Skip to content
Black Gradient Minimalist Corporate Business Personal Profile New LinkedIn Banner (1)

Contracts as Code: How We’re Reimagining Healthcare Agreements with AI

A new way to think about AI contract management by turning complex healthcare agreements into executable intelligence. 

Healthcare provider contracts—especially in value-based care (VBC)—are among the most complex documents in the industry. A single template can contain more than 40 conditional rules and subtle wording differences, which can alter financial exposure by millions of dollars over the contract lifespan.  

Yet, existing legacy Contract Lifecycle Management (CLM) systems only store, route, and archive documents. They were never designed to understand the nuances—the rules hidden in text, comments, or redlines—what they mean, and how the contract behaves. 

This results in long manual review cycles that drag on for weeks, missed variations, inconsistent interpretations, and limited visibility into compliance, financial, and operational risks. 

As organizations adopt AI contract management software, the opportunity is not just to automate existing workflows but to redesign contract management altogether. 

The HiLabs Approach: Contracts That Behave Like Code 

AI contract management workflow showing five steps: upload contract, AI-driven rule extraction, contract structuring, AI rule execution, and Jinja2 template encoding. 
At HiLabs, we’re approaching complex healthcare agreements by treating them not as documents but as executable programs. We’ve built an AI-powered contract template generator that transforms contracts into logic-aware, reusable templates. 

Using a pipeline of agentic large language models (LLMs) and a custom execution engine, our system: 

  1. extracts rules from comments, highlights, and subtle drafting instructions, 
  2. reconstructs the contract hierarchy (articles, sections, and cross-references), 
  3. binds each rule to the exact text spans for unambiguous linkage, 
  4. executes conditions to resolve overlaps and normalize inputs, and 
  5. generates Jinja2 templates in which every clause “knows” when it should exist in a contract and what each clause impacts. 

Think of it as upgrading provider contracts from static Word files into programs that can be executed, compared, and simulated. 

Why “Contracts as Code" Matters 

By reframing contracts as executable templates, we unlock new capabilities that traditional tools don’t support. Instead of treating agreements as static text, teams get to work with smart contracts that understand their own rules, conditions, and dependencies. This enables: 

  1. Metadata-Driven Drafting
    Teams can generate tailored agreements instantly by entering parameters such as state, product line, or risk configurations. 

  2. One-Click Draft Building
    A complete draft can be created in seconds using the template builder. 

  3. Clause-Level Tracking
    Teams can monitor which clauses change between the template and the signed agreement. 

  4. Precise Comparison
    Teams can detect subtle variations that impact obligations with more ease. 

  5. What-If Simulations 
    Organizations can model negotiation scenarios and assess potential compliance or financial impact prior to finalizing agreements. 

Together, these capabilities can make it less laborious for teams to understand agreements, terms, and their downstream implications created in real-world operational environments. 

Side-by-side contract clause comparison showing static text on the left and an AI-generated Jinja2 template with conditional logic and variable substitution on the right.

Results So Far 

In internal pilots using dummy provider contracts, our system has shown promising results: 

  • 95% accuracy in automated rule execution, ensuring reliability and trust 
  • 60% faster review cycles, reducing weeks of manual work to days 
  • More than 90% cost efficiency, bringing review costs to less than $1 per contract 
  • 1.5 minutes average processing time per contract, even for agreements containing dozens of conditional rules. 

These results demonstrate that contract intelligence can be scaled effectively, helping save time and cost by improving consistency, enhancing visibility, and supporting clearer negotiation outcomes. 

The Path Ahead for Contracts as Code 

Our work in redefining how contract management systems function altogether has only just begun. Building on our current results, our future roadmap includes: 

Self-Evolving Templates  

Templates that update as new negotiations and real-world usage feed back into the system. 

Optimization Engine  

An AI-guided search process that helps find the best clause combinations that balance compliance, risk, and acceptance. 

Beyond Healthcare  

 Given the success of Contracts as Code in healthcare, the HiLabs team plans to test this approach in other highly regulated sectors, like financial services, where contracts carry equally dense and complex logic. 

The Future of Contract Intelligence 

We started this work with a simple question: why do we still manage contracts like static text when so much of what matters is hidden in underlying logic? 

Our initial pilots have shown that contracts can, in fact, be treated like executable programs. By pulling out the rules buried inside agreements and making them executable, we’ve reduced review time, lowered cost, and created a system that teams can trust.  

 This shift is not only about efficiency. It’s also about clarity. When logic is made explicit, everyone involved in a negotiation can understand which clauses apply, why they exist, and what changes may mean downstream. While the approach continues to mature, our path ahead is clear: contracts that are faster to generate, easier to compare, and smarter to negotiate. 

At HiLabs, we are dedicated to solving the most complex challenges in healthcare data. To discover a clearer, faster way to understand and manage contract logic, book a demo today.

Frequently Asked Questions

Why are healthcare contracts so complex?

Healthcare contracts—especially payer–provider agreements—contain dozens of conditional rules, cross-references, rate structures, amendments, and variations across states or product lines. Small wording differences can change financial obligations significantly. Because the logic is often embedded in comments, annotations, or references to external documents, these agreements are difficult to interpret when handled as static text. 

How is AI used in contract lifecycle management (CLM)?

AI in CLM typically helps extract clauses, classify content, and support search or routing. It can surface relevant sections more efficiently and enable natural language queries on contract content. However, most CLM tools treat contracts as documents rather than capturing the underlying logic. The emerging direction in AI-driven CLM focuses on identifying conditions, dependencies, and structure so contracts can be interpreted more consistently. 

How does AI contract management software turn contract text into executable logic?

AI can identify rules embedded in comments, annotations, and conditional language, then link those rules back to the exact text spans where they originate. Once the hierarchy and dependencies are reconstructed, these elements can be represented in a structured template. This allows the logic to be executed, compared, and used to generate tailored drafts without altering the original meaning of the agreement. 

How do agentic LLMs improve contract interpretation and rule extraction?

Agentic LLMs break the interpretation task into smaller steps, such as reading drafting cues, reconstructing hierarchy, validating extracted logic, and resolving overlaps. This stepwise reasoning helps the model to interpret complex rules more consistently. When combined with traceability back to the contract text, it supports clearer and more reliable extraction of clauses and conditions. 

What are the benefits of using AI-based contract management for payer and provider agreements?

AI-based approaches can help teams generate drafts more efficiently, identify clause-level changes between versions, and compare agreements with greater precision. They also make it easier to model negotiation scenarios by making the logic within contracts explicit. For payer and provider teams dealing with logic-heavy agreements, this reduces manual review effort and helps clarify downstream operational or financial implications. 
, ,