Pre-Submission Predictive Scrubbing
Analyzes diagnostic ICD-10 and CPT coding combinations alongside payer-specific policy guidelines prior to transmission, flagging claims likely to trigger denial with 96.8% predictive accuracy.
[ HEALTHCARE AI ]
DenyFix.ai
Co-Founder & Director
Restoring liquidity to hospital networks by deploying clinical natural language models to automatically predict, prevent, and successfully appeal medical insurance claim denials.
In the global healthcare economy, commercial payers leverage automated algorithms to systematically reject more than $300 billion in legitimate hospital insurance claims annually. Facing complex bureaucratic appeals processes, healthcare providers write off billions in earned revenue simply because they lack the administrative bandwidth to manually contest each denial. DenyFix.ai automates the entire appeal lifecycle through deep clinical and regulatory artificial intelligence.
[ CAPABILITIES ]
Clinical Evidence Chart Extraction
Proprietary LLMs extract supporting clinical evidence, operative notes, and laboratory findings from unstructured EHR records, assembling an irrefutable evidentiary packet for medical necessity.
Algorithmic Legal Appeal Synthesis
Automatically generates customized, legally cited appeal briefs directly addressing payer denial codes, citing statutory appeal deadlines and relevant Medicare/Medicaid regulatory precedents.
Payer Adjudication Telemetry
Aggregates behavioral adjudication data across insurance conglomerates, uncovering silent policy changes, downcoding patterns, and unannounced automated denial algorithms in real time.
[ SPECIFICATIONS ]
- Venture Name
- DenyFix.ai
- Executive Role
- Co-Founder & Director
- Market Target
- Hospital Systems & RCMs
- Initial Overturn Rate
- 94.2% Success Metric
- A/R Days Reduction
- 65% Acceleration
- Regulatory Standard
- HIPAA, HITECH, SOC2
- EHR Integrations
- Epic, Cerner, Allscripts
[ RELATED ]
Full detail appears in the executive dossier.