Virginia · Health IT · Federal Programs · Medicare & Commercial

    Every Clinical Finding Traced to Its Source — Built for Federal Programs

    CloudAnalytics reads unstructured medical documentation and extracts structured, source-cited clinical evidence — mapped to specific criteria and traceable to the exact paragraph in the original record. Purpose-built for program integrity, prior authorization, risk adjustment, and quality measurement.

    PHI de-identification occurs before any language model inference. All outputs are advisory with a full audit trail. Virginia-based · SAM.gov Active · Small Disadvantaged Business (SDB).

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    Why CloudAnalytics

    Purpose-built for healthcare review workflows — not generic NLP applied to health data

    Most clinical NLP platforms extract data. CloudAnalytics extracts evidence — source-cited, criteria-mapped, and structured for the specific review workflow a federal or commercial reviewer is actually running. Every output is traceable to its origin in the source document.

    Generic clinical NLP

    • ⊘Extracts data — no source citations
    • ⊘No criteria mapping (LCD/NCD, HEDIS, RADV)
    • ⊘Black-box output, not audit-ready
    • ⊘Built for general use, not review workflows

    CloudAnalytics

    • Paragraph-level source citations on every finding
    • Criteria-mapped: LCD/NCD, HEDIS, RADV, PA rules
    • PHI de-identified before any LLM inference
    • Advisory outputs with full immutable audit trail

    The Problem

    Clinical records are unstructured.
    Review decisions are not.

    Federal reviewers, payers, and provider organizations rely on clinical documentation to make coverage, payment, and quality determinations. That documentation arrives as free-text narratives, scanned PDFs, and dictated notes.

    Manual extraction is slow, inconsistent, and does not scale. CloudAnalytics automates the evidence extraction step — producing structured, source-cited clinical data mapped to the specific criteria reviewers need.

    Clinical judgment stays human. Evidence extraction becomes automated.

    Unstructured medical documentation
    Progress notes · Pathology reports · Certifications · SOAP notes
    CloudAnalytics Platform
    Automated extraction · Source citations · Criteria mapping
    Structured clinical evidence
    Audit-ready · Deterministic · Human-reviewed output
    3×

    Pilot Result

    3×
    Reviewers processed three times their baseline weekly document volume — with every finding traceable to an exact paragraph in the source record.

    Structured pilot review · Hospice certifications, DMEPOS records, and encounter notes · Source citations validated against original documents by clinical reviewers prior to sign-off.

    Before

    • ⊘Hours per document, per reviewer
    • ⊘No source citations on findings
    • ⊘Reviewer re-reads the entire record
    • ⊘No structured audit trail

    After — CloudAnalytics

    • Under 2 minutes per document
    • Every finding source-cited to paragraph
    • Reviewer confirms citation, not the full record
    • Immutable audit trail on every extraction

    Capabilities

    Production-ready across the care and payment lifecycle

    Program Integrity & Medical Review

    Automated clinical evidence extraction for medical necessity reviews, post-payment audits, and fraud investigations. Every finding is source-cited and audit-ready.

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    Risk Adjustment Validation

    Read encounter documentation submitted to support diagnosis codes. Determine whether clinical records reflect active condition management or historical mentions.

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    Prior Authorization & Clean Claims

    Extract clinical criteria from oncology notes, pathology reports, and specialty records — flagging missing documentation before submission.

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    Quality Measurement & HEDIS

    Evidence mapped to HEDIS measure criteria with lookback window validation and source citations for audit compliance and STAR Rating improvement.

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    From document to determination

    4-step pipeline · under 2 minutes per document · full audit trail on every extraction

    01

    Ingest

    PDFs, scanned documents, HL7, C-CDA, and faxed clinical notes via FHIR API or direct upload.

    02

    Extract

    Diagnoses, lab values, medications, procedures, and functional status — each with confidence score and source citation.

    03

    Validate

    Evidence mapped to clinical criteria: coverage requirements, risk adjustment standards, hospice eligibility, or PA rules.

    04

    Review

    Structured evidence with source citations linked to the original document. All outputs advisory — clinical judgment stays human.

    All processing on HIPAA-compliant AWS infrastructure. Automated PHI de-identification occurs before language model inference. Full audit trail on every extraction.

    What a reviewer actually sees

    Every extracted finding links to its exact source paragraph. Reviewers confirm the citation — they do not re-read the record.

    See full platform documentation
    Clinical Evidence FindingAdvisory · Reviewer sign-off required
    ⊘

    NOT SUPPORTED BY DOCUMENTATION

    Clinical record contradicts the billed condition. No supplemental oxygen in use at time of certification.

    Source Citation

    Progress Note · Page 3 · Paragraph 2

    "O₂ saturation 98% on room air. No supplemental oxygen in use at time of visit."

    LCD Criteria

    L33393 — Section 4.b.ii

    Determination

    Not Met

    Confidence

    0.96

    Ready to see a live extraction on your document types?

    No sales pitch — a direct demonstration of clinical evidence extraction on your records.

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