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OverviewWhen physicians collect HIPAA-compliant real-world clinical data during the course of regular medical care to evaluate and improve established treatment protocols — without introducing novel interventions or intending to produce generalizable scientific knowledge — IRB review is not legally or ethically required. This is consistent with both the Common Rule and HIPAA, as well as best practices across academic and clinical institutions.IRB review is not necessary for the HIPAA-compliant collection of real-world clinical data by physicians acting within the scope of their regular practice of medicine, when the primary aim is quality improvement of established treatment protocols.Applicable Definition of “Research”Research v. Quality ImprovementFederal regulations and ethical frameworks governing human subjects research distinguish between activities that qualify as “research” and those that are considered Quality Improvement (QI). Physicians collecting real-world data in the course of standard patient care for the purpose of evaluating or improving existing treatment protocols — without introducing untested interventions, without a plan to contribute to generalizable knowledge, and in compliance with HIPAA — do not meet the regulatory definition of “human subjects research” requiring Institutional Review Board (IRB) review. This conclusion is supported by guidance from the Office for Human Research Protections (OHRP), HHS regulations, and detailed analysis of the Common Rule and HIPAA regulations.Under 45 CFR 46.102(l), "research" is defined as a systematic investigation designed to develop or contribute to generalizable knowledge. If an activity does not meet both components of this definition, it is not considered research and therefore not subject to IRB review.QI activities typically aim to assess or improve internal processes or outcomes and do not intend to generalize findings outside the institution or clinical setting in which they are conducted. The HHS explicitly states that activities conducted to enhance patient care, collect data for administrative purposes, or evaluate provider performance generally do not meet the definition of research and thus do not require IRB review.Even if results are published, QI initiatives do not become “research” unless their design and intent were to develop generalizable knowledge: “The mere intent to publish an account of a QI project does not automatically classify it as research.” (OHRP Guidance.)The HIPAA Privacy Rule (45 CFR 164.501) expressly permits the use and disclosure of Protected Health Information (PHI) without patient authorization when used for health care operations, which include quality assessment and improvement activities, case management, and outcomes evaluation.When physicians collect real-world clinical data solely to improve existing treatment protocols for their own practice, this constitutes a health care operation, not research. If the data are de-identified or handled in accordance with HIPAA’s limited data set provisions, and used within the scope of operations, this does not trigger research regulations.Even if a QI project is borderline, federal guidance offers exemptions under the Common Rule. Category 4 exemption covers secondary research using identifiable private information or biospecimens, when such use is regulated under HIPAA for healthcare operations. No IRB review is needed if the physician is not intervening beyond routine care, not randomizing patients, and is using existing data for internal analysis.What Is Exempt ResearchEven for exempt research formal IRB approval is not required, only a determination that the activity qualifies as exempt. According to the Common Rule, research activities posing no more than “minimal risk” may qualify for expedited or exempt IRB review. Real-world data collected during routine care inherently involves no added risk, as it arises from normal clinical operations.The physician's collection of data without altering standard care, and without interacting with patients for research-specific purposes, reinforces that the activity remains non-research. “QI/QA activities… collecting data solely for clinical, practical, or administrative purposes… do not meet the definition of ‘research’.” — HHS/OHRP.The U.S. Department of Health and Human Services (HHS) and the Office for Human Research Protections (OHRP) provide that QI initiatives do not require IRB review unless they introduce untested clinical interventions intended to produce scientific evidence.The University of Southern California, Boston University, and the Minnesota Department of Health affirm that internal QI efforts carried out as part of normal practice without intent to generalize do not fall under IRB oversight. 1ConclusionCircles represent clinically and financially valuable datasets relevant to licensees for a broad variety of uses. When they are licensed to conduct or support research, it will be the responsibility of the licensee to seek IRB approval when appropriate.In addition, Sponsors are able to establish “Private Circles” where IRB approval may be appropriate. It then is the Sponsor’s responsibility to organize such approvals. RegenMed regularly assists in coordinating with the IRB in such instances. Circles typically involve real-world data collection from multiple sites, making commercial IRBs a practical option when IRB involvement is appropriate. This will also comply with approaches taken by most academic medical centers for multi-site trials. See the SMART IRB Network.

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While the $60 billion healthcare data analytics industry expands rapidly, ASCs are failing to exploit their most valuable asset while others do so. This RegenMed video explains how any ASC can generate new revenue streams and asset value by capturing and monetizing real-world evidence (RWE) from routine patient care. Key sections of the video, with time codes, are:The Opportunity [0:18 – 1:14]Real-World Evidence — structured datasets generated from routine patient care — is in high demand across industry and research. The $60 billion healthcare data analytics industry is growing at a rate of over 20% annually. ASCs are not only uniquely positioned to contribute to this market, but to benefit from it financially. RWE licensors include product manufacturers, insurers, research centers, and AI models. Use cases for licenses include competitive intelligence, regulatory submissions, product innovation, legal compliance, value based care, social determinants of health, and health equity. ASCs Can Participate [1:15 – 2:26]ASCs already regularly enter EMR and other real-world data, and pay to do so. However, that data is being aggregated, manipulated, and licensed by others. However, and despite their high cost, RWE datasets synthesizing EMRs, prescription, insurance claim and other disparate sources are incomplete, unverifiable and generally clinically irrelevant. Using RegenMed’s patented Circles technology and processes, ASCs can now structure, aggregate, own and monetize that RWD themselves — without burdening staff or altering clinical workflows. Primary and well correlated datasets which are verifiably sourced from daily patient care – against a prospectively designed Observational Protocol – are far more clinically relevant and “fit-for-purpose” than current RWE offerings. They are therefore far more valuable.What Do Circles Datasets Look Like [2:27–3:40]This section shows two separate illustrative reports. One compares four different knee implant products over twelve months against KOOS Jr. scores in the context of total knee arthroplasty. The second compares two separate surgical techniques over 27 months against a VAS score in the context of total hip arthroplasty.Although these are orthopedics examples, Circles can be and are used to generate clinically and statistically significant datasets for any anatomical region, pathology, treatment protocol, and standardized or custom outcomes score – regardless of medical specialty. EBITDA and New Asset Potential For An Average ASC [3:40 – 6:04]Based on the stated assumptions, which of course will depend on the specific circumstances of each ASC, five Circles datasets generated by ten physicians can produce new annual EBITDA of $850,000 for a total cost of only $31,000 — a return on investment of 14x. Moreover, these five 6-month longitudinal Circles datasets represent asset value of $2.6 million on the stated assumptions. Finally, an ASC can continue to increase these income streams and asset value through continued outcomes capture, additional Observational Protocols relevant to its practice mix, and combining internal and external Circles datasets.ConclusionTo discuss your specific situation, please contact us. RegenMed is not a mere solutions vendor. It works with each Client as a long-term partner to develop meaningful clinical and financial returns on investment.Contact usRegenMed | www.rgnmed.comcircles@rgnmed.com

The New Role of Real-World Evidence (RWE)Real-World Evidence (RWE) has emerged as a cornerstone of modern healthcare innovation, regulatory science, and payer strategy. Once considered supplementary, RWE now informs pivotal regulatory decisions, market access strategies, and post-market evaluations. The U.S. Food and Drug Administration (FDA), through the 21st Century Cures Act and subsequent guidance, has formally embraced RWE — defined as clinical evidence derived from real-world data (RWD) such as electronic health records (EHRs), insurance claims, and patient-generated information.RWE’s legitimacy has been reinforced by landmark regulatory approvals, including Medtronic’s CRT-D device and label expansions for drugs like Ibrance and Tecentriq, all supported by high-quality RWD. Industry and payers alike are accelerating investment in RWE strategies. A 2023 Deloitte survey revealed that 92% of life sciences firms have RWE strategies, with two-thirds planning increased investments. Likewise, payers such as UnitedHealthcare and Aetna are leveraging RWE to drive value-based care.The Imperative for Primary, Validated RWDDespite the growing utility of retrospective datasets, the RegenMed’s recent White Paper argues for the superiority of purpose-collected, prospective RWD. These data sources — gathered directly from clinicians and patients — offer several compelling advantages over administrative or passively collected data:Fit-for-Purpose DesignEMR and claims data, collected primarily for billing, often lack the precision and completeness required for rigorous research. Purpose-built datasets enable the use of validated instruments, structured formats, and contextually relevant clinical endpoints.Clinical NuanceStructured physician reporting captures nuanced clinical insights, including treatment rationale, staging, and intent — data points typically obscured in EMRs.Patient-Reported OutcomesProspective data collection engages patients directly, capturing metrics like pain, functionality, and satisfaction — offering a fuller picture of treatment efficacy than retrospective proxies.Agility and TimelinessReal-time data collection enables swift adaptation to emergent clinical trends, whereas retrospective data sources lag by months or years.Data IntegrityPurpose-collected data allows immediate validation at the point of entry. Structured electronic case report forms (eCRFs) and integrated logic checks dramatically reduce error rates.Regulatory CredibilityHigh internal validity and transparent provenance make prospectively collected RWD more persuasive in regulatory and payer contexts.Circles: RegenMed’s Response to the RWE ChallengeTo meet the demand for validated RWD, RegenMed regularly publishes and executes “Circles” — an ecosystem of statistically robust, domain-specific datasets focused on discrete pathologies, anatomical regions, and standardized outcomes. These datasets are:Built from physician-generated, real-world clinical data.Structured to answer specific scientific or clinical questions.Fully de-identified and HIPAA/GDPR compliant.Designed for flexibility in observational protocol (OP) development.Circles empower clinicians to contribute cases while sharing in dataset monetization, aligning incentives and driving long-term data growth. Importantly, Circles data typically do not require IRB review, as they are gathered during routine care for quality improvement purposes rather than interventional experimentation.Real-World Application: TKA and THA DatasetsThe White Paper includes illustrative Circles reports on Total Knee Arthroplasty (TKA) and Total Hip Arthroplasty (THA), demonstrating Circles’ potential for granular, verifiable insights. These reports exemplify how customized observational protocols, rich clinical detail, and long-term patient-reported outcomes converge to support practice-changing analytics and licensing opportunities.ConclusionValidated real-world data, collected directly from clinicians and patients with rigor and intent, is no longer optional — it is essential. As regulatory bodies, payers, and innovators continue to demand more actionable, credible, and agile evidence, RegenMed’s Circles platform positions itself as a next-generation solution. By aligning clinical insight, scientific design, and economic incentives, Circles transforms real-world data into real-world impact.

In today’s rapidly evolving healthcare landscape, the integration of data analytics has become crucial for delivering high-quality patient care. Leveraging patient data effectively enables clinicians to make informed decisions, personalize treatment plans, and ultimately improve clinical outcomes. Here’s how healthcare providers can harness the power of data-driven decision-making:Collect Comprehensive and Accurate DataStart by gathering extensive patient information—demographics, medical history, lab results, imaging, medication records, and lifestyle factors. Ensuring data accuracy and completeness lays a strong foundation for meaningful analysis.Implement Advanced Data Analytics ToolsUtilize modern analytics platforms, machine learning algorithms, and AI-powered tools to identify patterns, predict risks, and uncover insights that might not be apparent through traditional methods. These tools can help flag high-risk patients and suggest tailored interventions.Integrate Data Across SystemsCreate a unified data environment by integrating Electronic Health Records (EHR), wearable devices, pharmacy databases, and other sources. A holistic view of patient information enhances decision-making accuracy and reduces information silos.Personalize Treatment PlansUse data insights to customize therapies based on individual patient profiles. Personalized medicine increases the likelihood of treatment success and improves patient adherence.Monitor Outcomes and Adjust AccordinglyContinuously track patient progress and clinical outcomes. Data-driven feedback loops enable clinicians to modify treatment strategies in real-time, ensuring optimal results.Prioritize Data Security and PrivacyEnsure compliance with healthcare regulations like HIPAA and GDPR. Protecting patient privacy builds trust and encourages more comprehensive data sharing.Foster a Data-Driven CultureTrain healthcare teams on data literacy and analytical tools. Cultivating a culture that values data-driven insights encourages proactive decision-making and continuous improvement.ConclusionHarnessing patient data effectively transforms healthcare from reactive to proactive. By making informed, data-driven decisions, clinicians can enhance diagnostic accuracy, optimize treatment plans, reduce adverse events, and ultimately achieve better clinical outcomes for their patients. Embracing data analytics isn’t just an option — it’s a necessity for the future of healthcare excellence.