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OMNY Health’s Real-World Data is Redefining Clinical Trial Control Arms

The pharmaceutical industry is rapidly embracing real-world evidence (RWE) to accelerate drug development and regulatory approvals. External control arms (ECAs), built using real-world data (RWD), are transforming clinical trials by offering an alternative to traditional placebo groups. By reducing recruitment burdens, increasing statistical power, and aligning with regulatory expectations, ECAs have the potential to redefine late-stage drug development. 

OMNY Health is at the forefront of this shift, providing research-ready EHR datasets that seamlessly integrate into clinical trial designs. Our recent study demonstrates how EHR-derived control arms can effectively mirror traditional placebo groups, offering a robust and scalable solution for regulatory submissions. 

Building an External Control Arm with OMNY Health’s RWD 

To evaluate the feasibility of an ECA in a late-stage clinical trial, OMNY Health leveraged six specialty dermatology networks and six integrated delivery networks (2017-2024) from its real-world data platform. The study constructed an ECA for the Phase 3 POETYK PSO-1 trial, which evaluated deucravacitinib versus placebo for moderate-to-severe plaque psoriasis.

The ECA was built using precise patient selection criteria that ensured alignment with the trial’s placebo group. Patients were included if they met physician global assessment (PGA) score eligibility, ensuring comparable disease severity. Baseline treatment history was carefully controlled, and topical medication use was restricted prior to the index severity visit to reflect treatment-naïve status. By tracking patients longitudinally, we assessed week-16 outcomes using structured EHR data, mirroring the trial’s methodology. 

While some demographic differences were observed—ECA patients were older, more likely to be female, and had a different racial distribution compared to the placebo arm—the disease severity and baseline clinical characteristics were well matched. 

Key Findings: OMNY Health’s ECA vs. Trial Placebo Arm

The real-world ECA demonstrated a significantly higher response rate compared to the traditional placebo arm, reinforcing its validity as a comparator. 

  • 18.5% of ECA patients achieved PGA 0/1 (clear or almost clear skin), versus only 7.2% in the placebo arm. 
  • Despite differences in age, gender, and race, disease burden and severity scores aligned closely between the ECA and placebo group. 
  • Stratification by prior biologic use, systemic therapy history, and weight showed no notable impact on outcomes. 

One notable finding was racial disparities in achieving the primary endpoint within the ECA, suggesting that differences in patient demographics between the ECA and placebo group may have contributed to the observed differences in response rates. Further research could explore adjustments such as population weighting to refine ECA comparability even further. 

These results confirm that OMNY Health’s real-world dataset can be used to generate ECAs that replicate clinical trial placebo groups, while also revealing new insights into patient diversity, treatment history, and long-term outcomes. 

Why OMNY Health’s Data is a Game-Changer for External Control Arms 

Traditional clinical trials face high recruitment costs, ethical concerns over placebo use, and long enrollment timelines. OMNY Health helps to eliminate these barriers by offering regulatory-grade EHR data that aligns with clinical trial endpoints. 

With 85M+ patients, 1B+ encounters, and 4B+ unstructured clinical notes, our dataset provides a scalable and statistically powerful alternative to traditional control groups. By incorporating structured disease severity scores, prescribing patterns, and physician-reported outcomes, our ECAs offer more efficient and cost-effective alternative to prospectively collected placebo data.

Beyond reducing recruitment time, OMNY Health’s real-world ECAs improve trial generalizability, capturing diverse patient populations often underrepresented in traditional studies. As the FDA increasingly endorses RWD for regulatory decision-making, the ability to integrate ECA’s into pivotal trials is becoming a competitive advantage for pharmaceutical companies.

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From Symptom to Strategy: What Itch Intensity Data Tells Us About Pruritus Management

Pruritus, or chronic itch, is one of the most common symptoms reported in dermatology, yet real-world treatment patterns remain poorly characterized. While topical therapies are typically the first-line approach, severe cases often require systemic interventions. But how do clinicians determine when to escalate therapy? OMNY Health conducted a real-world evidence study leveraging research-ready EHR data from six specialty dermatology networks to examine how itch intensity, a critical but underutilized measure, influences real-world prescribing patterns. By integrating structured and unstructured clinical data, OMNY provides new insights into the relationship between symptom severity and treatment decisions.

Quantifying Pruritus Severity in Real-World Data 

While pruritus is a symptom, its severity can significantly impact quality of life and treatment choices. This study focused on patients with a documented 10-point itch intensity score, captured through physician assessments and patient-reported outcomes. The dataset included 7,330 patients with 8,115 encounters, capturing the following structured severity measures:

  • Itch intensity scores (0-10 scale) recorded alongside pruritus-related encounters. 
  • Pruritus-related prescriptions categorized into topical and systemic treatments. 
  • Demographic characteristics, including age, gender, and race, to understand patient stratification. 
  • Treatment patterns by disease severity, assessing whether increasing itch intensity influenced therapy selection. 

By leveraging EHR-derived severity assessments, the study provides a more granular understanding of how treatment decisions align with symptom burden.

How Itch Intensity Drives Treatment Decisions

Findings revealed a clear relationship between itch severity and systemic therapy use, while topical therapies were prescribed at consistent rates across all levels of severity.

  • Topical corticosteroids were the most prescribed treatment, used in nearly half of all pruritus-related visits, regardless of itch intensity. 
  • Topical calcineurin inhibitors were prescribed far less frequently, at around 10% of cases, with minimal use of alternative topical therapies. 
  • Systemic therapy prescriptions increased as itch severity worsened, with sedative antihistamines emerging as the most commonly used option. 
  • Prescription rates for sedative antihistamines climbed from 13% in mild cases to 26% in severe cases, while other systemic treatments—including non-sedative antihistamines, systemic doxepin, SSRIs, and opioid receptor antagonists—were prescribed far less frequently. 

The increasing use of sedative antihistamines in severe cases suggests a reliance on limited systemic options, leaving a gap in alternative therapies for patients with refractory pruritus.

Why Itch Intensity Matters for Clinical and Research Applications

The underutilization of structured itch severity scores in real-world studies has historically limited the ability to quantify treatment impact. Our findings reinforce why itch intensity should be a standard measure in both clinical decision-making and drug development. For clinicians, understanding real-world prescribing trends tied to symptom severity can optimize treatment pathways and inform escalation decisions. For researchers and trial sponsors, itch intensity scores provide an opportunity to refine patient segmentation, support cohort identification, and assess real-world treatment responses. For drug developers, the limited use of alternative systemic therapies highlights a need for novel treatments, particularly for patients with refractory pruritus.

Advancing Dermatology Research with Real-World Data

This EHR-based study provides new insights into how structured itch intensity measures correlate with real-world prescribing behavior. By integrating structured severity assessments with prescribing data, OMNY Health’s research-ready datasets offer a unique lens into treatment trends, patient stratification, and disease burden.

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Advancing SDoH research with unstructured clinical notes to improve patient care

Every patient’s story extends beyond structured medical records. Social determinants of health (SDoH), such as economic insecurity, often go unrecorded in traditional coding systems, leaving critical gaps in understanding patient needs. A recent study utilizing OMNY Health’s real-world data platform showcases the power of unstructured clinical notes in identifying financial hardship among psoriasis patients—insights that structured EHR data alone did not capture. 

Leveraging NLP to Extract Real-World Patient Challenges 

The OMNY Health Platform was used to access electronic health record (EHR) data for patients with International Classification of Diseases, Tenth Revision (ICD-10) codes related to economic insecurity. These codes, outlined in Table 1, include classifications for financial instability, lack of adequate food and safe drinking water, extreme poverty, low income, and material hardship. However, structured data alone failed to capture the full scope of patient struggles. 

By applying natural language processing (NLP) to unstructured clinical notes from five specialty dermatology networks (2017-2019), researchers uncovered 686 patients with financial hardship indicators that would have otherwise gone undetected. These were patients whose struggles—such as insurance challenges, difficulty affording medications, and financial stress impacting care decisions—were only documented in free-text notes, never coded in structured fields. 

At a probability threshold of 0.91, the model achieved a 91% precision rate, though manual review found that 60% of flagged sentences were true positives. This highlights the need for continued refinement while demonstrating how clinical notes provide deeper, more patient-centric insights than structured data alone. 

What Structured Data Misses: A Holistic Approach to SDoH 

Traditional structured EHR data often fails to capture the full scope of social determinants of health (SDoH), creating critical blind spots in patient care. Many healthcare decisions—such as delaying treatments, switching medications, or discontinuing care altogether—are influenced by social and financial challenges that remain undocumented in structured records. 

For example, economic insecurity is a major yet often invisible factor shaping healthcare journeys. By analyzing clinical notes, researchers have identified patients facing financial hardship that was not reflected in coded data, underscoring the need for a more comprehensive approach to SDoH research. But financial instability is just one piece of a much larger puzzle.  

OMNY Health’s dataset goes beyond economic hardship, capturing a wide range of social determinants that impact health outcomes, including:   

  • Housing Instability – Identifying patients experiencing homelessness or frequent relocations that may affect continuity of care. 
  • Food Insecurity – Detecting concerns related to nutritional deficiencies and limited access to healthy food.  
  • Transportation Barriers – Recognizing challenges patients face in accessing healthcare facilities. 
  • Education and Health Literacy – Understanding how limited education levels influence patient adherence and treatment outcomes. 
  • Social Support and Caregiver Burdens – Capturing notes related to lack of family or community support, impacting long-term disease management. 

By incorporating unstructured clinical notes into SDoH research, OMNY Health’s data helps: 

  • Identify at-risk patients who may otherwise be invisible in structured records. 
  • Support more effective intervention strategies tailored to individual social challenges.   
  • Enhance real-world evidence (RWE) generation to guide healthcare policy and decision-making. 

As healthcare organizations seek to advance health equity, leveraging unstructured data provides a more complete view of patient experiences—ensuring that social determinants are not just acknowledged, but actively addressed in care strategies and policy development. 

Expanding the Impact of Unstructured Data  

This study is just the beginning. Researchers are expanding NLP-driven approaches to explore other SDoH domains, including housing instability and undereducation. With continued model refinement, these insights will help healthcare providers, researchers, and policymakers gain a more complete understanding of patient experiences beyond structured EHR limitations. 

As healthcare shifts toward patient-centered solutions, unstructured clinical notes will be key to closing critical data gaps. The ability to capture these hidden patient struggles has the potential to transform care delivery and research.

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Why Clinical Measures Matter: Linking Disease Activity to Treatment Decisions in Actinic Keratosis

In dermatology, real-world data plays a crucial role in understanding disease progression and optimizing treatment strategies. Actinic keratosis (AK), a common precancerous skin condition, requires tailored management approaches based on disease severity. OMNY Health’s vast dermatology dataset provides new insights into AK treatment patterns by leveraging structured electronic health records, including lesion count and patient-reported pain scores.

Measuring Disease Activity: A Data-Driven Approach

One of the unique aspects of OMNY Health’s dataset is the inclusion of real-world AK disease activity measures. Traditionally, treatment decisions for AK have been guided by lesion count, but OMNY’s data also incorporates pain scores using the 0-10 Visual Analogue Scale (VAS). This dual-measure approach provides a more comprehensive understanding of how AK severity impacts treatment decisions.

Linking Disease Severity to Treatment Patterns

Analyzing data from six specialty dermatology networks within the OMNY Health platform (2017-2024), researchers examined how lesion count and pain VAS influence real-world treatment strategies. The study included 334,410 patients with 704,665 assessments, highlighting distinct trends in treatment selection.

Key findings include:

  • Fluorouracil prescriptions increased with lesion count but decreased with pain severity. 
  • Lesion destruction procedures (e.g., cryosurgery, electrosurgery) and photodynamic therapy became more common as pain scores increased. 
  • Treatment approaches remained relatively stable across different lesion counts, suggesting that pain level plays a more significant role in guiding intervention choices. 

Clinical Implications: The Power of Real-World Dermatology Data

These findings emphasize the value of structured EHR measures in refining dermatological treatment strategies. By incorporating both lesion count and pain VAS, OMNY Health’s dataset enables providers and researchers to:

  • Identify patterns in real-world clinical decision-making. 
  • Optimize treatment plans based on both physical disease burden and patient-reported symptoms. 
  • Treatment approaches remained relatively stable across different lesion counts, suggesting that pain level plays a more significant role in guiding intervention choices. 

Next Steps: Expanding Real-World Evidence in Dermatology

OMNY Health continues to enhance its dermatology RWD offerings by integrating unstructured clinical notes and refining disease activity metrics. Future analyses could leverage clinical notes to provide richer insights into treatment rationale and long-term outcomes.

As dermatology evolves, real-world evidence will be essential in bridging the gap between clinical research and everyday patient care. OMNY Health’s commitment to data-driven insights ensures that providers have access to the most comprehensive, research-ready EHR datasets to inform their decisions.

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OMNY Health Launches Dynamic GLP-1 Network of 600,000+ Patients to Fuel Clinical Research on Its Use

New Studies on the Use of These Medications in Adult and Pediatric Populations are Supported by the Data Network

Atlanta, Georgia – August 27, 2024 – OMNY Health, the leading healthcare ecosystem known for facilitating compliant cross-industry data partnerships, today announced the launch of its Glucagon-Like Peptide-1 (GLP-1) data network, further cementing the company’s commitment to democratizing healthcare data across industries. This new data network comprised already of over half a million patients will supply its life sciences and health system partners, as well as its AI-driven health tech partners, with additional knowledge of GLP-1 use, related social determinants of health (SDOH), and patient demographics to better inform generative AI tools, researchers, and clinicians and improve patient outcomes.

Despite a staggering amount of healthcare data being generated across health systems and life sciences companies, 97% of this data goes unused. This underutilization is particularly crucial as the usage of GLP-1 agonists surge, driven by their effectiveness in treating chronic conditions such as type 2 diabetes and obesity. The growing demand for these medications has sparked an urgent need for more comprehensive data that evaluates their effectiveness and long-term effects for other conditions like cardiovascular disease, liver disease, and skin disorders.

“GLP-1 therapies are transforming the treatment landscape across multiple conditions beyond diabetes and obesity in profound ways. The systemic impact of these medications underlines the need for more comprehensive data to fully understand how these therapies affect patients as a whole,” said Dr. Mitesh Rao, CEO of OMNY Health. “By democratizing data partnerships between life sciences companies and health systems, we can enable more in-depth clinical research that will fuel healthcare innovation and revolutionize the potential impact of GLP-1’s for years to come.”

OMNY’s GLP-1 data network consists of curated EMR data from more than 645,000 patients representing a broad set of demographic characteristics, including age ranges, race/ethnicity, region of care delivery, as well as provider and payer types. The data network supported two recent studies that address gaps in understanding GLP-1 therapies, particularly within pediatric and patient populations impacted by SDOH factors enabling partner companies to better understand and apply findings to patient treatments.

Pediatric use of GLP-1s was not approved by the FDA until December 2022, making data on these use-cases extremely limited despite the prescriptions of GLP-1RAs for children and adolescents increasing by 594.4% from 2020 to 2023. OMNY’s GLP-1 network contains valuable information on pediatric GLP-1 use, enabling a study that found the median age of users was 16 years and the majority were female in gender (72%).

“We are able to deliver more equitable clinical outcomes when the research is backed by data from diverse populations,” said Dr. Sameer Badlani, Fairview Health Services’ Executive Vice President, and Chief Strategy Officer. “With access to data that factors in SDOH for diverse populations we learn valuable insights in service of providing resources and clinical care that is differentiated by excellence in quality, safety, experience and health equity for every consumer who trusts us with their wellness and care.”

OMNY’s network also provides critical information on SDOH factors, including economic insecurity, food insecurity, and social isolation often documented in clinician notes. With many of these factors accounting for up to 50% of the variation in health outcomes in the US, it was crucial that the SDOH status of patients receiving GLP-1 treatment be made available. A second study by OMNY found that GLP-1 patients were half as likely to have SDOH economic burden issues noted in their EHR record as compared the population of non-users.

OMNY’s data network enables companies to diversify their patient populations for clinical trials, better understand medication interactions, and strengthen treatment outcomes.

This announcement follows OMNY’s partnerships with QuantHealth, the leading AI-driven clinical trial design company, and ArisGlobal, a technology company at the forefront of life sciences and the creator of LifeSphere®. OMNY Health is committed to AI-enabled healthcare improvement and supporting research programs that aim to improve clinical care. To learn more about how OMNY Health is transforming lives and driving patient care by connecting providers and life sciences companies through data, visit www.marketing-dev.omnyhealth.com.

About OMNY Health

OMNY Health™ is a national data ecosystem connecting the world of healthcare to fuel partnerships that improve clinical outcomes and drive patient care. OMNY’s dynamic partnerships with specialty health networks, healthcare systems, academic medical centers, and integrated delivery networks span all fifty states and cover over 75 million patient lives. The company’s comprehensive data layer powers health tech companies to drive the next generation of innovation. The platform serves as a centralized resource for life sciences and healthcare provider groups to facilitate mutually-beneficial data sharing and research collaboration at scale, fueling innovation where patients need it the most. OMNY Health’s data ecosystem now reflects more than seven years of historical data encompassing more than 2 billion clinical notes from 300,000+ providers across 200+ specialties – and is growing. For more information, visit www.marketing-dev.omnyhealth.com.

Media Contact:

Chloe Fredericksdorf
omnyhealth@solcomms.co.