant challenge of maintaining terminological coherence across a vast and evolving dataset.
• System Learning Through Reinforcement: The platform incorporates reinforcement learning to facilitate continuous system improvement, driven by human input. An interface for human verification enables experts to review and validate term mappings, feeding insights back into the system to inform ongoing model refinement. This feedback loop is essential for long-term accuracy and adaptability.

Implementing RAG-Based Interaction for Intuitive Query Responses

To further enhance user engagement, the team introduced a retrieval-augmented generation (RAG)-based interaction system, allowing users to pose complex, context-specific queries. Building this required a comprehensive document parameter structure—including embeddings, therapeutic classifications, dates, regions, and other metadata—to deliver intelligent, nuanced responses. This RAG-based setup markedly upgraded user experience, making the platform intuitive and conversational.

Ultimately, Market4Health serves as a compelling use case for the application of modern AI in healthcare policy analysis. The platform’s sophisticated design and technical accomplishments showcase how AI can bring clarity and efficiency to complex datasets — a scalable framework that could be easily adapted for other document types, regions, and regulatory frameworks. This project underscores the transformative potential of AI to streamline and enhance policy interpretation across a range of industries, setting a new standard for impactful solutions in regulatory and document-intensive fields.