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Datavant Announces New Tools to Power Enterprise Data Connectivity

Datavant

May 23, 2023

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Today, we’re announcing our enterprise data connectivity solution, which enables our partners to bring together disparate datasets in a controlled and compliant way, with a bird’s eye view of all data connectivity projects across their organization.

Leading pharmaceutical companies work with hundreds of first and third-party data sources, and dozens of analytics platforms. One major challenge is that these sources and platforms are rarely interoperable. As a result, bringing together data from different parties can take months, if not years. This translates into under-utilization of data and foregone insights from clinical development to commercial launch. While many companies aspire to approach this systematically across the enterprise by building something new, Datavant accelerates the journey by working with your existing infrastructure and partners to unlock enterprise data connectivity.

The data strategy challenges that our customers face

Let’s use the example of a pharmaceutical company that wants to create an enterprise data strategy spanning their own data from Research and Development (e.g., trial data, genomic screening tests) and Commercial (e.g., specialty pharmacy networks, patient HUBs), along with commercially licensed data assets (e.g., claims database, oncology EHR dataset, etc.). Historically the company’s data acquisition and analysis has been decentralized, resulting in huge amounts of under-utilized data and unrealized value.

Today, the company wants:

A holistic enterprise-wide view of what data they have and how it is being used
A path to activating that data for specific use cases with the least possible friction
Assurance that all data is being used in a controlled and compliant way

How might the company unlock value from connecting their data? Here are a few examples:

Enable a long-term monitoring strategy across their R&D portfolio by connecting tokenized trial patients to comprehensive claims databases
Pool multiple data sources to create statistically significant synthetic control arms for oncology studies
Connect genomic test results to claims data in order to build a predictive model that helps diagnose potential rare disease patients sooner
Bring together dispense and claims data from multiple pharmacies and insurers to understand adherence and switching behavior

Sophisticated clinical development, real world evidence, and commercial analytics teams are pursuing many of these use cases today. The next step is working systematically across the enterprise to see all data assets at a glance, understand how they’re being activated, and ensure their use in a controlled and compliant way.

The same challenges pharmaceutical companies face are shared by large insurers, medical device companies, research networks, and government agencies. Organizations across healthcare need enterprise data connectivity to help them realize the full value of their data.

Unlocking the power of enterprise-wide data connectivity

Organizations need a way to bring data together quickly, seamlessly and compliantly — and then send it where it needs to go. One way of framing the task is through four types of connections that an organization needs to manage:

Source connections to all the relevant sources of data needed to solve a particular problem (e.g., claims data source, SDOH data source, first party data)
Application connections to the relevant applications needed to process the data and ensure that it is useful to address the problem (e.g., data quality score, synthetic data generator)
Platform connections to those that can provide integrated analytics and solutions relevant to the particular problem (e.g., oncology analytics platforms, patient journey mapping tools, commercial analytics dashboards)
User connections that allow connected, useful data to be sent to the end user (e.g., an internal analytics team, consultancy, or customer)

To unlock the power of enterprise data connectivity, no one wants to start from scratch. In most organizations, these connections consist of dozens of bespoke data projects, with spreadsheets attempting to keep track of everything. The key to moving from one-off projects to enterprise data connectivity is to leverage what you already have in place, but then to make the different sources, applications, and internal teams work seamlessly together. That’s the problem that Datavant’s technology is designed to solve: our suite of tools enables companies to build the ultimate data connectivity platform.

Key features of Datavant’s enterprise data connectivity solution

Datavant’s enterprise solution includes the ability to:

Explore potential partners in the rapidly growing health data ecosystem, with access to the largest network of sources, applications, platforms and users
Assess how datasets would complement each other when linked
Onboard and distribute data with compliance and control
De-identify data, by removing or transforming fields to minimize risk
Connect data, by generating encrypted IDs that can be matched at the individual level across datasets even when underlying PII has changed
Comply with HIPAA regulations every step of the way through industry experts and technology solutions
Activate and manage connected data projects, including controlling permissions, across your organization

New tools, including project management dashboards, enable data connectivity at scale:

*Get a bird’s eye view of all the data connectivity projects happening across your organization*

*Drill down into specific projects to see current status of partner relationships*

Our most successful enterprise customers use Datavant’s tools holistically to activate and manage many dozens of use cases across their organization. Each use case involves data flows with different users, data sources, and third-party platforms. Our tools enable customers to project manage this activity across the enterprise, defining goals and access permissions for each use case — like linking trial data with real-world data or bringing together disparate datasets for commercial analytics. This approach allows data and analytics leaders to manage dozens of complex connectivity projects at a glance.

Realize your data strategy, improve patient outcomes

Datavant can help you realize your enterprise data strategy by unlocking the full value of the data you already have. The value of enterprise-wide data connectivity is improved patient outcomes: more effective research and development, a deeper understanding of patient populations, and an enhanced ability to demonstrate the safety and efficacy of therapies.

Request a demo to explore how Datavant can power your enterprise data connectivity platform.

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.