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Inside Look: How OMNY Health is Revolutionizing Real-World Data Access with a Cloud-First Approach

Datavant

March 11, 2025

min

Table of Contents

The demand for real-world data (RWD) in life sciences research is on the rise, driven by the need for more personalized and effective healthcare solutions. From clinical development to post-market surveillance, researchers rely on diverse data sources to generate meaningful insights. However, accessing high-quality RWD remains a significant challenge due to fragmentation, complex privacy considerations, and inefficient feasibility assessment processes.

Traditionally, identifying the right dataset for a study involves a manual, time-intensive process that can take months. Compounding the problem, data purchases frequently result in mismatches between the acquired data and research needs—industry estimates suggest that up to 40% of data purchases are unsuccessful, leading to costly delays.

To tackle these challenges head-on, Datavant and Amazon Web Services (AWS) have joined forces to revolutionize the process, making data connectivity faster, more seamless, and more powerful than ever before. Leading the way in this transformation is OMNY Health, a pioneer in adopting a cloud-first strategy for RWD accessibility. Through this partnership, researchers will be able to accelerate feasibility assessment and ultimately reduce the time from data acquisition to actionable insights.

The Case for a Cloud-First Real-World Data Strategy

Traditional RWD feasibility analyses are inefficient and often yield suboptimal results and missed opportunities for breakthroughs:

Finding the right data sources requires researchers to engage multiple vendors and manually assess the utility of each dataset.
Researchers often struggle utilizing electronic health record (EHR) data, which is siloed across multiple systems, making interoperability and timely access difficult.
It can take months to find suitable data for studies.
Even after purchase, data is often inconsistent, incomplete, or outdated.

A cloud-first approach offers a solution by centralizing dataset exploration and streamlining feasibility analyses. With Datavant Connect powered by AWS Clean Rooms, research teams can identify and evaluate RWD from multiple sources, unlocking greater efficiency and accelerating time to insight. For data sources, Clean Rooms provide more visibility into how data is being evaluated.

OMNY’s Cloud-First Approach: A Blueprint for Scalable, Flexible EHR Data Utilization

OMNY Health is a national data ecosystem encompassing more than 4 billion clinical notes from 500,000+ providers across 200+ specialties. OMNY’s data ecosystem reflects more than 8+ years of historical data and covers over 85 million patient lives.

With such robust, multimodal data, OMNY sought to make it easier for researchers to analyze feasibility and access the precise cuts of data for their specific use cases. Therefore, OMNY decided to register its entire dataset on AWS – embracing a cloud-first approach to enhance data discoverability and usability. This enables researchers to more quickly and accurately determine whether OMNY’s data aligns with their study needs.

Key Benefits of OMNY’s Cloud-First Approach

Scalability: As a lean, growth-stage organization, OMNY requires scalable solutions to support generating, evaluating, and closing data purchase agreements with life sciences companies and other data consumers. By leveraging a cloud-first approach, OMNY accelerates each phase of data purchasing, fueling growth while maintaining operational efficiency.
Flexibility: OMNY’s rich clinical dataset can answer deep research questions, in some cases as the primary data source and in other cases as a complementary data source to another primary dataset. By making its entire dataset available on AWS for evaluation, OMNY increases its chances of being discovered for a broader range of use cases.
Efficiency: With a faster and more streamlined data purchasing process on both sides of the ecosystem, OMNY more efficiently disqualifies opportunities that aren’t a strong fit — freeing its team to focus on targeted research questions that align with its data network and other high-value work that improves patient outcomes.

Real-World Impact: OMNY’s Work in Action

As an industry pioneer in making its entire dataset available for discovery on the cloud, OMNY offers life sciences teams rapid feedback on how its dataset aligns to their direct research needs.

These recent examples showcase how OMNY’s rich, multimodal EHR data is driving meaningful insights for life sciences research:

Case Study 1: Unlocking Insights into IBD Treatment

Inflammatory bowel disease (IBD) treatment pathways are complex, with variations in therapy response and disease progression. OMNY’s research combined structured EHR data with unstructured physician notes to generate novel insights into patient outcomes. By leveraging multimodal data, researchers identified 7 distinct reasons for treatment alteration across 7 biologics.

Case Study 2: Understanding Healthcare Costs for GPP Flares

Generalized pustular psoriasis (GPP) flares can result in hospitalizations and significant healthcare costs. OMNY analyzed multimodal data, including unstructured clinical notes and claims data, to uncover hidden cost drivers and patterns in patient care. These insights are shaping cost-effective interventions and improving patient management strategies.

Get Involved: Experience the Future of RWD Access

By adopting a cloud-first approach, data sources and data consumers are revolutionizing real-world data access—accelerating insights, improving patient care, and shaping the future of life sciences research.

Ready to live at the forefront of cloud-first data strategy?

Limited spots are available in the Lighthouse Partner Program for early adopters to provide feedback to Datavant and AWS. Join the waitlist here.
Connect with OMNY Health at info@omnyhealth.com.
Meet us at ISPOR 2025! Datavant will be presenting on cloud-first insights generation in the Exhibit Hall Theater on Thursday, May 15, at 10:15 am EST, and at Booth #1707.

Spotlight on AnalyticsIQ: Privacy Leadership in State De-Identification

AnalyticsIQ, a marketing data and analytics company, recently adopted Datavant’s state de-identification process to enhance the privacy of its SDOH datasets. By undergoing this privacy analysis prior to linking its data with other datasets, AnalyticsIQ has taken an extra step that could contribute to a more efficient Expert Determination (which is required when its data is linked with others in Datavant’s ecosystem).

AnalyticsIQ’s decision to adopt state de-identification standards underscores the importance of privacy in the data ecosystem. By addressing privacy challenges head-on, AnalyticsIQ and similar partners are poised to lead clinical research forward, providing datasets that are not only compliant with privacy requirements, but also ready for seamless integration into larger datasets.

"Stakeholders across the industry are seeking swift, secure access to high-quality, privacy-compliant SDOH data to drive efficiencies and improve patient outcomes,” says Christine Lee, head of health strategy and partnerships at AnalyticsIQ.

“By collaborating with Datavant to proactively perform state de-identification and Expert Determination on our consumer dataset, we help minimize potentially time-consuming steps upfront and enable partners to leverage actionable insights when they need them most. This approach underscores our commitment to supporting healthcare innovation while upholding the highest standards of privacy and compliance."

Building Trust in Privacy-Preserving Data Ecosystems

As the regulatory landscape continues to evolve, Datavant’s state de-identification product offers an innovative tool for privacy officers and data custodians alike. By addressing both state-specific and HIPAA requirements, companies can stay ahead of regulatory demands and build trust across data partners and end-users. For life sciences organizations, this can lead to faster, more reliable access to the datasets they need to drive research and innovation while supporting high privacy standards.

As life sciences companies increasingly rely on SDOH data to drive insights, the need for privacy-preserving solutions grows. Data ecosystems like Datavant’s, which link real-world datasets while safeguarding privacy, are critical to driving innovation in healthcare. By integrating state de-identified SDOH data, life sciences can gain a more comprehensive view of patient populations, uncover social factors that impact health outcomes, and ultimately guide clinical research that improves health.

The Power of SDOH Data with Providers and Payers to Close Gaps in Care

Both payers and providers are increasingly utilizing SDOH data to enhance care delivery and improve health equity. By incorporating SDOH data into their strategies, both groups aim to deliver more personalized care, address disparities, and better understand the social factors affecting patient outcomes.

Payers Deploy Targeted Care Using SDOH Data

Payers increasingly leverage SDOH data to meet health equity requirements and enhance care delivery:

Tailored Member Programs: Payers develop specialized initiatives like nutrition delivery services and transportation to and from medical appointments.
Identifying Care Gaps: SDOH data helps payers identify gaps in care for underserved communities, enabling strategic in-home assessments and interventions.
Future Risk Adjustment Models: The Centers for Medicare & Medicaid Services (CMS) plans to incorporate SDOH-related Z codes into risk adjustment models, recognizing the significance of SDOH data in assessing healthcare needs.

Payers’ consideration of SDOH underscores their commitment to improving health equity, delivering targeted care, and addressing disparities for vulnerable populations.

Example: CDPHP supports physical and mental wellbeing with non-medical assistance

Capital District Physicians’ Health Plan (CDPHP) incorporated SDOH, partnering with Papa, to combat loneliness and isolation in older adults, families, and other vulnerable populations. CDPHP aimed to address:

Social isolation
Loneliness
Transportation barriers
Gaps in care

By integrating SDOH data, CDPHP enhanced their services to deliver comprehensive care for its Medicare Advantage members.

Providers Optimize Value-Based Care Using SDOH Data

Value-based care organizations face challenges in fully understanding their patient panels. SDOH data significantly assists providers to address these challenges and improve patient care. Here are some examples of how:

Onboard Patients Into Care Programs: Providers use SDOH data to identify patients who require additional support and connect them with appropriate resources.
Stratify Patients by Risk: SDOH data combined with clinical information identifies high-risk patients, enabling targeted interventions and resource allocation.
Manage Transition of Care: SDOH data informs post-discharge plans, considering social factors to support smoother transitions and reduce readmissions.

By leveraging SDOH data, providers gain a more comprehensive understanding of their patient population, leading to more targeted and personalized care interventions.

While accessing SDOH data offers significant advantages, challenges can arise from:

Lack of Interoperability and Uniformity: Data exists in fragmented sources like electronic health records (EHRs), public health databases, social service systems, and proprietary databases. Integrating and securing data while ensuring data integrity and confidentiality can be complex, resource-intensive and risky.
Lag in Payer Claims Data: Payers can take weeks or months to release claims data. This delays informed decision-making, care improvement, analysis, and performance evaluation.
Incomplete Data Sets in Health Information Exchanges (HIEs): Not all healthcare providers or organizations participate in HIEs. This reduces the available data pool. Moreover, varying data sharing policies result in data gaps or inconsistencies.

To overcome these challenges, providers must have robust data integration strategies, standardization efforts, and access to health data ecosystems to ensure comprehensive and timely access to SDOH data.

SDOH data holds immense potential in transforming healthcare and addressing health disparities.

With Datavant, healthcare organizations are securely accessing SDOH data, and further enhancing the efficiency of their datasets through state de-identification capabilities - empowering stakeholders across the industry to make data-driven decisions that drive care forward.