Real-World Evidence Circles

01/29/2026

𝐈𝐬 𝐲𝐨𝐮𝐫 𝐌𝐒𝐎 𝐫𝐞𝐚𝐝𝐲 𝐟𝐨𝐫 𝐭𝐡𝐞 𝐁𝐢𝐨𝐬𝐢𝐦𝐢𝐥𝐚𝐫 𝐕𝐨𝐥𝐮𝐦𝐞 𝐖𝐚𝐯𝐞?

Biologics are the margin play, but Biosimilars are the volume play. With the FDA now treating biosimilars like traditional generics, automatic substitution is about to become the new standard.

To win in this high-volume environment, you must move beyond 𝐀𝐝𝐦𝐢𝐧𝐢𝐬𝐭𝐫𝐚𝐭𝐢𝐯𝐞 𝐏𝐫𝐨𝐱𝐢𝐞𝐬. You need 𝐈𝐧𝐬𝐮𝐫𝐚𝐛𝐥𝐞 𝐈𝐧𝐭𝐞𝐠𝐫𝐢𝐭𝐲.

By utilizing 𝐎𝐛𝐬𝐞𝐫𝐯𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐏𝐫𝐨𝐭𝐨𝐜𝐨𝐥𝐬, we turn routine documentation into a high-margin, licensable asset. We help you achieve 𝐌𝐮𝐥𝐭𝐢𝐩𝐥𝐞 𝐄𝐱𝐩𝐚𝐧𝐬𝐢𝐨𝐧 by reclassifying your clinical nodes as 𝐓𝐞𝐜𝐡-𝐄𝐧𝐚𝐛𝐥𝐞𝐝 𝐀𝐬𝐬𝐞𝐭𝐬.

The data veracity is the only thing that can't be commoditized.
https://hubs.li/Q040Rz-m0

FDA removes biosimilar switching studies, enabling automatic substitution and shifting market success to real-world evidence proving clinical equivalence.

01/28/2026

𝐓𝐡𝐞 𝟖-𝐘𝐞𝐚𝐫 𝐑&𝐃 𝐁𝐚𝐫𝐫𝐢𝐞𝐫 𝐣𝐮𝐬𝐭 𝐜𝐨𝐥𝐥𝐚𝐩𝐬𝐞𝐝. 𝐈𝐬 𝐲𝐨𝐮𝐫 𝐝𝐚𝐭𝐚 𝐬𝐭𝐫𝐚𝐭𝐞𝐠𝐲 𝐬𝐭𝐢𝐥𝐥 𝐦𝐨𝐯𝐢𝐧𝐠 𝐚𝐭 𝐚 𝐬𝐧𝐚𝐢𝐥'𝐬 𝐩𝐚𝐜𝐞?

Commissioner Marty Makary has signaled the end of the "Red Tape" era for biosimilars. With the FDA halving the time and money for market entry, the 8-year development marathon is becoming a 30-month sprint.

𝐓𝐡𝐞 𝐂𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞: Speed without Veracity is a Liability. Accelerated approvals demand a new standard of 𝐈𝐧𝐬𝐮𝐫𝐚𝐛𝐥𝐞 𝐑𝐢𝐬𝐤 𝐌𝐨𝐝𝐞𝐥𝐢𝐧𝐠.

𝐓𝐡𝐞 𝐒𝐨𝐥𝐮𝐭𝐢𝐨𝐧: You need more than "Data Exhaust." You need 𝐑𝐞𝐠𝐮𝐥𝐚𝐭𝐨𝐫𝐲-𝐆𝐫𝐚𝐝𝐞 𝐆𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞. By using pre-structured 𝐎𝐛𝐬𝐞𝐫𝐯𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐏𝐫𝐨𝐭𝐨𝐜𝐨𝐥𝐬, we turn your clinical nodes into high-veracity research assets that provide the 𝐀𝐮𝐝𝐢𝐭-𝐑𝐞𝐚𝐝𝐲 "𝐆𝐫𝐨𝐮𝐧𝐝 𝐓𝐫𝐮𝐭𝐡" payers and litigators now demand: https://hubs.li/Q040Fqmj0

FDA shortens biosimilar R&D from years to 30 months, shifting approval to analytics and making real-world clinical evidence critical for payer trust.

01/27/2026

Consent is becoming the true currency of healthcare data. Discover why ethical value now depends on permission—not possession:

This article argues that ethical data value comes from continuous, traceable consent, redefining ownership, integrity, and trust in medical data systems.

01/21/2026

𝐁𝐢𝐠𝐠𝐞𝐫 𝐈𝐬𝐧’𝐭 𝐒𝐦𝐚𝐫𝐭𝐞𝐫: 𝐖𝐡𝐲 𝐇𝐞𝐚𝐥𝐭𝐡𝐜𝐚𝐫𝐞 𝐀𝐈 𝐍𝐞𝐞𝐝𝐬 𝐚𝐧 𝐄𝐩𝐢𝐬𝐭𝐞𝐦𝐢𝐜 𝐑𝐞𝐬𝐞𝐭

For years, the mythology of machine learning has worshipped scale — more data, more records, more parameters. In medicine, this belief became doctrine: if we gather millions of patient encounters, surely our models will become “truly intelligent.”

Discover in our article why bigger isn't always better in healthcare AI. Learn how precision, context, and responsible federation redefine "scale"—turning data volume into trustworthy insights and ethical innovation. Ready to rethink the future of medical intelligence? Read more: https://www.rgnmed.com/post/the-mirage-of-scale

01/13/2026

𝐖𝐡𝐞𝐧 𝐃𝐨𝐮𝐛𝐭 𝐁𝐞𝐜𝐨𝐦𝐞𝐬 𝐚 𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐌𝐨𝐝𝐞𝐥: 𝐓𝐡𝐞 𝐇𝐢𝐝𝐝𝐞𝐧 𝐂𝐫𝐢𝐬𝐢𝐬 𝐢𝐧 𝐌𝐨𝐝𝐞𝐫𝐧 𝐒𝐜𝐢𝐞𝐧𝐜𝐞

Science was once the disciplined pursuit of truth. Today, doubt has been commodified—manufactured, monetized, and weaponized. In a system where ambiguity sustains funding and controversy drives attention, progress becomes an illusion. How do we reclaim science’s integrity? Discover the moral challenge and the way forward in our latest article:

Modern science monetizes doubt, eroding trust and progress. Learn how uncertainty became a business model—and what we can do to restore integrity.

01/12/2026

𝐖𝐡𝐲 𝐃𝐨𝐞𝐬 𝐚 "𝐁𝐫𝐞𝐚𝐤𝐭𝐡𝐫𝐨𝐮𝐠𝐡" 𝐃𝐞𝐯𝐢𝐜𝐞 𝐅𝐫𝐨𝐦 𝐭𝐡𝐞 𝐅𝐃𝐀 𝐆𝐞𝐭 𝐚 "𝐍𝐨" 𝐅𝐫𝐨𝐦 𝐭𝐡𝐞 𝐂𝐚𝐫𝐫𝐢𝐞𝐫?

We recently had an interesting exchange with neurosurgeon Ahilan Sivaganesan at Hospital For Special Surgery on this exact disconnect. He describes it as the "Twin Sins" of healthcare: Prior authorization acting as both a barrier to high-value care and an enabler of low-value care.

The root cause isn't just policy—it's an 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 𝐏𝐫𝐨𝐛𝐥𝐞𝐦.

Most systems today are "scraping the exhaust"—attempting to find clinical meaning in billing codes and messy notes after the fact to "guess" at an outcome. This is "schema-on-read," and it creates an inference gap that insurers use to deny innovative treatments like TOPS surgery.

To build the 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐇𝐞𝐚𝐥𝐭𝐡𝐜𝐚𝐫𝐞 𝐒𝐲𝐬𝐭𝐞𝐦 Dr. Sivaganesan envisions, we need to move to 𝐒𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐢𝐧𝐠 𝐚𝐭 𝐭𝐡𝐞 𝐒𝐨𝐮𝐫𝐜𝐞. By defining the structure before the care happens (“schema-on-capture”), we:

▪️ 𝐌𝐢𝐧𝐭 𝐆𝐫𝐨𝐮𝐧𝐝 𝐓𝐫𝐮𝐭𝐡: Physicians capture high-fidelity, longitudinal evidence as a byproduct of their workflow.

▪️ 𝐎𝐯𝐞𝐫𝐫𝐢𝐝𝐞 𝐒𝐭𝐚𝐭𝐢𝐜 𝐑𝐮𝐥𝐞𝐛𝐨𝐨𝐤𝐬: We provide deterministic proof of value that carriers can no longer ignore.

▪️ 𝐑𝐞𝐬𝐭𝐨𝐫𝐞 𝐒𝐜𝐢𝐞𝐧𝐭𝐢𝐟𝐢𝐜 𝐒𝐨𝐯𝐞𝐫𝐞𝐢𝐠𝐧𝐭𝐲: The clinician—not the administrator—becomes the primary architect of the evidence.

Healthcare doesn't need more "data exhaust." It needs new rails for clinical veracity.

01/08/2026

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If you are interested in contributing to this important initiative or learning more about how you can be involved, please contact us.

Circle Coin: Transforming trust into liquidity. Discover how blockchain and tokenization make honesty an asset, proof a currency, and integrity the foundation of the new healthcare economy.

01/07/2026

𝐓𝐡𝐞 𝐅𝐃𝐀 𝐡𝐚𝐬 𝐜𝐥𝐞𝐚𝐫𝐞𝐝 𝐭𝐡𝐞 𝐩𝐚𝐭𝐡. 𝐈𝐬 𝐲𝐨𝐮𝐫 𝐒𝐨𝐜𝐢𝐞𝐭𝐲 𝐫𝐞𝐚𝐝𝐲?

A major rule change at the now allows de-identified, real-world to play a central role in drug and medical device reviews. This has created an unprecedented demand for high-quality, specialty-specific information — the kind a medical society’s members can provide.

𝐂𝐢𝐫𝐜𝐥𝐞 𝐃𝐚𝐭𝐚𝐬𝐞𝐭𝐬 position those societies at the heart of this innovation.

✔️𝐑𝐞𝐠𝐮𝐥𝐚𝐭𝐨𝐫𝐲-𝐆𝐫𝐚𝐝𝐞 𝐃𝐚𝐭𝐚: Capture the clinical rigor required for new product approvals.
✔️𝐀𝐈 𝐑𝐞𝐚𝐝𝐢𝐧𝐞𝐬𝐬: Provide the accurate, real-world information needed to train the next generation of medical AI.
✔️𝐌𝐞𝐦𝐛𝐞𝐫 𝐁𝐞𝐧𝐞𝐟𝐢𝐭𝐬: Give members a way to benchmark their own results against the best in their field.

Third-party “big-data” aggregators should not be the middleman for a medical society’s data. Rather, a society should build a sovereign resource that benefits its members and its members’ patients directly.

📄𝐃𝐨𝐰𝐧𝐥𝐨𝐚𝐝 𝐭𝐡𝐞 𝐎𝐯𝐞𝐫𝐯𝐢𝐞𝐰: https://hubs.li/Q03Y_4Rg0

01/06/2026

𝐕𝐢𝐬𝐜𝐨𝐬𝐮𝐩𝐩𝐥𝐞𝐦𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧 𝐝𝐨𝐞𝐬𝐧’𝐭 𝐡𝐚𝐯𝐞 𝐚 𝐝𝐫𝐮𝐠 𝐩𝐫𝐨𝐛𝐥𝐞𝐦. 𝐈𝐭 𝐡𝐚𝐬 𝐚𝐧 𝐞𝐯𝐢𝐝𝐞𝐧𝐜𝐞 𝐩𝐫𝐨𝐛𝐥𝐞𝐦.

For years, knee osteoarthritis injections have lived in a strange contradiction:

▪️ Widely used by clinicians
▪️ Increasingly restricted by payers
▪️ Questioned by guidelines
▪️ Yet persistently beneficial for some patients

The real failure isn’t hyaluronic acid. It’s how we measure value.

Most trials aggregate heterogeneous patients, rely on subjective endpoints, and can’t even verify whether the injection reached the joint. Claims data then compounds the problem by forcing inference where verification is required.

There’s a better path forward. By capturing 𝐢𝐦𝐚𝐠𝐢𝐧𝐠-𝐯𝐞𝐫𝐢𝐟𝐢𝐞𝐝 𝐝𝐞𝐥𝐢𝐯𝐞𝐫𝐲, 𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞𝐝 𝐝𝐢𝐬𝐞𝐚𝐬𝐞 𝐬𝐞𝐯𝐞𝐫𝐢𝐭𝐲, 𝐚𝐧𝐝 𝐥𝐨𝐧𝐠𝐢𝐭𝐮𝐝𝐢𝐧𝐚𝐥 𝐨𝐮𝐭𝐜𝐨𝐦𝐞𝐬 at the moment of care, providers can generate 𝘥𝘦𝘵𝘦𝘳𝘮𝘪𝘯𝘪𝘴𝘵𝘪𝘤 #𝘳𝘦𝘢𝘭𝘸𝘰𝘳𝘭𝘥𝘦𝘷𝘪𝘥𝘦𝘯𝘤𝘦 — not data exhaust.

That shift doesn’t just clarify efficacy. It enables:

✔️Precision patient selection
✔️Synthetic control arms for trials
✔️Value-based contracts with payers
✔️Regulatory-grade proof, not averages

This is how procedures become proof — and how clinical debates finally get resolved.

If you are interested in contributing to this important initiative or learning more about how you can be involved, please contact us:
https://hubs.li/Q03Z8Vpr0

12/31/2025

𝐓𝐮𝐫𝐧𝐢𝐧𝐠 𝐝𝐚𝐢𝐥𝐲 𝐩𝐫𝐚𝐜𝐭𝐢𝐜𝐞 𝐢𝐧𝐭𝐨 𝐚 𝐬𝐞𝐥𝐟-𝐟𝐮𝐧𝐝𝐢𝐧𝐠 𝐫𝐞𝐬𝐞𝐚𝐫𝐜𝐡 𝐞𝐧𝐠𝐢𝐧𝐞.

Most clinical registries are expensive to build, hard for members to use and, at the end of the day, offer little real-world clinical value. We believe documenting high-quality care should actually fund the 𝐩𝐫𝐨𝐠𝐫𝐞𝐬𝐬 of care.

With 𝐂𝐢𝐫𝐜𝐥𝐞 𝐃𝐚𝐭𝐚𝐬𝐞𝐭𝐬 𝐟𝐨𝐫 𝐌𝐞𝐝𝐢𝐜𝐚𝐥 𝐒𝐨𝐜𝐢𝐞𝐭𝐢𝐞𝐬, we turn members’ documented outcomes into a sustainable resource for the society’s mission.

✔️𝐍𝐞𝐰 𝐅𝐮𝐧𝐝𝐢𝐧𝐠 𝐒𝐭𝐫𝐞𝐚𝐦𝐬: Use allocations from data licensing to fund research grants and member scholarships.
✔️𝐀𝐭𝐭𝐫𝐚𝐜𝐭 𝐈𝐧𝐧𝐨𝐯𝐚𝐭𝐢𝐨𝐧: Become a hub for industry-sponsored studies and product innovation.
✔️𝐌𝐢𝐧𝐢𝐦𝐚𝐥 𝐄𝐟𝐟𝐨𝐫𝐭: Our turnkey platform is designed for busy clinicians, requiring very little administrative time from the Society.

Medical Societies: Support your members and your mission by reclaiming the value of your specialty's clinical expertise.

📄 See the model here: https://hubs.li/Q03Y_5270

12/30/2025

𝐑𝐞𝐭𝐡𝐢𝐧𝐤𝐢𝐧𝐠 𝐇𝐞𝐚𝐥𝐭𝐡𝐜𝐚𝐫𝐞 𝐃𝐚𝐭𝐚: 𝐖𝐡𝐲 𝐅𝐞𝐝𝐞𝐫𝐚𝐭𝐞𝐝 𝐒𝐲𝐬𝐭𝐞𝐦𝐬 𝐎𝐮𝐭𝐩𝐞𝐫𝐟𝐨𝐫𝐦 𝐂𝐞𝐧𝐭𝐫𝐚𝐥𝐢𝐳𝐞𝐝 𝐒𝐢𝐥𝐨𝐬

In the era of AI, pattern recognition is just the beginning. True medical intelligence requires understanding context — the environment, decisions, and sequences behind every data point. Without it, AI becomes a powerful calculator, but not a true clinician.

𝐓𝐡𝐞 𝐂𝐚𝐬𝐞 𝐟𝐨𝐫 𝐅𝐞𝐝𝐞𝐫𝐚𝐭𝐞𝐝 𝐒𝐲𝐬𝐭𝐞𝐦𝐬:

▪️𝐏𝐫𝐞𝐬𝐞𝐫𝐯𝐞 𝐌𝐞𝐚𝐧𝐢𝐧𝐠 𝐚𝐭 𝐭𝐡𝐞 𝐒𝐨𝐮𝐫𝐜𝐞: Keep data local, maintaining its rich, multi-layered context.

▪️𝐄𝐧𝐡𝐚𝐧𝐜𝐞 𝐈𝐧𝐭𝐞𝐫𝐩𝐫𝐞𝐭𝐚𝐛𝐢𝐥𝐢𝐭𝐲: Models learn from diversity, respecting differences that make data meaningful.

▪️𝐁𝐮𝐢𝐥𝐝 𝐓𝐫𝐮𝐬𝐭 & 𝐑𝐞𝐬𝐩𝐨𝐧𝐬𝐢𝐛𝐢𝐥𝐢𝐭𝐲: Context anchors data to human judgment, strengthening oversight and accountability.

▪️𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦 𝐊𝐧𝐨𝐰𝐥𝐞𝐝𝐠𝐞 𝐢𝐧𝐭𝐨 𝐀𝐜𝐭𝐢𝐨𝐧: Contextual data turns static information into a continuous, transparent conversation — fueling better decisions and patient care.

The future isn’t just faster AI — it’s smarter, more responsible, and fundamentally human-centric. Federated architectures enable this shift by ensuring each data point retains its story, fostering trust and advancing healthcare innovation.

Discover in the article how Circle Datasets and federated models are shaping the next generation of healthcare AI: https://hubs.li/Q03Z9Y-X0

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