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Linked dataset connects Inovalon claims data with OMNY Health EHR data to power real-world evidence research
Combines claims data alongside structured and unstructured clinical records for a 360-degree view of the patient
Real-world data offering helps researchers better understand disease progression and treatment response

BOWIE, Md. – August 11, 2026 – Inovalon, a leading provider of data and solutions empowering data-driven healthcare, and OMNY Health, a leading healthcare ecosystem for compliant real-world data (RWD) insights at scale, today announced a strategic real-world data partnership that combines Inovalon’s primary source closed claims data with OMNY Health’s EHR data and clinical notes. The dataset gives researchers and life sciences organizations a comprehensive view of disease progression, treatment patterns, and outcomes to support real-world evidence studies and help accelerate access to new precision therapies for patients.

The linked dataset includes 248 million lives in closed claims, 175 million lives in EHR data, and 7+ billion clinical notes, with more than 40% of lives linked across sources. Closed claims provide longitudinal visibility into care utilization and costs across providers, payers, and settings, while EHR data and clinical notes add the clinical depth behind each encounter, including medical history, test results, disease-specific scores and markets, provider or patient reported outcomes. Together, they support research into how therapies perform in the real world across diverse patient populations.

“True innovation in medicine requires looking beyond the codes on a medical bill to understand the actual human experience of a disease,” said Mitesh Rao, M.D., CEO of OMNY Health. “By linking Inovalon’s massive longitudinal claims engine with OMNY’s deep, unstructured clinical data and physician notes, we are giving researchers the closest thing to a complete, real-world patient narrative. This linked dataset bridges a critical gap, allowing life sciences teams to discover meaningful insights faster and accelerate the delivery of precise, life-saving therapies.”

The dataset supports research across any therapeutic area, and the value compounds in complex conditions. For example, with Alzheimer’s disease, patients move between insurance plans as they age and their disease progresses. Claims data maintains continuity across those transitions, while clinical notes capture cognitive decline or improvement, so researchers gain a continuous view of both the care pathway and the underlying disease.

“HEOR teams are under growing pressure to generate evidence that is clinically rich, economically defensible, and compliant,” said Ed Chidsey, President of Inovalon’s RWD & Insights and Payer Business Units. “Inovalon and OMNY are giving researchers a complete de-identified view of the patient along with the clinical and economic insights to drive the next generation of breakthrough research.”

Inovalon and OMNY Health share a commitment to protecting patients and their sensitive health data. Privacy, security, and data quality are foundational to the offering, with de-identification under the HIPAA Expert Determination standard and continuous quality controls applied at every stage of the data lifecycle.

As part of the collaboration, Inovalon has made an undisclosed strategic investment in OMNY Health.

To learn more about Inovalon’s linked real-world dataset and full suite of HEOR and RWE solutions, please visit: https://www.inovalon.com/products/life-sciences/.

About Inovalon

Inovalon is a leading provider of data and solutions empowering data-driven healthcare. We bring together national-scale connectivity, real-time primary source data access, and advanced analytics into a sophisticated platform empowering improved outcomes and economics across the healthcare ecosystem. The company’s analytics and capabilities are used by over 50,000 active, licensed customers, and are informed by the primary source data of more than 99 billion medical events across 1.1 million physicians, 736,000 clinical settings, and 461 million unique lives. For more information, visit www.inovalon.com.

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.

Contact:

Tom Paolella

AVP, Press and Analyst Relations, Inovalon

Thomas.Paolella@inovalon.com

OMNY Health Media Contact: media@omnyhealth.com

Depression and anxiety are common conditions associated with a range of physical health outcomes, including changes in body weight. However, risk is not uniform across all patients. Two individuals with the same diagnosis may have very different symptom profiles and very different metabolic risks.

This symptom variability raises an important question: Can routinely collected symptom data help identify patients who may be at higher risk for clinically meaningful weight outcomes?

Many real-world data sources capture diagnoses, procedures, and medications, but few contain the depth of clinical information needed to understand symptom-level variation within a disease. Integrated electronic health record data can provide access to routinely collected patient-reported outcomes, such as the Patient Heath Questionnaire-9 (PHQ-9) responses, which is a questionnaire assessing mental health symptoms across several domains. The integration of the PHQ-9 with clinical measurements like body mass index (BMI) and comorbidities creates an opportunity to move beyond diagnosis codes and evaluate how specific symptoms may relate to meaningful health outcomes.

To explore this question, we analyzed real-world clinical data from nearly 2 million encounters among adults with depression and/or anxiety who had documented BMI measurements and item-level responses to the PHQ-9 within the OMNY Health real-world data platform. Rather than focusing only on diagnosis-level measures, we examined PHQ-9 item 5, which captures appetite-related symptoms (“poor appetite or overeating”).

Our findings showed that appetite dysregulation was associated with meaningful differences in BMI outcomes. Higher severity of appetite-related symptoms was associated with increased likelihood of both underweight and severe obesity, suggesting that appetite-related symptoms may identify patients at risk for weight extremes.

Importantly, the relationship was not simply driven by obesity overall. The strongest pattern was observed for class II-III obesity, while class I obesity remained relatively stable across appetite symptom severity levels. This result suggests that symptom-level data may help identify patients with more clinically significant metabolic risk profiles.

These findings persisted even after accounting for demographic characteristics, antidepressant and antipsychotic use, and cardiometabolic comorbidities including diabetes, hypertension, and dyslipidemia.

A key takeaway is that routinely collected clinical information, such as PHQ-9 responses, when documented and accessible, can provide value beyond traditional diagnosis categories. Item-level patient-reported outcomes may offer scalable opportunities to better understand heterogeneity within populations and support more personalized approaches to care.

As real-world data continues to expand, leveraging the depth of information already captured in clinical workflows may help uncover new insights into disease patterns, patient risk, and opportunities for intervention.

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This work was presented as a podium presentation at the 2026 ISPOR Annual Meeting in Philadelphia, highlighting the value of rich real-world clinical data and patient-reported outcomes for generating actionable evidence.