Denial prevention that understands why claims fail.
The physician decides. The system serves.
PhysOpsAI catches documentation gaps before submission by matching clinical encounters against payer coverage criteria. Not by learning what your coders did in the past.
Selecting 5 practices for our 2026 founding cohort.
in initial claim denials. Every year.
US healthcare providers submit approximately 6 billion claims annually. 9-12% are initially denied. Each denial costs $25-118 to rework, and most trace to documentation gaps that existed at the point of care. Gaps that could have been caught before the claim was ever submitted.
Most AI denial tools learn from your historical claims data. That means they inherit your coding team's habits, your EHR template artifacts, and your institution's documentation culture. Deploy the same tool at a different practice, and accuracy drops because the patterns it learned were specific to you, not to what payers actually require.
One encounter. Three interventions. Zero denials.
A single integrated pipeline where each stage feeds the next and intelligence compounds. You don't pick one. You get all three.
Document
Clinical Narrative GenerationThe encounter becomes a complete, structured clinical narrative. Medical terminology validated at multiple layers of accuracy. Patient health information separated before any AI processing.
What this catches
- +Incomplete documentation that triggers payer review
- +Terminology errors that map to wrong billing codes
- +Missing clinical elements that payers require for coverage
Code
Billing Code Generation & ValidationDocumentation becomes billing-ready codes. ICD-10 diagnoses, CPT procedures, HCPCS supplies. Every code validated against payer rules and national coding guidelines before a human ever sees it.
What this catches
- +Incorrect codes that trigger automatic denials
- +Unbundling violations and modifier errors
- +Undercoding that leaves revenue on the table
- +Overcoding that triggers audits
Prevent
Denial Risk Analysis & RemediationCodes and documentation are validated against what the specific payer actually requires. Every denial risk flagged with the exact policy language, the specific documentation gap, and actionable remediation. Before the claim is submitted.
What this catches
- +Medical necessity gaps the documentation doesn't support
- +Missing prior authorizations
- +Payer-specific documentation requirements
- +The subtle mismatches that rule-based systems can't find
Each stage makes the next one smarter.
Most denial prevention tools analyze claims after coding, after documentation, working with whatever quality they inherit. PhysOpsAI controls the full pipeline. Documentation is structured for codability. Codes are validated against payer rules. Claims are checked against specific coverage requirements. Each stage guarantees the input quality for the next.
And it compounds over time. Denial outcomes feed back into documentation generation. The system learns which documentation patterns survive payer scrutiny. Every claim submitted through PhysOpsAI makes the next claim more likely to be paid.
PhysOpsAI is a single intelligence pipeline from encounter to clean claim. Each stage is designed to make the next stage's job easier and the final claim harder to deny.
Engineered properties.
Cross-Environment Generalization
Our technology learns the relationship between clinical documentation and payer requirements, not the coding patterns specific to your site.
Deploy at a new practice. No retraining required. Accuracy holds.
Explainable Reasoning
Every denial risk flag includes the specific payer policy language and documentation gap.
No black-box risk scores. The system shows its work.
Privacy by Architecture
Patient data is processed inside hardware-attested secure environments.
AI providers never see identifiable data. No BAA required.
Built on science.
PhysOpsAI's technology is built on peer-reviewed research from Georgia Institute of Technology in how AI systems generalize across clinical environments.
Our work addresses a fundamental challenge: conventional AI systems learn patterns specific to one clinical setting, documentation conventions, coding habits, template artifacts, rather than the underlying medical-regulatory relationships that determine claim outcomes. We developed mathematical frameworks that prevent this failure mode by construction.
Georgia Institute of Technology · Patent Pending
M.R. Amiri
Founder & Chief AI Scientist
M.S. Computer Science (in progress), Georgia Institute of Technology
Specialization: AI & Computing Systems
Bachelor's Degree in Computer Science & Data Science, New York University
The Courant Institute School of Mathematics, Computing, and Data Science
Research in geometric representation learning: how neural networks can be designed to learn invariant relationships across heterogeneous clinical environments rather than site-specific shortcuts. Coursework under Prof. Yann LeCun (Deep Learning, audit) and Prof. Alfredo Canziani (Intro to Deep Learning) at NYU. Health informatics pipelines on MIMIC-III clinical data at Georgia Tech.
USPTO patent bar candidate. Three entities founded across research, clinical AI products, and consulting.
Advised by practicing specialists at a leading children's research hospital.
PhysOpsAI Inc. · Delaware C-Corp · Lake Nona Medical City, Orlando, FL