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What is Genomic Data and How to Access It?

Datavant

April 4, 2024

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Genomic data is the result of analyzing an individual's DNA sequence, encompassing information about identified genes, variants, and their specific locations within the genome.

Genomic variations play a critical role in numerous human diseases, spanning from common conditions like hypertension to complex disorders such as cancer and rare diseases. Precision therapies often aim to address specific genetic mutations underlying these diseases and driving their progression.

Real-World Applications of Genomic Data

Genomic data isn't enough alone; it needs real-world data like patient demographics data, EHR data, imaging data, and claims data. Combining genetic data with clinical data helps increase study accuracy.

The integration of genomic data with real-world data (RWD) is revolutionizing healthcare research and clinical practice. Here are the key applications of genomic data:

Identify Physicians with Eligible Patients: By connecting genomic data with claims and lab data, researchers can identify physicians who have patients eligible for specific drugs, enabling targeted interventions and optimizing treatment pathways.
Identify Undiagnosed Patients: Connecting real-world data enables the creation of cohorts of potentially undiagnosed patients, facilitating early intervention and improving diagnostic accuracy.
Analyze Treatment: By connecting specialty pharmacy data, researchers can perform cohort analysis to understand treatment effectiveness, identify trends, and optimize therapeutic approaches for complex conditions.

Privacy Considerations in Genomic Data Usage

Despite its immense potential, the utilization of genomic data raises significant privacy concerns due to its sensitive nature. Genomic data is difficult to anonymize as only a small collection of genomes is needed to identify an individual, making it extremely identifiable data.

Safeguarding patient privacy is paramount, and researchers must adhere to stringent data protection protocols. Measures to ensure privacy include:

De-Identification: Privacy experts should mitigate the risk of disclosing individuals from genomic data through a combination of data modifications and technical, legal, and administrative safeguards such that the genomics data is considered de-identified under HIPAA.
Informed Consent: Researchers must obtain explicit consent from participants before accessing their genomic data, ensuring transparency and respect for individual autonomy.
Secure Data Storage: Robust data storage infrastructure with encryption and access controls is imperative to safeguard genomic data against breaches or unauthorized usage.

Emerging Trends in Lab and Genomic Data

The landscape of lab and genomic data is evolving rapidly, driven by technological advancements and shifting healthcare paradigms. Key trends include:

Rise of Genomic Testing: Greater understanding of biomarker-drug associations, declining testing costs, and expanding reimbursement are fueling the proliferation of genomic testing across diverse clinical settings.
Shift Towards Less Invasive Methods: Innovations in liquid biopsy and other less invasive testing methods enable earlier disease detection and ongoing monitoring, revolutionizing disease management and patient care.
Population-Level Genomic Testing: Health systems are increasingly adopting population health screening programs to elucidate genotype-phenotype interactions and drive biomarker discovery, advancing precision medicine initiatives.
Diagnostics Industry Transformation: The diagnostics industry is witnessing a surge in direct-to-consumer (DTC) testing options, facilitated by startups and conventional players, leading to fragmented patient data capture and greater consumer empowerment.

Genomic data represents a transformative force in healthcare research and clinical practice, offering unprecedented opportunities for personalized medicine and improved patient outcomes. With Datavant's real-world data ecosystem, researchers can use genomic data while navigating privacy considerations and driving collaborative innovation in genomic medicine.

Through continual advancements and responsible data stewardship, the integration of genomic data into healthcare research holds the promise of a brighter, more personalized future for patients worldwide.

Access Genomic Data with Datavant Connect

Datavant plays a pivotal role in democratizing access to genomic data through its real-world data ecosystem. By connecting to diverse sources of health data, including genomic and lab data, Datavant empowers actionable insights while ensuring compliance with privacy regulations. Benefit from:

Comprehensive Data Access: Datavant provides researchers with access to a vast network of genomic and lab data, facilitating multidimensional analyses and enabling groundbreaking discoveries.
Privacy-Preserving Technologies: Leveraging state-of-the-art privacy-preserving technologies, Datavant maximizes data utility of genomic data while protecting patient privacy.
Collaborative Research Opportunities: Datavant fosters collaboration among researchers and healthcare stakeholders, catalyzing innovation and accelerating the pace of scientific discovery in genomic medicine.

Datavant ecosystem provides access and connectivity to various data sources, including:

Claims data
EHR data
Genomic data
Mortality data
SDOH data
Specialty pharmacy data

With Datavant, organizations can securely access genomic and lab data to empower data-driven decisions.

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.