The new AI approach to eliminating contract-based errors

Contracts form the foundation for the relationship between health plans and providers, but the complexity of these contracts can cause overpayments and increased administrative costs. It is estimated that about 20% of medical claims errors can be attributed to manual processes, and more than 25% of payment administrative issues can be traced to contract-related errors.¹ Contracts and the manual review of those contracts represent a critical vulnerability in the claim lifecycle.  

But there is a more efficient way. The next phase is contract digitization, powered by AI. This innovative use of AI helps health plans quickly reference provider rates, carve-outs and other reimbursement rules in contracts to help minimize errors in pricing claims. 

To learn more about contract digitization solutions, visit our Contract Intelligence page. 

Due to the sheer volume, complexity and length of contracts, finding needed information can be like looking for a needle in a haystack. Contracts also change frequently and can be housed in different systems and a variety of formats, including PDFs, Microsoft Word documents and images. One file may even contain multiple contracts. Ultimately, data quality is inconsistent. 

As a result, manual contract review is a long and labor-intensive process. It’s not hard to see why health plans struggle to catch contract-based errors, leading to inaccurate payments.

Contract digitization transforms static payer-provider contracts into dynamic, actionable data.  All relevant health plan documents are converted into one format and consolidated into a centralized, searchable repository. There are two main benefits of digitization:  

1. Disparate files are transformed into digital contract data to help streamline required operational workflows. 
2. The data in those files becomes machine-readable. 

Once the documents are in a readable digitized format, large language models (LLMs) read and interpret the digital contract contents. Advanced AI-driven clause extraction identifies, extracts and organizes key terms, obligations and financial details. This enables robust AI-powered contract searches that go beyond simple keyword searches.  

The digitized contract data can then be combined with claim information to fuel AI-powered decision intelligence that integrates directly with data mining systems. This results in the rapid application of contract-based claim edits and scalable detection of errors and overpayments.  

Contract digitization solutions can help health plans improve efficiency, medical cost savings and productivity in a variety of ways. The main benefits include:

• Payment accuracy — Improve contract configuration to enforce accurate claims pricing rules, strengthening audit precision with the goal of reducing inaccurate payments and administrative waste from incorrect claims processing.
• Revenue leakage prevention — Accurate pricing validation and dynamic edits can minimize revenue leakage and reduce friction between health plans and providers. Health plans can potentially experience an improvement in medical cost savings of up to 20%.²
• Audit optimization — Enable efficient identification of improper payments, reduce false positives and accelerate both error prevention and recovery efforts.
• Transform contract management capabilities — Contract digitization acts as a service layer on top of contract management systems, digitizing and structuring documents, tracking version lineage and supporting compliance monitoring.

Ultimately, digitization can lead to fewer administrative errors and the potential for increased medical cost savings by accurately pricing claims during initial processing.

Healthcare contracts are long and complicated, adding complexity to the payment integrity process. It’s time to simplify the way contracts are leveraged and referenced. Put the power of AI to work to help improve efficiency, reduce errors and create a more streamlined claims lifecycle.

At Optum, we have robust guardrails in place and conduct third-party reviews to help ensure our models are designed to be ethical, transparent, explainable and to do no harm, aligning with the healthcare industry’s robust privacy and security standards.