Scaling Trust in Healthcare AI: How LLM Evals Benchmark Accuracy, Latency, and Cost

Evals turns innovation into accountability!

When I left Microsoft to start Nēdl Labs, one of the first lessons I carried over was simple:

You can’t improve what you don’t measure—and what you measure shapes what you build.

Large Language Models (LLMs) have unlocked extraordinary possibilities. But the excitement often hides a hard truth: without robust evaluation frameworks, LLM outputs remain unreliable, unaccountable, and unfit for production.

LLM Evals are structured evaluations that test not only accuracy, but also latency (speed of response) and cost (efficiency at scale). In healthcare, where decisions affect both patient outcomes and billions of dollars in spending, all three dimensions matter.

At Nēdl Labs, we’ve invested heavily in the eval discipline. Our Policy Intelligence platform benchmarks at over 96% accuracy in policy understanding and summarization, clause recognition and extraction, coding, modifiers, and coverage identification and detection.

At the same time, we optimize for low-latency responses (sub-seconds per page) and predictable cost structures that make enterprise-scale deployments feasible.

1. What Are LLM Evals?

An LLM Eval is a structured test that measures how well a model meets real-world needs and expected output.

For NeldLabs Policy Intelligence, evals mean:

• Clause Extraction Accuracy: Can the system correctly extract all relevant policy clauses (e.g., prior authorization, medical necessity, documentation requirements) without hallucinating or omitting exceptions?
• Code & Modifier Mapping Fidelity: Can it accurately map all CPT, HCPCS, and ICD codes, and associated modifiers into clear categories: covered, partially covered with conditions, investigational/experimental, or not covered — while preserving policy context like age limits, site-of-service restrictions, or prior-authorization rules?
• Coverage Classification Specificity: Can it classify services into covered, partially covered with exceptions (e.g., clinical criteria, volume limits), or excluded, ensuring each classification is traceable back to the original policy text for auditability and regulatory compliance?
• Can it achieve this at a speed and cost-effectiveness that allows for scaling across tens of thousands of policies and claims without significantly impacting budgets or review timelines?

Without evaluation, AI outputs remain anecdotes. With evaluation, they become benchmarks.

2. What Are LLM Evals?

The industry LLM benchmarking practices fall into three buckets:

• Academic Benchmarks (e.g., MMLU, BIG-Bench, TruthfulQA): Good for general intelligence, but not domain-specific.
• Enterprise Benchmarks (task-specific evals, LLM-as-judge loops, human review): Focused on applied accuracy.
• Healthcare Benchmarks (clause fidelity, code accuracy, coverage alignment): Prioritize trust, compliance, and auditability. Emphasize transparency, defensibility, and operational scalability.

Each sector values different dimensions, but all increasingly converge on accuracy, latency, and cost as the core triad.

3. How Nēdl Labs Policy Intelligence Benchmarks at 96%+

Achieving 96%+ accuracy across policy extraction, clause segmentation, CPT/HCPCS mapping, coverage vs. exclusion classification, and code modifiers doesn’t happen by chance. It requires a structured, multi-layered evaluation pipeline designed for the complexity of healthcare data and the operational realities of payers.

Step 1: Task-Specific Evaluation Design

We start by breaking down healthcare policy intelligence into discrete, measurable tasks rather than relying on generic benchmarks:

• Policy Clause Extraction: Identifying clauses related to prior authorization, coverage limits, or service exclusions.
• Code & Modifier Mapping: Extracting CPT, HCPCS, and ICD codes along with coverage modifiers.
• Coverage Classification: Labeling services as covered, not covered, or experimental/investigational.
• Comparative Benchmarking: Measuring coverage similarities across payers, Medicare, and commercial policies.

Each task is assigned accuracy, latency, and cost thresholds so we’re not just chasing precision but also speed and scalability.

Step 2: Ground Truth + Hybrid Evaluation

Our evaluation process blends domain expertise with scalable AI techniques to ensure statistical rigor and operational relevance:

• Domain Expert Annotations for Gold-Standard Ground Truth: Healthcare policy experts manually annotate thousands of policy pages, creating gold-standard labels for clauses, codes, and classifications. Each label includes clear definitions, edge-case notes, and decision rules to ensure consistency across annotators.
• Measurement Sets & Blind Evaluation Sets: We use three tiers of datasets — a ground-truth set for broad benchmarking, a targeted set for fine-grained, task-specific evaluation, and blind test sets held out entirely to measure on unseen and complex policies.
• Human-in-the-Loop Audits Random samples undergo multi-annotator reviews for inter-rater reliability. Any disagreements trigger error analysis sessions to refine both labeling standards and model logic.
• LLM-as-Judge for Scalable Comparisons Secondary LLMs score outputs at scale for precision, recall, faithfulness, and coverage similarity against gold-standard labels. Human reviewers periodically audit these scores to maintain fairness and detect bias.

This hybrid approach balances accuracy, consistency, and cost-efficiency—essential when processing millions of policy pages across payers.

Step 3: Dashboarding & Benchmarking

All metrics flow into a real-time internal benchmarking dashboard designed for both engineering teams and payer executives to track performance, uncover gaps, and validate accuracy. We monitor four key dimensions:

• Codes Extracted: Our policy intelligence platform accurately processes 12,000+ CPT codes with coverage guidelines, setting the baseline for internal model accuracy tracking across multiple payers and policy types.
• Coverage Alignment Scores: Benchmarked internally at ~20 top national and regional payers, these coverage benchmarking of policies highlights policy variations and helps prioritize future model refinements.
• Throughput Benchmarks: Optimized for high-volume post-pay reviews, the system processes thousands of pages per hour to deliver actionable insights within hours rather than days, meeting operational audit timelines.
• Cost Efficiency Metrics: By leveraging caching, hybrid rules+LLM architectures, and batch processing, the platform maintains 20–40% lower processing costs per 1000 PDF and Web pages compared to legacy internal workflows.

The dashboard also provides drill-down analytics, enabling teams to trace results back to individual clauses or policies — critical for audit readiness, compliance reviews, and internal QA loops.

Step 4: Continuous Improvement Loop

Evaluation doesn’t end with reporting. We’ve operationalized it into a continuous improvement cycle:

• Weekly Eval Runs: New payers, policies, claims data, and payer-specific edge case feed into weekly evaluation pipelines.
• Error Category Analysis: Misclassified clauses or codes trigger targeted retraining rather than full model retraining, saving cost and time - [more on this in a future article].
• Regression Monitoring: Any accuracy, latency, or cost regressions automatically flag engineering sprints for mitigation.
• Policy Drift Detection: Coverage rules change frequently—our pipelines flag policy drift so models stay aligned with the latest CMS and payer rules.

This ensures our 96%+ benchmark isn’t a one-time milestone but a living performance standard.

Putting It All Together

By combining task-specific evaluation design, expert-annotated ground truth, blind-set validation, real-time benchmarking dashboards, and a continuous improvement loop, Nēdl Labs ensures its Policy Intelligence platform delivers on the promises that matter most:

• Accuracy: 96%+ clause and code extraction fidelity.
• Scalability: Optimized for millions of pages across post-pay workflows.
• Cost-Efficiency: Predictable, competitive processing costs per claim or policy page.
• Auditability: Every extraction is fully traceable back to the source text for compliance and regulatory defense.

In an industry defined by policy complexity, affordability pressures, and strict regulatory oversight, this integrated approach turns AI from a black box into a transparent, trustworthy, and scalable solution for healthcare payers.

The Road Ahead

In healthcare, accuracy alone isn’t enough. AI must deliver auditability, compliance, trust, and scalability — with costs and speed that work at enterprise scale.

The next wave of LLM Evals will go further, measuring fairness, robustness, explainability, and cost-effectiveness. Healthcare AI will ultimately be judged not just by what it achieves, but by how reliably and transparently it delivers value across millions of policies and claims.