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OMNY Health Dataset Hits 100 Million Patient Milestone, Unlocking Unprecedented Access to Healthcare Data for Research

Spanning nearly 30 percent of the U.S. population, the de-identified data available includes insights from unstructured clinical notes, electronic medical records, and medical claims

ATLANTA, GA – July 31, 2025 – OMNY Health, the leading healthcare ecosystem for compliant real-world data (RWD) insights at scale, today announced its data network now officially encompasses insights from more than 100 million patients across all 50 U.S. states. This landmark achievement solidifies OMNY Health’s position as a premier source for comprehensive, HIPAA-compliant de-identified RWD, providing life sciences organizations, healthcare providers, and AI innovators with unparalleled access to standardized, rich, longitudinal patient journeys.

Since onboarding its first provider partner in 2020, OMNY Health has expanded to include more than 46 leading healthcare organizations, including St. Luke’s University Health Network, Bon Secours Mercy Health, and Baptist Health System KY & IN, providing a comprehensive view of patient care. The data on its platform spans more than eight years of historical data, including billions of clinical encounters from more than 650,000 providers as well as 6.5 billion clinical notes, making it the deepest repository of unstructured clinical knowledge in the country. The breadth of the OMNY health platform enables deeper insights into disease progression, treatment patterns, and patient outcomes. Additionally, OMNY Health’s data spans every therapeutic area, with most having comprehensive data from more than 10 million patients each, further supporting its diverse use cases across the healthcare ecosystem.

“Unlocking AI’s transformative power in healthcare demands a new approach for collaboration. It’s not just about volume of data, but more importantly, about the quality, diversity, and real-world representation of the data available to researchers,” said Matthew Fenty, Managing Director for Innovation & Strategic Partnerships at St. Luke’s University Health Network. “From their beginnings, OMNY Health has maintained a keen understanding of this critical need and provides a platform that empowers health systems like ours to securely contribute and collectively build the rich, diverse data foundation essential for cutting-edge AI development. Our participation with OMNY Health is a testament to our commitment to advancing patient care through responsible data innovation.”

“The healthcare industry has witnessed time and again how the power of collective knowledge and collaboration can improve the outlook on complex healthcare challenges. The COVID-19 pandemic was a stark reminder of the need for sharing de-identified data to close information gaps,” said Mark Townsend, MD, MHCM, Chief Clinical Digital Ventures Officer, Bon Secours Mercy Health and Accrete Health Partners. “We are proud to partner with OMNY as they continue to bring stakeholders together, ensuring that shared data drives innovation safely and responsibly.”

Brett A. Oliver, MD, Chief Medical Information Officer at Baptist Health System KY & IN, added, “Artificial intelligence is only as smart as the patchwork quilt of information we feed it. At Baptist Health, we’ve learned that real clinical insight—and true equity—emerges only when every thread of the patient story is woven into the model. That’s why OMNY Health’s drive to knit together a living, breathing data network isn’t just helpful; it’s the loom on which tomorrow’s breakthroughs will be spun. We’re thrilled to keep adding squares to that ever‑growing quilt.”

OMNY Health’s proprietary AI, Natural Language Processing (NLP), and Large Language Model (LLM) technologies are central to this achievement, transforming vast amounts of raw, often unstructured, clinical information found in clinical notes into research-ready variables. This unique capability allows researchers to uncover nuances buried in free-text clinician notes – details on symptoms, adverse events, treatment rationales, and social determinants of health – that are critical for a complete understanding of real-world patient experiences.

“Reaching 100 million patient lives is a monumental step forward in our mission to accelerate life-changing innovations through data-driven insights,” said Dr. Mitesh Rao, Founder and CEO of OMNY Health. “This milestone could not have been achieved without the ongoing support of our data partners. We’re incredibly grateful to collaborate with leading organizations that share our vision for advancing healthcare research. As we continue to grow, we look forward to expanding the platform to impact more lives and solve some of the most difficult problems in healthcare.”

OMNY Health plans to continue to expand its dynamic network of specialty health networks, hospitals, academic medical centers, and integrated delivery networks across the U.S., with the goal of achieving more than 125 million patient lives on the platform by the end of 2025. For more information on OMNY Health and its data platform, please visit www.omnyhealth.com.

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About OMNY Health OMNY Health™ is the leading healthcare ecosystem for compliant real-world data insights at scale. OMNY Health connects patients, providers, and life sciences companies by transforming vast amounts of de-identified electronic health record data, clinical notes, and claims data into robust, research-ready insights. Leveraging proprietary AI, NLP, and LLM technologies, OMNY Health accelerates therapeutic innovation, optimizes clinical development, and enhances patient care. For more information, visit www.omnyhealth.com.

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Unlocking the Full Story: The Power of Clinical Notes in Real-World Data 

The year is 2025, or more than fifteen years since the enaction of The HITECH Act and Meaningful Use.  Almost all of the clinical data recorded from ordinary Americans’ physician office visits and hospital stays have now shifted to electronic format.  Therefore, increasing emphasis is gradually being placed on the value of real-world data, with the hope that medical knowledge resulting in care improvements can be extracted from the vast amount of information that exists in electronic health records. 

Structured vs. Unstructured Clinical Data 

This electronic clinical data can be subdivided into two categories – structured and unstructured.  Examples of structured data include demographic information, diagnoses and procedures (in the form of clinical codes), medication prescription information, insurance records, and vital signs, while examples of unstructured data include free-text clinical narratives and imaging and test reports. 

Both types of clinical information are important and perform complementary functions in real-world data.  Structured data contains many basic data elements and is traditionally easier to process, due to its tabular nature.  However, unstructured data has been estimated to comprise 80% of clinical data by volume and often provides insights that are absent from structured clinical data and claims data [1].  There is an old saying among medical professionals that “90% of diagnoses can be made using the patient history, and 10% using the physical exam [2]” (notably, both elements are virtually absent from structured EHR data). 

What are some of the details captured in the well-written clinical note that are typically excluded from structured EHR data?

Information Extraction from Unstructured Data in an LLM-World 

A well-written clinical note contains many details about the patient that are absent from both structured tabular data and claims data.  Until just a couple of years ago, the challenge was extracting information from a clinical note into a usable format.  However, with the advent of large language models (LLMs), one can present a note as context and ask a favorite LLM questions about the note, such as “Where is the location of this patient’s pain?” or “Why did the patient discontinue lisinopril?”  Adaptation of this method enables extraction of information from the note as structured categorical data, which can then be used as structured data. 

OMNY Notes: A First-of-its-Kind Clinical Notes Data Product  

OMNY Notes is one of our exciting new data products that makes billions of de-identified clinical notes from diverse health systems available to the end-user.   Researchers no longer must rely solely on structured EHR and claims data; they can now view the full patient journey with our HIPAA-compliant de-identified linked structured EHR, claims and notes solutions representing more than 75M individuals. No other solution available today provides the combined depth, breadth, and scale of OMNY structured and unstructured data to support improving quality, safety, and efficiency of healthcare delivery and overall public health.

Contact us at info@omnyhealth.com to learn more about our OMNY Notes product and our other data products: 

  • OMNY Foundation 
  • OMNY Linked Claims 
  • OMNY MedTech

References: 

    [1] https://healthtechmagazine.net/article/2023/05/structured-vs-unstructured-data-in-healthcare-perfcon.

    [2] Tsukamoto, Tomoko, et al. “The contribution of the medical history for the diagnosis of simulated cases by medical students.” International Journal of Medical Education 3 (2012).

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Cognitive Impairment in Alzheimer’s Disease: Patient Characteristics and Treatments in the Real-World Setting

OMNY Health’s recent study is shedding light on the role of cognitive impairment in patients diagnosed with Alzheimer’s disease.   Alzheimer’s disease (AD) is the most frequent type of neurodegenerative disease that develops over several years. It is characterized by multiple cognitive deficits that progress over time, including memory deterioration. Newly approved monoclonal antibodies, unlike traditional medications that primarily relieve symptoms, have been shown to slow the progression of Alzheimer’s disease by approximately 30%.

At OMNY Health, researchers recently completed a retrospective analysis (2017 to 2024) of electronic health records from over 150,000 patients that had received Alzheimer’s disease care in the United States.  The study aimed to characterize the cognitive state of patients and to describe their treatment regimens.  OMNY’s researchers focused on nearly 7,000 Alzheimer’s disease patients in the dataset who had either the Montreal Cognitive Assessment (MoCA) or the Mini-Mental State Exam (MMSE) scores available.  

Key Findings  

The findings provide insight into the cognitive state of patients as they are first observed within the health system and receiving a diagnosis of Alzheimer’s disease.   On average patients were 78 years of age at the time of their first observed diagnosis.  Following the first observed diagnosis of Alzheimer’s, patients were categorized into four groups based on their first reported cognitive scores: normal, mild, moderate, and severe.  

The distribution of cognitive severity among patients was as follows: 

  • Normal: 15%  
  • Mild: 37%  
  • Moderate: 35%  
  • Severe: 13% 

 

OMNY’s research also found that with increasing cognitive impairment (normal, mild, moderate, severe), there was a monotonic rise in the proportions of female patients (51%, 60%, 64%, 67%) and nonwhite patients (15%, 17%, 22%, 27%). 

The study additionally assessed treatment patterns within 30 days of diagnosis. Many patients (59%) received cholinesterase inhibitors or NMDA receptor antagonists—therapies aimed at symptom management—with usage rates consistent across impairment levels. Fewer than 1% of patients had received treatment with newer monoclonal antibodies.

Why It Matters  

As disease-modifying therapies become more accessible, OMNY’s insights can support the research community in evaluating treatment effectiveness, informing clinical decision-making, and advancing understanding of disease progression across patient populations.

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Mining the Clinical Note: Extracting FEV1/FVC with LLMs for Scalable RWE

An OMNY Health Study on Severity Measure Extraction in Respiratory Care

Understanding disease severity is essential to supporting treatment decisions, yet many critical severity metrics—especially in respiratory conditions—are often buried in unstructured EHR notes. At ISPOR 2025, OMNY Health presented findings on how large language models (LLMs) can accurately extract FEV1/FVC scores, a core pulmonary function test (PFT) metric, directly from free-text clinical notes.

Why It Matters 

Clinical measures like FEV1/FVC are pivotal in evaluating lung function and diagnosing COPD and asthma. However, these measures are not consistently captured in structured EHR fields, making them difficult to access at scale. OMNY Health’s study explored whether retrieval-augmented LLMs could help close that gap—automating severity extraction while preserving accuracy. 

Study Overview

OMNY Health researchers sampled 50 random note excerpts containing the phrase “FEV1/FVC” from the OMNY Health platform and categorized them as: 
 

  • Simple (S): One FEV1/FVC score present 
  • No Value (NV): No score provided 
  • Complex (C): Multiple scores present 

OMNY Health researchers tested two Gemini LLMs (Flash and Pro) using a structured prompt and evaluated:

  • Accuracy in correct extraction 
  • Hallucination rate (false outputs when no score existed) 
  • Latency (processing time in slot milliseconds) 

Key Findings 

1. High Accuracy in Simple Notes 
Flash extracted FEV1/FVC scores with 90% accuracy, outperforming Pro (73.3%).

2. No Hallucinations in NV Notes  
Neither model generated phantom data when no score was present.

3. Pro Recognized Complex Context Better 
While Flash returned one of the multiple scores, Pro acknowledged the complexity—useful for nuanced documentation.

4. Flash Was Faster 
Flash processed data using 6,210 slot milliseconds versus Pro’s 23,576—offering speed advantages for scale. 

Example Outputs

Performance Snapshot

What’s Next 

This study highlights how LLMs can reliably extract clinical severity measures from real-world EHR data. Moving forward, the team plans to evaluate performance across more complex note structures, investigate how interpretability and robustness vary by model, and assess the trade-offs between cost, speed, and depth of output. The long-term vision is to expand this approach to other therapeutic areas where structured fields fall short—enabling deeper, more scalable evidence generation. 

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OMNY Health Becomes the First EHR Dataset Available on Datavant Connect Powered by AWS Clean Rooms

OMNY Health, the leading healthcare ecosystem for compliant real-world data insights at scale, today announced it has become the first electronic health record (EHR) company to offer its real world data (RWD) dataset on Datavant Connect powered by AWS Clean Rooms through its Lighthouse Partner Program.

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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 Achieves HITRUST e1 Certification: A Milestone in Healthcare Data Security

We at OMNY Health are thrilled to announce that the OMNY Health Platform has successfully achieved HITRUST e1 Certification, a landmark achievement in our ongoing commitment to foundational cybersecurity controls and information risk management in healthcare. 

HITRUST e1 Certification focuses on foundational cybersecurity and the most critical set of controls for essential cybersecurity hygiene. This certification demonstrates that OMNY’s Ecosystem Platform has a comprehensive set of rigorous controls and best practices in place for essential for cybersecurity hygiene and protecting sensitive information. 

“The HITRUST e1 Validated Assessment is a good tool for cyber-aware organizations like OMNY HEALTH that want to build assurances and progressively demonstrate due diligence around information security and privacy,” said Robert Booker, Chief Strategy Officer at HITRUST. “We applaud OMNY HEALTH for their commitment to cybersecurity and successful completion of their HITRUST e1 Certification.” 

Achieving HITRUST Certification is no small feat. It represents countless hours of hard work, meticulous attention to detail, and an unwavering commitment to excellence from our entire team. This certification validates our robust approach to data security and privacy, covering 19 domains of information security. 

“The HITRUST e1Certification is more than just a badge of honor – it’s a rigorous, comprehensive validation of our security practices,” said Dr. Maik Lindner, OMNY’s Chief Information Security Officer.  “This achievement demonstrates that we’ve implemented a robust set of controls that meet the unique challenges of protecting sensitive healthcare data. It’s a reflection of our proactive approach to security and our commitment to staying ahead of evolving cyber threats in the healthcare industry.” 

For our health system partners, the HITRUST Certification of our platform offers several key benefits:

  • Enhanced Trust: You can be confident that your data is protected by security measures that meet or exceed industry standards.
  • Simplified Compliance: Our certification helps streamline your compliance efforts, particularly with regulations like HIPAA. 
  • Reduced Risk: With our certified security framework, the risk of data breaches and associated costs is significantly reduced.
  • Improved Interoperability: Our certification enhances our ability to securely share and process data across the healthcare ecosystem.

“Achieving HITRUST certification is a testament to our unwavering commitment to data security and privacy in healthcare. This milestone reflects the dedication of our entire team and reinforces our position as a trusted partner in the healthcare data ecosystem. We’re proud to offer our health system partners the highest level of assurance in data protection, allowing them to focus on what matters most – improving patient outcomes,” said Dr. Mitesh Rao, CEO. 

At OMNY, we view this certification not as a final destination, but as a milestone in our ongoing journey of excellence. We are committed to:

  • Continuously improving our security measures 
  • Staying ahead of emerging threats in the digital healthcare landscape
  • Providing our partners with the highest level of data protection 

As we celebrate this achievement, we want to thank our dedicated team and our valued partners for their trust and support. We look forward to continuing our mission of advancing healthcare through secure, innovative data solutions.