DenialIntel

How It Works

From transaction data to evidence-backed action

DenialIntel follows eight steps from healthcare transaction data to prioritized, evidence-backed work, with optional AI explanation and human review at the end. This page describes the process in business terms. It does not publish implementation internals.

The process at a glance

The first eight steps are established by deterministic processing, business rules and statistical analysis. AI explanation is an optional layer on top of those results. People review the evidence and decide what to do.

Enrollment, eligibility and authorization records are used as evidence where they are available. Not every organization will have every transaction type.

  1. Ingest
  2. Validate
  3. Match
  4. Classify
  5. Investigate
  6. Prioritize
  7. Support Recovery
  8. Identify Prevention Opportunities
  9. Optional AI-supported explanation
  10. Human review and action

  1. Ingest

    Healthcare transaction data is brought into the workflow: 837 claims and 835 remittances, and, where available, 834 enrollment, 270/271 eligibility and 278 authorization data.

    Produces
    Transaction records ready for validation
    In the public demo
    Synthetic data is pre-loaded. Upload and import are deliberately unavailable.

  2. Validate

    Transaction and data validation checks identify missing, incomplete or inconsistent information, and records are normalized into a consistent structure for analysis. These checks support analysis quality; they are not a certification of X12 conformance.

    Produces
    Normalized data and visibility of data issues
    Why it matters
    Analysis is only as reliable as the data beneath it

  3. Match

    Claims are matched to their remittances so each payment outcome is linked to what was billed.

    Produces
    Claim-to-remittance links
    Why it matters
    A denial can be read alongside the claim that produced it

  4. Classify

    Denials are identified from remittance outcomes and classified by reason, payer, department and other dimensions.

    Produces
    A classified denial population
    Used for
    Financial impact, rates, trends and concentrations

  5. Investigate

    For a denial, related eligibility and authorization records are brought alongside the claim and remittance, where available, so analysts can see what the transaction record supports.

    Produces
    Cross-transaction evidence for a denial
    Used by
    Denial analysts and billing specialists

  6. Prioritize

    Financial impact, ageing, priority analysis and statistical and ML pattern analysis show which denials need attention first. ML outputs are supporting signals, not authoritative decisions.

    Produces
    Priority worklists and management views
    Used by
    Denial teams and revenue-cycle management

  7. Support Recovery

    Prioritized worklists, evidence and exports support the appeal, correction and follow-up work carried out by your team. DenialIntel does not guarantee recovery of denied revenue.

    Produces
    Evidence-backed worklists, reports and Excel exports
    Used by
    Denial and billing teams

  8. Identify Prevention Opportunities

    Root-cause, trend and pattern analysis highlight likely contributing causes and recurring issues. These are candidates for investigation and human review, not final determinations.

    Produces
    Prevention opportunities for operational review
    Used by
    Operational teams and leadership
Core

What establishes the facts

Deterministic processing, business rules and statistical analysis perform the validation, matching, classification, calculations and prioritization described above. The same data produces the same results.

Optional

Where AI fits

AI Insight and AI Deep Dive explain and summarize findings that have already been established. AI does not perform the core calculations, make clinical decisions, decide whether a claim must be paid or replace professional and payer-policy review.

Human review and action: people review the evidence and decide what to do.

Follow the process in the synthetic demo.

The public demonstration requires no login and contains synthetic data only. AI output in the demo is representative and pre-generated.

Launch Synthetic Demo Explore the Product

Synthetic demonstration data only. Do not enter PHI or other sensitive personal information.