Automated Financial Contract Risk Extraction in Healthcare

Extraction uses healthcare-trained AI models to automatically parse payer contracts and identify financial obligations, payment terms, prior authorization triggers, and compliance requirements - eliminating manual line-by-line review, with a confidence score and source citation on every extraction for human verification. The system integrates directly with Epic, Cerner, and athenahealth to flag contract-to-claims mismatches in real time and routes high-risk clauses to your revenue cycle team for human verification. This approach combines AI speed with human oversight, ensuring that complex healthcare payment models (bundled payments, readmission penalties, network exclusions) are captured accurately and fed into your claims and prior authorization workflows.

Yes. Contracts are processed in isolated, encrypted environments with zero retention of contract text after extraction completion - only structured risk data is stored in your secure dashboard. All API connections to Epic, Cerner, Veeva Vault, and email systems use OAuth 2.0 authentication with field-level encryption. Audit logs of every extraction, human review, and system update are maintained for CMS Conditions of Participation and OIG compliance documentation, with role-based access controls ensuring only authorized finance and compliance staff access sensitive contract details.

We work the C.O.R.E. Method, with a working system live inside the first 100 days. Weeks 1-3 audit the work: system architecture design and Epic/Cerner API integration setup. Weeks 4-10 build: contract ingestion, model fine-tuning on your specific payer portfolio, UAT with your revenue cycle team, and staff training. Weeks 11-14 deploy: go-live, workflow integration, and continuous learning. A rollout like this is scoped to show measurable results within 60 days of go-live - denial rates moving against the baseline set during scoping, and prior authorization processing accelerating as the system learns your contract patterns and flags exceptions automatically.

Three things change. Every payer contract gets read line by line instead of skimmed under deadline, so the readmission penalties, bundled payment thresholds, and network exclusions buried on page 40 get flagged before they cost you. Contract terms reach the claims workflow before submission, so denials get prevented instead of fought one at a time. And the hours your revenue cycle managers spend parsing 50-page agreements move to the work that actually needs their judgment - denial management and payer negotiation. Every extraction carries a confidence score and a source citation, and your team verifies the flags rather than doing the reading.

The amendment gets ingested and parsed the same way the original contract was - and that is where the system earns its keep. Amended payment terms, new prior authorization rules, and changed exclusion criteria are flagged against the prior version, the compliance rules in your revenue cycle system update, and clinical teams get alerted to new documentation obligations. Today, that amendment sits in someone's inbox until a denied claim reveals it. The risk registry stays current because it is fed by the document flow, not by whoever remembered to update the spreadsheet.

It attacks the two numbers revenue cycle leaders watch: denials and days in A/R. Denials fall because contract-to-claims mismatches get flagged before submission instead of discovered in the rejection queue. Days in A/R compress because payment terms stop hiding in 50-page documents until a claim rejects - they sit in a live risk registry wired into your Epic or Cerner claims workflow. Prior authorization stops waiting on a human to find the rule in the contract. Your revenue cycle managers still own every interpretation call; they just make it from an AI-generated summary with source citations instead of the raw document.