Scale Coding Capacity Without Sacrificing Control
Healthcare organizations face coder shortages, rising coding costs, backlogs, denials, and charge lag. Traditional Computer-Assisted Coding can improve productivity but still requires significant human review.
The Fulcrum Solution
Fulcrum Autonomous Medical Coding uses context-aware AI to analyze clinical documentation and generate validated ICD-10-CM codes.
The solution:
- Extracts clinical conditions, findings, and treatments
- Identifies present, negated, historical, and hypothetical conditions
- Understands clinical relationships such as “due to” and “secondary to”
- Maps clinical concepts to ICD-10-CM candidates
- Validates codes and maintains an explainable audit trail
- Routes low-confidence or complex cases to human coders
| How It Works Clinical Note -> Context Analysis -> Code Generation & Validation -> Autonomous Coding or Human Review High-confidence routine encounters can be automated, allowing experienced coders to focus on complex cases, exceptions, and compliance. |
Business Value
Reduce Coding Costs
Automate routine, high-confidence encounters and reduce dependency on outsourced coding.
Accelerate Revenue
Reduce coding backlogs, DNFB, and charge lag.
Optimize Coding Resources
Focus scarce coding expertise on complex and higher-value cases.
Improve Auditability
Maintain supporting clinical evidence and derivation for automated coding decisions.
Potential Impact
For a modeled health system processing 250,000 encounters annually, Fulcrum estimates:
- ~65% autonomous coding
- $300K-$380K potential annual direct coding savings
- $700K-$1.3M indicative total annual value across coding costs, denial rework, DNFB/charge lag, and coder redeployment
The Outcome
| AI handles routine coding. Coders focus on complexity. Fulcrum enables a more scalable medical coding model while keeping every automated decision traceable and auditable. |