Evidence Vault

Circle: March 2 A Million (CMS PRO-PM + Non-Opioid Post-Op Pain Management) Observational Protocol: TKA-001Data “Swamps” v. High Quality Data LakesThe Paradigm Shift: Physician Ownership/Monetization Rights“Big Data Lakes” are an attempt to convert EMR, claims, and other real-world healthcare data into discrete datasets which can be used for clinical decision-making, research, and value-based care. However, they have proved to be “data swamps”. The opaque algorithm-driven synthesis from hundreds of heterogeneous sources, and millions of heterogeneous records, have only resulted in higher costs, worse outcomes, and less reliable conclusions. This failure will only deepen in the era of AI. Traditional data lakes are a multi-billion dollar business. Yet the principal data sources – providers who enter EMR, Rx, claims, registry, and other records – see little if any of that value. Circle Datasets invert that model by giving physicians unambiguous ownership of – and value for – their data. Moreover, Circle Dataset quality is materially enhanced through structuring based on practice-of-medicine clinical hypotheses and outcomes capture. The best quality healthcare data is (i) captured at the point of care, (ii) structured according to practice-of-medicine clinical hypotheses, (iii) tied to correlated long-term outcomes, and (iv) fully verifiable. In other words, Circle Datasets – clinically/scientifically impactful data lakes refreshed in real-time from pre-structured primary data sources. An Illustrative Monetization ExampleCircle Datasets can be generated with minimal clinical burden and cost. RegenMed’s patented technical infrastructure and processes ensure unambiguous physician ownership, publication rights, evidence-based clinical decision-making, superior patient engagement, and data monetization opportunities. Simple, flexible contracting; no long-term commitments; cancel anytime.A Powerful Clinical and Financial Combination*Applicable to the OREF/Wickline/Mercuri Protocol only. Other OP’s: 85% to participating physicians.ILLUSTRATIVE ALLOCATIONS (First Year Only)AssumptionsResults1 Observational Protocol (OP)Total Cases: 1,20010 Collaborating PhysiciansLicenses: 6010 Cases/Month/Physician Net License Fees: $1,500,00012 Licensable DatasetsTo Each Non-PI Physician: $60,000 *5 Net Licenses/ Licensable Dataset @ $25,000Cost per Non-PI Physician: $1,02040% Net License Fees to Non-PI Physicians *Net Profit per Non-PI Physician: $58,980 *Non-PI Physician Cost: $5/Case, $35/monthROI per Non-PI Physician: 58x *‍

The PremiseScience was once the organized skepticism of civilization — a disciplined method for transforming doubt into understanding. But in the modern era, that skepticism has been commodified. Doubt itself has become a product — manufactured, traded, and weaponized. The more uncertain the evidence, the more profitable the debate. In this inverted moral economy, the very act of questioning can serve ignorance as efficiently as it once served truth. The problem is not the presence of uncertainty, but its exploitation. In the industrialized research economy, ambiguity sustains funding; controversy sustains attention; and perpetual revision sustains institutions that thrive on the illusion of progress. Doubt is no longer a stage toward knowledge — it is the business model of modern science. The Distortion This economy rewards paralysis over clarity. Entire fields orbit unsolved questions not because they are insoluble, but because they are lucrative. Each new study extends the horizon of uncertainty just enough to justify the next grant cycle. The rhetorical tools of humility — caveats, limitations, “more research is needed” — become instruments of inertia. Doubt, once epistemic modesty, has been repackaged as moral virtue. Meanwhile, industries external to science — pharmaceuticals, policy think tanks, media outlets — capitalize on this cultivated ambiguity. Manufactured uncertainty becomes a shield for inaction. When doubt can be monetized, truth becomes a threat to market stability. The Consequence The transformation of doubt into commodity corrodes public trust. Citizens encounter a world where every study is contradicted by another, every conclusion softened by caveat. The lay observer no longer distinguishes between scientific caution and institutional evasion. Into that confusion steps ideology, offering certainty as an emotional balm. The tragedy is not merely epistemic but democratic: when the language of science ceases to resolve uncertainty, demagogues will. Within research itself, the moral cost is despair. Scientists trained to seek understanding become artisans of ambiguity. Inquiry becomes a theater of perpetual hesitation — the ritual of deferral masquerading as rigor. The Way Forward Science must reclaim doubt as discipline, not currency. Uncertainty should be bracketed, not broadcast; acknowledged, not amplified. Funding structures can reward closure — synthesis papers, confirmatory meta-analyses, and knowledge integration — as much as new exploration. The moral challenge is to restore courage: the willingness to finish a sentence, to say what is known and bear the weight of saying it. Doubt should again be a doorway, not a dwelling. ReferencesRegenMed (2025). Genuine Medical Research Has Lost Its Way. White Paper, November 2025Oreskes, N., & Conway, E. M. (2010). Merchants of Doubt. Bloomsbury Press. Ioannidis, J. P. A. (2014). How to Make More Published Research True. PLoS Medicine, 11(10). Sarewitz, D. (2018). The Twilight of the Scientific Elite. Issues in Science and Technology, 35(1). Collins, H. M., & Evans, R. (2007). Rethinking Expertise. University of Chicago Press. Funtowicz, S. O., & Ravetz, J. R. (1993). Science for the Post-Normal Age. Futures, 25(7), 739–755. Get involved or learn more — contact us today!If you are interested in contributing to this important initiative or learning more about how you can be involved, please contact us.

‍When honesty becomes the foundation of liquidity.The Economics of BeliefEvery market begins with faith — that what is exchanged will be honored. But modern medicine trades in abstractions that faith can no longer sustain: algorithms we cannot audit, datasets we cannot trace, and claims we cannot verify. The market for truth collapsed not through corruption, but through opacity. When provenance disappears, credit follows. Circle Coin repairs the balance sheet of belief by making honesty fungible. It transforms truth from moral ornament to financial infrastructure. The Failure of ReputationInstitutions once guaranteed integrity through their names — academic hospitals, journals, regulators. Today, those reputations function like overdrawn accounts: they spend trust faster than they earn it. Circle’s distributed model replaces institutional reputation with transactional proof. Each verified event — a patient’s consent, a dataset’s validation, a physician’s attestation — accrues intrinsic credit recorded permanently on the network. Reputation becomes measurable and portable, because trust is no longer declared; it is demonstrated. Proof as MediumIn classical finance, currency is the medium through which value moves; in the Circle system, proof is that medium. Every exchange — whether economic, clinical, or informational — passes through a layer of verification. This transforms ethics into a market function. The cost of honesty becomes zero, the cost of deceit infinite. Each Circle Health Coin (CHC) thus represents not merely ownership of data, but the liquidity of integrity — proof that can circulate, compound, and clear.The Decentralization of TrustCentralized validation (peer review, audit committees, IRBs) cannot scale to modern information velocity. Federation solves this bottleneck: every node becomes both participant and verifier. Trust moves horizontally, at the speed of consensus. This networked verification creates ethical liquidity — the ability of truth to flow freely without dependence on central intermediaries. Where honesty was once bureaucratic overhead, it is now the shortest path between two points. The Yield of IntegrityTruth, when structured properly, generates yield. Verified datasets lower compliance costs, increase clinical reliability, and attract capital investment. Every act of verification enriches the ecosystem — a compounding dividend paid in confidence. Circle’s architecture captures this yield through tokenization: each CHC represents a share of the moral productivity of the network. Integrity becomes not only virtue but asset class. The Moral Outcome Currency began as a trust technology; Circle restores it to that purpose. It redefines value as verified honesty, and markets as instruments of moral alignment. In the Circle economy, wealth no longer accumulates through possession, but through proof. The oldest promise of commerce — that truth will be honored — becomes, at last, a protocol. Selected References RegenMed (2024). Circle Datasets: The Foundation For Circle Health Coins.OECD (2024). Trust as Economic Infrastructure. Deloitte (2024). The Liquidity of Integrity. European Commission (2025). Verification Economies in Health Data Markets. Get Involved Or Learn More — Contact Us Today!If you are interested in contributing to this important initiative or learning more about how youcan be involved, please contact us.

Why federated systems that retain local meaning outperform centralized silos.The Blind Spot of IntelligenceArtificial intelligence has conquered pattern recognition but not interpretation. It can detect anomalies faster than any clinician, yet it cannot explain why they matter. That is because the information it consumes has been stripped of the one thing that makes it humanly intelligible: context.A data point divorced from its origin — who entered it, when, and under what conditions — is not knowledge. It is residue. Modern healthcare AI is trained on residue disguised as information.Context, not computation, is what medicine has been missing.The Anatomy of Context In clinical reality, data is never neutral. Every entry encodes environment, intention, and sequence. A “blood pressure 180/100” in isolation means nothing; in the chart of a trauma patient under sedation, it means everything.Context in medicine is multi-layered:Temporal: What changed before and after the event?Procedural: Who made the decision, and why?Environmental: What institutional or technological factors influenced it?Interpretive: What was believed at the time?AI systems that ignore these layers become impressive calculators of irrelevance.The Centralization Fallacy Centralized data architectures promise simplicity through uniformity: aggregate everything, clean it later. But cleaning is not the same as clarifying. The process of normalization removes precisely the differences that made the data interpretable.The Centralization FallacyCentralized data architectures promise simplicity through uniformity: aggregate everything, clean it later. But cleaning is not the same as clarifying. The process of normalization removes precisely the differences that made the data interpretable. Context does not survive transit; it must remain anchored where it was born. Centralization therefore breeds blindness. It converts medicine’s living variability into dead averages. The irony is that by trying to make everything comparable, we make nothing meaningful. Federation as Context PreservationFederated systems reverse the entropy of meaning. They allow data to stay local — inside the environment that gives it interpretive depth — while still contributing to shared computation. Each participating site maintains control of its own data model, applies local metadata, and transmits only verified derivatives to the network. Circle Datasets are built on this principle: context is never exported, only referenced. The model learns from diversity without erasing it. It “knows” that the same lab value may mean something different in different settings — and respects that difference as information. Context as SignalIn modern learning health systems, the next differentiator is not more data, but richer context per datum. Temporal sequences, decision pathways, and institutional metadata can all become signal if they are preserved structurally. Federated architectures can encode this through standardized ontologies and audit trails that travel with each contribution. This turns every observation into a mini-experiment — one whose conditions are transparent and reproducible. The system ceases to be a static warehouse and becomes a continuously annotated conversation. The Epistemic DividendWhen context is preserved, two transformations occur: Clinical: Models become interpretable. Clinicians can trace not only what was predicted, but why it made sense locally. Regulatory: Oversight becomes easier. Inspectors no longer rely on trust but on traceable evidence of provenance and process. The dividend is moral as much as technical: meaning is no longer sacrificed for efficiency. Context reintroduces narrative — the human texture of decision — into the machine’s logic. The Moral Geometry of Federation Context is not just metadata; it is moral architecture. It binds data to responsibility. When a record retains its local coordinates, someone remains answerable for it. This transforms governance from bureaucracy into conscience: every data steward knows their contribution is visible, interpretable, and consequential. Federation therefore doesn’t just preserve context — it preserves care. The next era of AI will not belong to systems that think faster, but to those that remember better. The Circle Principle The Circle model’s brilliance lies in treating context as continuity — maintaining the chain between the act of observation and its analytic use. That continuity is the foundation of trust, because it keeps human judgment and machine reasoning in dialogue. In the end, intelligence is not the ability to process information; it is the ability to understand circumstance. Federated context makes that possible. Selected References RegenMed (2025). Circle Datasets For Federated Healthcare Data Models. White Paper. Amann, J. et al. (2022). Explainability and Trustworthiness in AI-Based Clinical Decision Support. Nature Medicine. Gebru, T. et al. (2021). Datasheets for Datasets. Communications of the ACM. OECD (2024). Data Provenance and Context Preservation in Health AI. Get involved or learn more — contact us today!If you are interested in contributing to this important initiative or learning more about how you can be involved, please contact us.

AbstractKnee osteoarthritis (KOA) represents one of the most costly and clinically ambiguous conditions in modern musculoskeletal care. Intra-articular hyaluronic acid (HA) viscosupplementation occupy a contested position between conservative therapy and total knee arthroplasty, simultaneously supported by widespread clinical use and challenged by guideline skepticism and payer resistance. This article argues that the controversy surrounding viscosupplementation is not fundamentally pharmacologic, but evidentiary. By shifting from retrospective, inferential data to prospectively captured, schema-driven real-world evidence (RWE), providers can generate deterministic “ground truth” capable of resolving long-standing disputes over efficacy, value, and appropriate patient selection. We examine how imaging-verified delivery, longitudinal outcomes, and synthetic control arms can transform viscosupplementation from a commoditized procedure into a verifiable, value-based intervention.The Viscosupplementation ParadoxThe global viscosupplementation market is substantial and growing, driven by demographic aging and the rising prevalence of osteoarthritis. Estimates place the market between approximately $1.6 and $4.7 billion annually, with projected compound annual growth rates near 8–9% through the early 2030s. Despite this scale, viscosupplementation remains persistently controversial.Clinical guidelines illustrate this tension. The American Academy of Orthopaedic Surgeons (AAOS) has historically recommended against routine use of HA injections for knee osteoarthritis, citing modest average improvements over placebo in randomized controlled trials (RCTs). Although the most recent guidelines softened earlier language, they continue to question whether statistically significant effects translate into clinically meaningful benefit for the average patient. In contrast, rheumatology societies and manypracticing clinicians report consistent benefit in carefully selected patients, particularly earlier in the disease course.This disconnect has real consequences. Payers increasingly impose step-therapy requirements, restrict coverage, or deny reimbursement altogether. Providers face heightened audit risk, while manufacturers struggle to defend formulary placement. Yet none of these actors dispute the underlying biology of HA; rather, they disagree about who benefits, under what conditions, and by how much.The Limits of Conventional EvidenceAt the heart of the controversy lies a structural problem in how evidence is generated. Traditional RCTs in knee osteoarthritis aggregate heterogeneous populations, often including late-stage patients with minimal remaining cartilage — patients unlikely to respond to viscosupplementation. This dilution effect suppresses observed efficacy and masks responder subgroups.Compounding the issue is the unusually large placebo response in osteoarthritis trials. Placebo effects account for up to 60–75% of observed pain reduction in some intra-articular injection studies, obscuring true treatment effects and contributing to late-stage trial failures. When outcomes are subjective and follow-up inconsistent, inferential gaps become unavoidable.Real-world data, as currently captured, does little to solve this problem. Claims data and unstructured electronic medical record (EMR) notes lack confirmation of intra-articular delivery accuracy, disease severity, or standardized outcomes. These “data exhaust” sources require heavy inference and cannot establish causality. As a result, regulators, payers, and guideline bodies are forced to guess at value rather than verify it.Schema-on-Capture and Deterministic Ground TruthA fundamentally different approach is required — one that treats data generation as a clinical act rather than a byproduct of billing. The schema-on-capture model proposes defining the evidentiary architecture before care is delivered. In the context of viscosupplementation, this means prospectively capturing:Verified delivery: Fluoroscopic or image-guided confirmation that HA was delivered intra-articularly, rather than peri-articularly or into the fat pad. Accuracy rates for blind knee injections may be as low as 70–80%, compared with >95% for image-guided techniques, a difference with potential downstream impact on outcomes.Structured disease state: Radiographic severity (e.g., Kellgren–Lawrence grade, joint space width) captured in standardized, machine-readable form.Longitudinal outcomes: Validated patient-reported outcome measures (WOMAC, KOOS-JR) collected at fixed intervals, combined with objective endpoints such as time to total knee arthroplasty.Optional biologic context: Synovial fluid biomarkers, when available, to characterize inflammatory phenotypes associated with response or non-response. By enforcing structure at the moment of care, schema-on-capture eliminates the inference gap. Each record becomes a deterministic unit of evidence rather than a probabilistic data point.Precision, Placebo, and the Synthetic Control ArmOne of the most powerful applications of deterministic RWE is the construction of high-fidelity synthetic control arms. In osteoarthritis drug development, placebo-controlled trials are expensive, slow, and ethically contentious due to high screen-failure rates and strong placebo responses. Pharmaceutical sponsors routinely spend $40,000–$100,000 per patient in late-phase trials.An imaging-verified, longitudinal dataset of patients receiving standard-of-care viscosupplementation can serve as an external comparator, dramatically reducing trial costs while improving interpretability. Unlike conventional registries, such datasets explicitly characterize the “ritual of care” associated with injections — frequency of visits, imaging, clinician interaction — allowing placebo effects to be modeled rather than ignored .This approach aligns with growing regulatory acceptance of real-world evidence and synthetic control methodologies, particularly in areas where traditional trials are impractical or ethically fraught. Importantly, the value of such datasets derives not from scale alone, but from veracity.Economic Implications: From Commodityto AssetHealthcare data is often treated as a commodity, but evidence quality follows a steep valuation gradient. Low-fidelity claims data may command $50–$150 per record, while curated clinical registries reach $500–$1,500 per patient. Deterministic, regulatory-grade longitudinal records — analogous to those that underpinned acquisitions such as Flatiron Health and CorEvitas — can justify valuations exceeding $5,000 per record when used in regulatory, payer, or drug-development contexts.For large KOA provider groups, this reframes the economics of care delivery. Rather than maximizing procedure volume under reimbursement pressure, practices can generate durable data assets that support value-based contracts, post-market surveillance, and licensing to life-science partners. In this model, data quality becomes a clinical and financial imperative.Reframing the Clinical DebateThe long-running dispute over viscosupplementation efficacy has persisted not because the therapy is ineffective, but because the evidence infrastructure has been inadequate. Average effects derived from heterogeneous populations and unverified delivery cannot resolve questions of precision medicine.Deterministic RWE offers a path forward. By identifying responder phenotypes, verifying technical success, and anchoring outcomes in longitudinal reality, clinicians can move the discussion from ideology to proof. For payers and regulators, this enables rational coverage decisions based on measurable cost avoidance, such as delayed arthroplasty. For patients, it offers a clearer answer to asimple question: Will this work for someone like me?ConclusionKnee osteoarthritis sits at the intersection of clinical uncertainty, economic pressure, and regulatory scrutiny. Viscosupplementation has become a proxy battle for deeper failures in how healthcare value is measured. The solution is not another meta-analysis of imperfect trials, but a structural redesign of evidence generation itself.Schema-on-capture, imaging-verified delivery, and deterministic real-world datasets allow providers to become architects of truth rather than subjects of inference. In doing so, they can transform viscosupplementation from a contested procedure into a verifiable, precision-guided intervention — and, in the process, redefine their role in the evidence economy.‍References RegenMed, Circle Datasets for the Viscosupplements Market with a Focus on Knee Osteoarthritis, December 2025. American Academy of Orthopaedic Surgeons. Management of Osteoarthritis of the Knee (Non-Arthroplasty): Clinical Practice Guideline, 2021. Borst et al. Placebo Effect Sizes in Clinical Trials of Knee Osteoarthritis Using Intra-Articular Injections. Arthritis Care & Research, 2025. Roche. Roche to Acquire Flatiron Health to Accelerate Industry-Wide Use of Real-World Data, 2018. Thermo Fisher Scientific. Thermo Fisher Scientific Acquires CorEvitas for $912.5M, 2023.‍

Why most healthcare AI breaks under real-world conditions — and how verifiable data makes it durable. From Accuracy to Fragility Healthcare AI excels in benchmarks and struggles in practice. An algorithm may post a 94 percent F1-score in validation and still misfire when exposed to the variability of actual care. Small shifts in patient demographics, documentation style, or instrumentation can degrade performance overnight. This brittleness is not a model flaw; it is a data inheritance problem. AI learns whatever instability exists in its training source — and amplifies it. When the foundation is incomplete, intelligence becomes fragile. The Hidden Instability in Healthcare Data Clinical data is inherently dynamic: patients move across systems, therapies evolve, and coding standards shift. Yet most training datasets capture a single snapshot in time — an incomplete view that cannot sustain learning across change. This leads to three predictable weaknesses: Temporal drift: models trained on past cohorts fail on present ones. Context loss: missing longitudinal context creates misleading correlations. Structural noise: inconsistent coding and missing metadata degrade signal quality. In other words, today’s AI is only as stable as yesterday’s documentation. Verification as Structural Reinforcement Circle resolves fragility by embedding verification into the data’s life cycle. Every record is created under an Observational Protocol that enforces standardized capture, continuous lineage tracking, and cryptographic validation. This transforms raw data into ground truth — information with structural integrity: Each variable’s origin and update history are recorded. Quality metrics and validation status travel with the record. Time, context, and consent remain verifiable across every reuse. The result is a learning substrate that can withstand change because its truth is self-documenting. Resilience Through Continuity Resilient intelligence requires continuity, not just volume. When AI models train on Circle datasets, they inherit longitudinal consistency: patient trajectories, treatment histories, and outcomes are all linked through verifiable timelines. This continuity stabilizes learning curves and reduces model drift. Algorithms retrain faster, recalibrate automatically, and maintain performance as clinical patterns evolve. For clinicians, that means reliability; for regulators, traceability; for investors, predictable durability. Operational and Economic ImpactFragile AI increases downstream cost: more manual oversight, more false positives, more wasted validation cycles. Resilient AI built on verified data does the opposite — it compounds efficiency. Hospitals spend less time auditing; payers process fewer disputes; researchers reuse datasets confidently across studies. Each proof-ready dataset becomes a reusable asset that strengthens with time — turning verification from a defensive measure into a productivity engine. Strategic Outcome The failure of fragile intelligence is not a cautionary tale — it’s an engineering lesson. Healthcare AI will mature when it stops optimizing for accuracy and starts designing for durability. Circle’s verifiable data architecture gives the industry that foundation: a continuous, self-reinforcing evidence base where learning models evolve safely with reality rather than apart from it. In a market now defined by reproducibility and accountability, resilience — not novelty — is the new frontier of intelligence. Key Takeaways Get involved or learn more — contact us today!If you are interested in contributing to this important initiative or learning more about how you can be involved, please contact us.