Evidence Vault

Investing in the Next Generation of Healthcare Technology InnovatorsRegenMed is proud to sponsor a University of Wisconsin–Madison Computer Sciences Capstone Project (CS 639, Fall 2025), joining the ranks of leading organizations such as Epic Systems, Capital One, American Family Insurance, Google, GE HealthCare, and Medtronic that have supported this nationally recognized program.The Capstone program pairs senior undergraduate students with industry partners on real-world software development projects, giving students a chance to tackle meaningful challenges while sponsors gain fresh insights, prototypes, and future talent pipelines. Since launching in 2021, the program has expanded to engage 100+ students and up to 18 sponsors each semester, ranging from Fortune 500 companies to startups and nonprofits.The RegenMed Project: Enhancing Patient-Reported OutcomesRegenMed’s sponsored project, titled “Electronic Patient Reported Outcomes (ePRO) Data Capture”, focuses on improving how patients share their post-treatment experiences.RegenMed’s platform already collects critical treatment information from both clinicians and patients. But one persistent challenge remains: patients often fail to complete outcome surveys in a timely manner. These surveys, known as ePROs, are vital to understanding treatment effectiveness and advancing value-based care.The Capstone team will explore ways to:Redesign the User Experience for patient-facing survey modules.Incorporate Gamification and Rewards to make survey completion more engaging.Develop Web and Mobile Enhancements using tools such as React JS, .NET C#, and AWS Cloud technologies (ECS, Lambda, Cognito, and more).By combining creativity with technical expertise, students will help RegenMed improve patient engagement, generating richer datasets for clinicians, payers, and researchers.Experienced Mentorship for Student SuccessCapstone students benefit from direct mentorship by RegenMed’s leadership and engineering team, including:Dolph Courchaine, RegenMed CIO, with over 40 years of experience in healthcare software engineering.Will Graupmann, RegenMed Software Engineer and UW–Madison alumnus, who brings recent student and startup perspectives.Laura Prey, RegenMed Board Member and IT executive, with more than three decades of experience in healthcare and insurance technology.This mentorship model ensures students learn not only advanced coding and cloud infrastructure, but also the real-world dynamics of healthcare data, insurance, and patient engagement.Why It Matters: Democratizing Healthcare EvidenceThe U.S. spends more than $5 trillion annually on healthcare, much of it on treatments with limited evidence of effectiveness. RegenMed’s mission is to democratize access to treatment-effectiveness data, empowering clinicians, patients, and payers to make better decisions.By sponsoring this project, RegenMed is helping to:Train the next generation of healthcare software engineers.Advance the science of real-world evidence (RWE) and patient engagement.Position Wisconsin students at the forefront of solving one of healthcare’s most pressing challenges.A Proud Tradition of Industry PartnershipsWith sponsors like Epic, GE HealthCare, Google, Amazon/Shopbop, Medtronic, and PBS Wisconsin contributing in past semesters, UW–Madison’s Capstone Program has become a launchpad for impactful collaborations between academia and industry. RegenMed is honored to join this tradition — and to contribute to the future of patient-centered healthcare innovation.

RegenMed and OREF Launch MOTIV™: A Peer-Reviewed Real-World Evidence Library for Musculoskeletal Innovation RegenMed, in partnership with the Orthopaedic Research and Education Foundation (OREF), has launched M.O.T.I.V.™ — a pioneering initiative to generate structured, peer-reviewed, and clinically meaningful real-world evidence (RWE) datasets across the full spectrum of musculoskeletal (MSK) conditions. MSK disorders represent one of the largest global health burdens. Nearly one-third of the world’s population will experience an MSK-related condition in their lifetime, from degenerative joint disease and traumatic injuries to bone cancers and rare genetic disorders such as osteogenesis imperfecta. In the United States alone, MSK care costs are estimated to exceed $980 billion annually, nearly 5% of U.S. GDP. Yet despite this immense cost, outcomes for patients remain inconsistent. Scientific progress is slowed by reliance on traditional clinical trials, which are lengthy, expensive, and selective. Meanwhile, insurers and government payers are placing new emphasis on outcomes tracking — such as Medicare’s penalties for providers who fail to collect long-term results on hip and knee replacements. The Limitations of Today’s Data While “real-world evidence” has become a buzzword in healthcare, the vast majority of available datasets are derived from electronic health records, insurance claims, or proprietary algorithms. These sources, though large, are fragmented, unverifiable, and often lack correlation between diagnosis, treatment protocols, and long-term outcomes. The result is evidence that fails to meet clinical utility standards, limiting its role in advancing precision care, improving health equity, or accelerating innovation. What Makes M.O.T.I.V.™ Different The M.O.T.I.V.™ (Musculoskeletal Outcomes Through Informed Validation) program is designed to overcome these barriers. Each dataset in the library is: • Peer-Reviewed by OREF: Every observational protocol is vetted by leading MSK researchers. • Anatomically & Clinically Specific: Defined by one anatomical region, one pathology, one treatment protocol, and one standardized outcomes assessment. • Validatable to Primary Sources: Built for transparency and trust, avoiding unverifiable big-data manipulation. • Longitudinal & Fit-for-Purpose: Meeting FDA requirements for clinical and regulatory use. • Statistically and Clinically Relevant: Designed to support meaningful insights that improve patient care. By setting a new standard for structured, verifiable real-world data, M.O.T.I.V.™ ensures that research and innovation in orthopaedics are both clinically rigorous and widely accessible. Driving Value-Based Care and Innovation The launch of M.O.T.I.V.™ marks a critical step toward achieving value-based care in musculoskeletal medicine. By creating datasets that are trusted, transparent, and clinically actionable, RegenMed and OREF are empowering: • Providers to benchmark treatments and improve patient outcomes. • Insurers and payers to make evidence-based reimbursement decisions. • Researchers to accelerate the path from lab to bedside. • Patients to benefit from more equitable, data-driven care. This initiative positions RegenMed and OREF at the forefront of a healthcare transformation — one where real-world evidence is no longer an aspiration, but a validated, practical tool for medical advancement

Life sciences companies spend vast sums on real-world evidence, often relying on massive secondary datasets sold by big data aggregators. While these resources are convenient, familiar, and easy to license, they were never designed to address the nuanced questions that matter most to regulators, payers, and clinicians. The industry now faces an inflection point: the need for high-quality, longitudinal, verifiable evidence that is both strategically relevant and future-proof.The Problem With Big Data RWETraditional big data RWE draws primarily from electronic medical records (EMRs) and insurance claims. These datasets capture billing events and limited treatment codes, but they lack longitudinal outcomes, omit critical variables, and often arrive stale. Many areas of great importance — rare diseases, advanced therapies, and complementary interventions — are invisible in these datasets. Moreover, questions of ownership and legality loom large, with mounting costs and growing scrutiny around unauthorized data use. For artificial intelligence (AI) applications, which increasingly shape pharma’s strategy, these foundations are fragile and risky.Circles: A Better Way ForwardRegenMed’s Circles represent a new paradigm. Built on rigorously defined Observational Protocols (OPs), analogous to randomized controlled trial protocols, Circles generate datasets that are:‍Clinically Relevant: Tailored to specific therapeutic questions and aligned with HEOR, medical affairs, and regulatory needs. ‍Verifiable and Auditable: Every data point is timestamped, source-verified, and traceable. ‍Federated and Equitable: Data remains under local ownership, ensuring broader participation and inclusion of underrepresented populations. ‍Living and Continuously Growing: Unlike static datasets that depreciate, Circles appreciate over time as new cases and longitudinal outcomes are added. ‍AI-Ready: Standardized and proprietary, Circles provide a defensible foundation for training machine learning models without legal ambiguity. Benefits Across The Life CycleCircles provide critical advantages across the product lifecycle: Health Economics and Outcomes Research (HEOR): Strengthened cost-effectiveness models and payer negotiations through longitudinal, auditable outcomes data. Medical Affairs: Enhanced engagement with KOLs, support for outcomes-based contracts, and scientifically credible evidence generation. Post-Market Surveillance: High-quality longitudinal monitoring suitable for regulatory submissions and compliance with safety commitments. Rare Disease Research: Aggregation of small patient cohorts across institutions into statistically meaningful studies. Innovation Enablement: High-quality training datasets for AI, validation for digital health tools, and fuel for translational research. Future Proofing: Alignment with evolving regulatory standards, payer expectations, and AI-driven healthcare ecosystems. Comparative AdvantageWhile big data RWE will not vanish, Circles decisively outperform it in areas where big data structurally fails: rare diseases, post-market safety, outcomes-based reimbursement, and AI model training. Circles datasets are fresher, more complete, and strategically aligned with regulatory and payer needs. Unlike static licensed datasets, they grow in value, establishing a sustainable evidence ecosystem.Practical ImplementationCircles are not disruptive to adopt. They fit into existing budget categories (post-marketing, Phase IV, HEOR, rare disease) and can typically be launched in 4–6 weeks at costs lower than a single-year dataset license. Federated architecture ensures compliance and scalability across geographies, while AI-assisted coding workflows streamline data harmonization.ConclusionThe limitations of big data RWE are becoming more apparent as healthcare shifts toward value-based care, AI-driven analytics, and patient-centered outcomes. Circles are not merely a tactical supplement but a strategic imperative. By delivering clinically relevant, auditable, and continuously growing datasets, they provide life sciences companies with a credible, cost-efficient, and future-proof foundation for evidence generation. Early adopters will not only reap immediate advantages but also lead the industry into the next era of real-world evidence.Contact us to learn more.

Pediatric musculoskeletal (MSK) conditions span everyday sprains and overuse injuries to scoliosis, juvenile idiopathic arthritis (JIA), congenital deformities, infections, tumors, and rare neuromuscular disorders. What unites them is the need for decisions that respect growth and development, where early recognition, longitudinal follow‑up, and patient‑family experience matter as much as imaging and lab values. Yet the evidence gaps are real: randomized controlled trials (RCTs) in children are often constrained by ethics, logistics, and small cohorts. The result is uncertainty about long‑term outcomes, conservative (non‑drug) care, and the total burden borne by families.Why The Gaps Persist‍Diagnostic complexity: Children present differently from adults, may not verbalize pain, and “red flags” are subtle. Limb or joint pain can even herald systemic disease. Infections like osteomyelitis may not appear on X‑rays for one to two weeks, and septic arthritis carries a narrow window to prevent permanent damage—where “time is joint.”Developmental variability: Conditions appear at different ages and evolve over growth. Examples include scoliosis (≈3% of adolescents), clubfoot and developmental dysplasia of the hip (~1 in 1,000 births each), and slipped capital femoral epiphysis (~0.5 in 1,000 early adolescents). Care pathways must adapt to growth spurts and changing biomechanics.‍Long trajectories and lived burden: Many pediatric MSK conditions unfold over years. Pharmacologic options for JIA, for instance, have substantial costs and require sustained monitoring; bracing and physical therapy demand adherence that’s hard to measure outside research settings. Families shoulder indirect costs (missed work, travel, lodging) alongside clinical challenges.Traditional research limits: RCTs remain vital for efficacy questions but under‑represent children and rarely capture multi‑year real‑world outcomes or quality‑of‑life effects.Why Real‑World Evidence (RWE) NowReal‑world data (RWD) from electronic health records, registries, claims, digital health, and patient‑generated sources can fill gaps by observing diverse children over time in routine care. With 99% of U.S. hospitals and about 90% of office‑based clinicians using EHRs, the substrate exists to answer pediatric questions faster, at lower cost, and at scale. RWE complements — not replaces — RCTs by showing effectiveness, safety, and adherence in heterogeneous settings and by surfacing outcomes that matter to families (function, return‑to‑play, school participation). Methodological rigor still matters: data quality, missingness, confounding, and selection bias must be addressed to make findings decision‑grade.What Makes Circles DifferentCircles is RegenMed’s structured, clinician‑efficient approach to producing validatable RWE. Each Circle starts with a prospectively designed Observational Protocol (OP) focused on a concrete clinical objective for a well‑defined cohort and anatomy/pathology. Data capture flows through the physician‑facing inCytes™ and the patient‑facing Benchmarc™ modules, minimizing administrative burden while elevating patient (and caregiver) engagement. Three elements stand out:‍Closed‑system, high‑fidelity datasets: Circles integrate diagnosis and treatment data with well‑correlated long‑term outcomes, producing datasets that are controlled, unambiguously owned, and free of artifacts. This is crucial when rare cohorts and small N’s can otherwise magnify noise.Built‑in collaboration: Pediatric orthopedics often needs multi‑site aggregation to reach statistical power. Circles are designed for cross‑institutional — and even cross‑national —i nvestigator networks, enabling representative cohorts for rare or complex conditions.A continuous improvement loop: OP → collaborative data generation → ongoing analysis/learning → refined standards of care. Validatable RWE becomes a practical tool for clinical decision support, education, compliance, and funding — linking evidence to everyday practice and sustainability.Where Circles Moves The NeedleEarlier, more accurate diagnoses: By correlating granular histories, exams, imaging, labs, and outcomes across large cohorts, Circles help surface atypical presentations (e.g., leukemia presenting as limb pain) and time‑critical signals (e.g., septic arthritis) that are often missed in fragmented records. Longitudinal clarity for long‑horizon conditions: Multi‑year capture supports conditions whose trajectories span growth, such as scoliosis, JIA, and dystrophies —illuminating real‑world effectiveness and safety of bracing, physical therapy, and biologics.Evidence for interventions where RCTs are impractical: For pediatric devices and off‑label or compassionate uses, Circles generate the quality of RWE needed for label expansions, post‑market surveillance, and pediatric‑specific guidance.‍Non‑pharmacologic care, finally quantified: Circles track adherence (e.g., bracing hours, physical therapy frequency/intensity) and relate it to objective function (range of motion, return‑to‑play) and patient‑reported outcomes, strengthening the case for conservative care and reimbursement.‍Value‑based care and health economics: Benchmarc™ captures the lived experience — pain scales, CHAQ/PedsQL, and caregiver costs (missed work, travel, lodging) — to quantify true “cost of illness” and inform smarter payment models.Regulatory‑grade insight: With structured capture and multi‑site cohorts, Circles’ datasets align with how regulators increasingly use RWE to support new indications, pediatric populations, dosing refinements, and post‑approval requirements.Illustrative Pediatric MSK Use CasesScoliosis: Compare bracing adherence and curve progression with functional and respiratory outcomes; define timing and thresholds for bracing vs. early surgical options (e.g., fusion, tethering). ‍JIA: Track real‑world adherence, safety, and durability of biologics over years; relate disease activity control to school participation and caregiver burden; refine treat‑to‑target protocols. Congenital deformities & rare neuromuscular disease: Aggregate multi‑site cohorts to characterize natural history, assess emerging therapies, and set pragmatic standards of care when single‑center RCTs are infeasible.Sports and overuse injuries: Quantify prevention and rehab protocols, identify risk factors (load, environment, nutrition), and define safer, data‑driven return‑to‑sport criteria.(See the summaries in Table 1 on pediatric MSK challenges, Table 2 on RWE advantages/limitations, and Table 3 on Circles use cases.)What Good Looks Like: From Data To DecisionsThe goal is not more data but better decisions. Circles connects high‑quality, longitudinal datasets to the work clinicians, families, payers, and regulators must do:For clinicians: earlier recognition; clearer care pathways; fewer avoidable surgeries; better rehab; practical decision support at the point of care.For families: visibility into outcomes that matter; reduced time and money burdens; shared‑decision‑making grounded in evidence.For payers and health systems: credible HEOR to pay for what works; reduced waste from non‑adherent or poorly sequenced care.For innovators and regulators: validated post‑market surveillance and faster pediatric label evolution without compromising safety.Pediatric MSK care needs multi‑stakeholder collaboration, standardized data models and interoperability, integration of RWE into point‑of‑care tools, regulatory pathways tuned for children, and authentic patient‑family engagement. Circles was built for that agenda. By turning routine care into validatable evidence — and doing it with minimal burden — Circles helps close the pediatric evidence gap and accelerates better outcomes for kids.Contact us to learn more.

Kinematic Alignment (KA) in Total Knee Arthroplasty (TKA) has emerged as a patient-specific alternative to the long-standing Mechanical Alignment (MA) approach. While KA aims to restore the native, pre-arthritic joint lines and rotational axes of each patient’s knee — promising a more natural-feeling joint and quicker recovery — its adoption has been slow. The hesitation stems largely from limited high-quality, long-term randomized controlled trial (RCT) data, conflicting clinical evidence, and questions around real-world applicability.Meta-analyses of existing RCTs often find no clinically important difference in patient-reported outcomes between KA and MA, and many of these trials carry moderate-to-high risks of bias with average follow-ups of just 24 months — far short of the 15–20 years implants are expected to last. This has left unanswered critical questions about implant longevity, revision rates, complication profiles, and the impact of surgeon experience on outcomes.The Circles platform offers a breakthrough solution to this evidence gap by generating high-quality, verifiable, and complete Real-World Evidence (RWE). Unlike traditional ‘big data’ sources — which often contain gaps, unverifiable origins, and inconsistent clinical context — Circles collect time-stamped, unmanipulated data directly from physicians, patients, and laboratories, all within closed Observational Protocols (OPs). These protocols adhere to Good Clinical Practice (GCP) standards and are designed to capture data across the full patient journey, from enrollment to long-term outcomes.For KA in TKA, Circles can design OPs to address the most pressing evidence gaps:Longitudinal comparative effectiveness of KA vs. MA, capturing nuanced patient satisfaction metrics like the Forgotten Joint Score (FJS) alongside implant survivorship data extending beyond a decade.Impact of enabling technologies, such as robotics and compartmental pressure sensors, on surgical precision, learning curves, complication rates, and functional recovery.Correlation of objective kinematics (e.g., gait analysis, wearable sensors) with subjective outcomes, helping bridge the disconnect between biomechanical metrics and patient perception.Real-world complication tracking, including ligament releases, recuts, and intra-operative injuries, validating or refining KA’s “ligament sparing” claims.By integrating advanced data streams — preoperative imaging, intraoperative precision metrics, postoperative kinematics — Circles can effectively create a ‘digital twin’ for each knee replacement. This allows unprecedented insight into how surgical precision translates to real-world function and satisfaction.The platform’s transparent methodology also supports best-practice development by analyzing performance variability across surgeons, institutions, and patient demographics. This is particularly important for KA, where surgeon experience and patient selection may significantly influence results.In a field where the stakes include decades of patient mobility and quality of life, Circles provide an agile, scalable, and scientifically rigorous way to build the evidence base that KA needs. By closing critical data gaps, they can accelerate responsible adoption, refine surgical training, and ultimately improve outcomes for TKA patients worldwide.

Total Knee Arthroplasty (TKA) is a life-changing intervention for patients with end-stage knee osteoarthritis. With over 600,000 procedures performed annually in the U.S. — and projected to rise nearly sevenfold by 2030—its impact is profound. Yet, despite the success of conventional techniques, up to a quarter of patients remain dissatisfied, often due to the inability to fully replicate the natural motion of the knee.The emergence of Robotic-Assisted TKA (RA-TKA) promises unprecedented precision in implant placement and alignment, with the aim of improving patient satisfaction and long-term implant survivorship. But the technology’s adoption is slowed by unresolved debates, inconsistent outcomes data, and the high financial burden of robotic systems. The orthopedic community continues to grapple with critical questions:Does RA-TKA truly deliver superior patient-perceived benefits in the long term?Is kinematic alignment (KA) better than mechanical alignment (MA)?Can the costs and learning curve be justified by measurable improvements in outcomes?Randomized Controlled Trials (RCTs) are the gold standard for evaluating surgical interventions, but they have limitations in real-world applicability due to controlled environments and narrow patient selection. Real-World Evidence (RWE) fills this gap, capturing data from routine clinical practice.However, traditional “big data” RWE sources — derived from EMRs, registries, and insurance claims — are often incomplete, unverifiable, and lack the longitudinal follow-up needed for meaningful conclusions. These shortcomings hinder the ability to answer pressing RA-TKA questions, particularly around alignment strategies, implant longevity, and cost-effectiveness.RegenMed’s Circles platform is engineered to address these evidence gaps with a methodology that mirrors the rigor of clinical trials while maintaining the practicality of real-world data collection.Key Features:Direct, Verifiable Data Sourcing: Information comes straight from physicians, patients, and laboratories, each datapoint time-stamped and immutable.Longitudinal Case Tracking: Every dataset follows a complete patient journey, from enrollment through long-term outcomes, capturing critical measures like implant survivorship and patient satisfaction.Driven Relevance: Each dataset is tied to a specific Observational Protocol (OP) for targeted, clinically meaningful insights—such as robotic KA vs. MA comparisons.Good Clinical Practice (GCP) Compliance: The platform inherently supports ethical and regulatory standards, including IRB processes and patient consent.Equitable Ownership and Benefits: Circle members retain data ownership and can receive up to 85% of licensing value, incentivizing participation.Solving RA-TKA’s Evidence Challenges:Quality and Verifiability: Circles eliminates the ambiguity of traditional datasets, enabling robust comparative studies of alignment strategies and robotic platforms.Long-Term Outcomes: Continuous data capture supports proactive monitoring of implant performance, complications, and evolving patient satisfaction.Cost-Effectiveness: Granular, structured data allows detailed economic analyses, critical in evaluating the true return on investment of robotic systems.Surgeon Learning Curve: Performance tracking over time can inform training programs and credentialing standards.Patient-Reported Outcomes (PROMs): High-fidelity capture ensures the patient’s voice is central in assessing surgical success.Robotic-assisted knee replacement holds tremendous promise, but the orthopedic field needs better answers before widespread adoption. Circles provides the framework to generate those answers — efficiently, cost-effectively, and with clinical rigor. By combining the precision of RA-TKA with the precision of high-quality RWE, Circles can guide technology adoption, resolve long-standing debates, and improve patient outcomes at scale.