Ardia publishes original research on healthcare revenue cycle economics, denial patterns, PAMA impact, quantum readiness, and the science of autonomous clinical proof systems. Our findings are freely available to labs, health systems, and healthcare investors.
Quantifies the structural failure in healthcare reimbursement — the inability of providers to economically prove care necessity — and introduces the Financial Adversary Engine as the first viable autonomous solution.
Analyzes the compounding effect of PAMA rate cuts and rising denial rates. Documents how <1% lab participation in 2016 data collection skewed Medicare pricing toward Quest/LabCorp scale rates, and maps the path to survival for independent labs.
Maps the complete Dallas-Fort Worth independent laboratory ecosystem — toxicology labs, molecular and genomic diagnostic labs, and pulmonary/respiratory practices — with estimated denial exposure and prioritized go-to-market targets.
Demonstrates why generic LLMs pose FCA liability risk in medical billing and introduces the 4-layer Triadic Adjudicative Reasoning Architecture (TARA) that constrains generative AI between deterministic logic layers to keep every assertion traceable to a verifiable source.
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Subscribe to ResearchThis is the open scientific question behind Ardia, and it is largely unmeasured. Published work on clinical AI focuses on diagnostic accuracy; almost none measures calibrated abstention on administrative reasoning — where the failure mode is silent. A wrongly upheld denial produces no error signal, just an unappealed claim and a patient who never receives the therapy their sequencing already identified.
We frame the problem with externally sourced figures — molecular and genomic denial rates of 23–31%, and roughly 65% of denials never appealed. These validate the problem, not our solution, and we present them that way.
Six deterministic gates, de-identification before inference, and 34 tests in CI. A failed gate withholds the answer rather than flagging it. This proves the governance is real and auditable on synthetic inputs — not that it is accurate on real denials.
Under a Limited Data Set and Data Use Agreement, run the engine against a design-partner laboratory’s historical, already-adjudicated denials where ground truth is known. Not yet performed — this is what a design partner unblocks.
Alongside the core validation study, we are pursuing a computational research track in clinical toxicology — drug-of-abuse and controlled-substance monitoring. Using de-identified test results, orders and adjudicated claims, we examine three questions: how well ordered testing aligns with the governing coverage policy (LCDs L36393 and L34645); which medically necessary toxicology tests are denied and why; and whether a governed engine can distinguish genuinely non-covered testing from wrongly denied testing.
Status: not yet begun. We hold no Data Use Agreement and process no real patient data today. This track starts when a design-partner laboratory and the appropriate human-subjects determination are both in place.
Sireesha Mamillapalli, Ph.D. — Assistant Professor of Biochemistry and Physiology, Geisinger Commonwealth School of Medicine, and a member of Ardia’s board — provides methodological oversight of validation design: how we structure ground-truth comparison, and how we report results credibly rather than selectively. We are also actively recruiting a lab revenue-cycle advisor and a healthcare-AI regulatory advisor.
No real-world validation. All work to date runs on synthetic data, so bias and subgroup performance cannot be assessed. Our HIPAA control matrix is self-graded 2 of 15. Respiratory guideline retrieval is still in development. De-identification catches structured identifiers; name detection is in progress. We state this here because a research claim that cannot be checked is not a research claim.
