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Real-world data

Accelerating Urology Research with Cloud-First Data Discovery and Assessment

Datavant

March 11, 2025

5 Minutes

Table of Contents

Real-world data has become an essential resource in healthcare research, offering valuable insights into patient journeys, treatment effectiveness, and healthcare utilization. In the field of urology, where patient outcomes and treatment patterns are complex, access to high-quality data is critical for health economics and outcomes research (HEOR) teams within life sciences.

However, identifying and assessing relevant datasets remains a significant challenge. Traditional data acquisition methods are slow, fragmented, and resource-intensive, often requiring extensive time and effort.

To address these challenges, Verana Health has partnered with Datavant and Amazon Web Services (AWS) to enable a cloud-first approach to real-world data discovery and assessment. This collaboration allows researchers to identify insights from curated electronic health record (EHR) data without moving the underlying datasets, ensuring faster access while preserving privacy.

“Datavant Connect’s new functionality accelerates the identification of our insights from curated EHR data by buyers, without moving the underlying data. This allows our partners quicker access to our unique multi-modal view of the patient journey to help advance their clinical research.”

– Sujay Jadhav, CEO at Verana Health

The Challenge: Unlocking Insights from Fragmented Data

For HEOR professionals, the ability to efficiently access high-quality real-world data and track outcomes is critical for conducting meaningful research in urology. However, traditional approaches to data acquisition and assessment present the following challenges:

Slow and Inefficient Processes: Identifying and acquiring relevant datasets often requires extensive time and effort, delaying research timelines.
Fragmented Data Landscape: Data exists across multiple platforms, making it difficult to compile a comprehensive view of patient experiences.
Privacy Compliance: Strict regulations around patient privacy add complexity to the process, limiting how datasets can be accessed and utilized.

These barriers restrict the ability of HEOR teams to generate timely, data-driven insights, potentially slowing advancements in urology research and patient care.

The Solution: Cloud-First Data Discovery and Assessment

Datavant and AWS are addressing these challenges by introducing cloud-first data discovery and assessment which allows data sources, such as Verana Health, to better surface insights. This approach allows researchers to efficiently evaluate datasets stored in AWS—without requiring data transfers—supporting a faster, more scalable solution for real-world data utilization.

Why Verana Health is choosing this new approach:

Expedite Data Identification & Access: Accelerate insights from Verana Health’s specialty curated EHR data in numerous urologic diseases.
Effortless Dataset Registration: Easily register AWS-stored datasets within Datavant Connect, making them discoverable by researchers.
Robust Access & Query Controls: Define specific access rules and query permissions, ensuring datasets can be evaluated while preserving privacy.
Actionable Engagement Insights: Track dataset interactions to monitor engagement, optimize access strategies, and support continued compliance.

With a cloud-first approach, Verana Health reduces friction in the data acquisition process, allowing HEOR teams to quickly access real-world data that is relevant to their research needs while reducing security risks and supporting privacy compliance.

Real-World Impact on Urology Research

By enhancing access to real-world data, HEOR teams can drive more informed decision-making in pharmaceutical research and healthcare, ultimately improving patient outcomes in urology.

Key benefits include:

Faster Dataset Assessment: Researchers can quickly determine whether a dataset is fit for their studies before committing to acquisition.
Seamless Integration Into HEOR Workflows: The ability to evaluate datasets in the cloud reduces operational burden and enhances efficiency.

Example Use Cases in Urology Research:

Evaluating Treatment Patterns & Outcomes: Understanding the real-world impact of various treatment strategies across patient populations.
Identifying Patient Cohorts for Comparative Effectiveness Studies: Building robust patient groups to assess differences in treatment efficacy.
Assessing Economic Burden & Resource Utilization: Analyzing healthcare costs and resource allocation to inform value-based care initiatives.

Curious to learn more?

Limited spots are available in the Lighthouse Partner Program for early users to provide feedback to Datavant and AWS. Join the waitlist here.

Get in touch with the Verana Health team here.

We’ll also be presenting at ISPOR in May 2025. Hope to see you in Montreal!

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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.