Solutions

We automate clinical evidence extraction for medical necessity reviews, post-payment audits, and fraud investigations. Our platform reads encounter documentation, hospice certifications, and DMEPOS records — identifying incongruence between billed services and clinical documentation.

For hospice oversight, the system cross-references face-to-face certification narratives against patient clinical records, flagging discrepancies such as a certification claiming oxygen dependence when the most recent clinical note documents normal oxygen saturation on room air and independent daily activity.

For encounter-level review, the platform reads submitted documentation and determines whether the clinical record supports the billed service — distinguishing between active clinical management and incidental historical mentions.

Every finding is source-cited with the specific document, section, and paragraph linked to each determination. All outputs are audit-ready and designed for integration into existing medical review workflows.

We read encounter documentation submitted to support diagnosis codes and determine whether the clinical record reflects active management of the condition — medication adjustments, lab orders, symptom assessment — or merely a historical problem list mention without clinical action.

Our platform structures evidence for chart review and HCC validation workflows, providing reviewers with a clear determination of whether each encounter supports the submitted risk adjustment code. The specific clinical evidence and source document are linked to every finding.

The system processes encounter notes across multiple specialties and identifies documentation patterns that indicate insufficient support for the submitted diagnosis, such as a condition appearing only in the problem list without corresponding assessment, treatment, or monitoring documentation in the encounter note.

We empower provider organizations to submit clean, compliant prior authorization requests and claims to Medicare, Medicaid, and commercial payers.

Our platform extracts clinical criteria from oncology progress notes, pathology reports, and specialty records — mapping extracted biomarker results, staging, performance status, and prior therapy history against payer-specific drug criteria.

The system flags missing documentation and clinical mismatches before submission. When a pathology report shows a biomarker status that contraindicates the requested therapy, the system catches it before the PA is submitted — preventing a denial cycle that typically delays treatment by 5 to 7 days.

For oncology, the platform performs multi-document clinical reasoning — cross-referencing molecular pathology results with the treating oncologist's progress note to verify that biomarker status, staging, performance status, and prior therapy history align with the specific payer criteria for the requested drug.

We extract clinical evidence from unstructured documents to support HEDIS measure closure, STAR Ratings improvement, and eCQM reporting.

Our platform identifies care gaps buried in clinical narratives that structured claims data alone would miss — such as a completed screening documented only in a progress note, a lab result filed in an unstructured PDF, or a blood pressure reading recorded in a clinical narrative without a corresponding structured observation.

Evidence is mapped to specific measure criteria with lookback window validation and source citations for audit compliance. The platform supports automated numerator and denominator identification and integrates with CQL-based measure evaluation engines for digital quality measurement workflows.