Our MVP directly implements the transparency commitments announced today by measuring policy clarity against standardized criteria. The system works by: (1) Users upload medical policy PDFs which get processed to extract text content and stored with unique policy IDs, and (2) Claude AI analyzes the extracted content to generate structured transparency assessments covering readability, clarity of authorization requirements, evidence alignment, and administrative burden—exactly the transparency metrics that health plans committed to improving. The scoring evaluates whether policies provide "clear, easy-to-understand explanations" as pledged by industry leaders, creating measurable baselines for the transparency improvements promised to 257 million Americans.
Current Procedural Terminology (CPT) codes define medical procedures and services, while International Classification of Diseases (ICD) codes categorize diagnoses and conditions. These standardized coding systems form the backbone of medical policy decision-making, determining coverage eligibility and reimbursement rates. Medical policies reference specific code combinations to establish when treatments are considered medically necessary or experimental. The complexity of code relationships requires systematic analysis to identify coverage gaps and inconsistencies across different policy frameworks.
Medical necessity guidelines contain the core logic governing healthcare coverage decisions, traditionally interpreted by clinical reviewers using evidence-based criteria. These guidelines balance clinical efficacy, cost-effectiveness, and appropriate utilization through complex decision trees that consider patient-specific factors. Historical manual application by nurses and physicians introduced variability based on individual interpretation and experience. Machine learning systems can standardize these interpretations while maintaining clinical nuance through structured decision-making frameworks.
National Coverage Determinations (NCDs) and Local Coverage Determinations (LCDs) represent government-developed policies available for public use, while proprietary guidelines from MCG and Interqual operate under commercial licensing restrictions. These commercial entities maintain copyright over interpretations of publicly available medical evidence, creating complex intellectual property considerations for AI training datasets. Plan-specific policies often reference these proprietary sources, requiring careful navigation of licensing requirements for comprehensive analysis.
Traditional medical policy enforcement relies on clinical staff who manually review cases against established criteria, creating workflows that ML systems must either replicate or integrate with existing processes. Administrative staff handle routine approvals while complex cases escalate to medical directors, establishing precedent patterns that inform automated decision-making. Green carding providers represents an early automation approach based on historical performance metrics, demonstrating successful integration between human oversight and algorithmic efficiency.
Healthcare appeals processes involve multiple layers—internal plan reviews, independent medical reviews, state insurance commission appeals, and federal oversight—each with distinct timelines, evidence requirements, and decision-making authorities. Commercial plans, Medicare Advantage, and traditional Medicare maintain separate appeals pathways with different procedural requirements and evidentiary standards. Emergency and expedited appeals compress normal review timelines while maintaining clinical accuracy requirements, creating operational challenges that automated systems could help streamline.
Current regulations create varied disclosure requirements across insurance types, with commercial plans, Medicare, and Medicaid operating under different transparency standards. State regulations mandate different levels of policy disclosure, with enforcement mechanisms that vary significantly across jurisdictions. Federal transparency rules focus primarily on pricing rather than coverage criteria, creating asymmetric information availability for AI training purposes. Consumer access to policy information remains inconsistent despite regulatory requirements for disclosure.
Medical policy information exists primarily in non-searchable PDFs and disparate digital formats that complicate systematic ML analysis. Payers publish policies in formats that satisfy legal disclosure requirements while creating technical barriers to automated processing through inconsistent structure and poor metadata. Web access restrictions and authentication requirements limit comprehensive policy collection for AI training datasets. Version control inconsistencies mean multiple policy versions may exist simultaneously without clear lineage tracking.
The Centers for Medicare & Medicaid Services maintains policy development processes that could serve as templates for standardized formats compatible with ML systems. Current federal oversight focuses on clinical appropriateness rather than technical standardization for automated processing. CMS coverage determination processes already incorporate evidence-based decision-making that translates well to algorithmic implementation. Commercial payers develop independent policy formats that create integration challenges for comprehensive AI analysis across different plan types.