By Steve Davenport (US Commercial Data Acquisition, Takeda) and Su Huang (Head of Data Strategy, Datavant)
Datavant recently hosted Real-World Data Connect, an event in New York City for real-world data leaders in the life science industry to convene and discuss use cases for linked real-world data across the therapeutic development lifecycle, from R&D to HEOR and Commercial. There were multiple sessions throughout the event – this blog post summarizes discussion and learnings from the Patient Insights track, which was focused on the use of real-world data to understand the patient journey and support commercial launches.
There are more options in the real-world data provider landscape than ever before, particularly when it comes to specialty data assets that are data type specific (e.g. comprehensive claims, lab results, genomics) or disease area specific (e.g. oncology). This means developing a layered approach to your real-world data strategy. Panel participants used a salad bar analogy to describe the approach – an ideal state where an analytics team can combine different data assets and categories all with a common anonymized patient key to bring it all together. A good approach is to start with the questions to be answered, then choose your base ingredient, and keep adding toppings to get to the precise data needed.
In oncology and precision medicine for example, panelists spoke about the challenge of small patient samples found in single datasets. In situations like this, you can tokenize data to de-duplicate patients across multiple claims datasets, creating a larger unique study population that is pooled from multiple data providers. Claims data can be further linked to specialized datasets such as genomics in order to stratify the study population by biomarker status, for more precise cohort analysis.
Data linkage can also be applied to chronic diseases to build a longitudinal, contextualized understanding of the patient journey and experience. For one use case in diabetes, claims data and HbA1c lab data for patients using continuous glucose monitor (CGM) devices was found through the process of conducting dataset overlaps. The ultimate goal was to understand the effectiveness of glucose control using the lab data, as well as assess broader health benefits of using a CGM device through the claims data. When conducting this type of analysis, completeness of data capture for a given patient is critical in order to gain a holistic understanding of the health impact of glucose control methods and device usage. You will learn things from the linkage process that you didn’t know to ask beforehand.
Connecting and analyzing real-world data can introduce insights and provide context about the “why” that may not have been clear. For instance, there is a huge gap today in patients actually starting on a prescribed medication. Connecting prescription data with Rx claims and consumer data can elucidate the “why”. Are patients with certain comorbidities reluctant to start a new medication out of concern for drug-to-drug interactions? Are patients without access to co-pay cards more likely to not fill their script? Are certain patients lacking the necessary caretaker support to start and remain on therapy? There are different strategies to address each of these reasons for prescription abandonment so understanding the specific pain points is important.
Once you understand these trends, how do you bring these insights back to the community to impact patient outcomes? A strategy that one large healthcare organization is utilizing is to leverage relationships with pharmacies, community provider practices and health systems to provide insights back to the pharmacists and community providers whom patients see on a routine basis. Patients trust clinicians, and when a clinician is involved in recommending a course of action, engagement increases to 65-75% versus direct-to-patient channels that typically plateau at 10-15% adoption. In general, there was significant emphasis across the audience that patients ought to get direct benefits out of the insights being generated from RWD research.
Building an enterprise wide capability around real-world data evaluation, de-identification, linkage, and analysis can require a change management process internally within organizations. Some companies have established a standing cross-team review process for new use cases that span participants from business, analytics, IT, compliance, ethics, privacy, security and legal. This process, along with plenty of communication and documentation along the way, helps everyone get aligned on the questions to be answered and to get comfortable with how to enable RWD linkage compliantly. This commitment also helps the collective organization mature over time, develop repositories of standard educational materials, and gain expertise with use cases involving linked RWD.
In addition to internal change management, external collaboration and knowledge-sharing is needed. Sharing of both challenges and best practices from organizations with more expertise can help bring less-experienced organizations faster up the learning curve. Life science organizations are also looking for collaboration and flexibility from the data provider community, especially when it comes to pricing structures for data licenses that would enable a more incremental approach for smaller and more precise data assets. To go back to the salad bar analogy, one participant asked how we can move away from a one-size-fits all, Costco-size salad bag approach, towards a more flexible and fit-for-purpose RWD strategy that leverages new options out there today.
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."
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.
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 increasingly leverage SDOH data to meet health equity requirements and enhance care delivery:
Payers’ consideration of SDOH underscores their commitment to improving health equity, delivering targeted care, and addressing disparities for vulnerable populations.
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:
By integrating SDOH data, CDPHP enhanced their services to deliver comprehensive care for its Medicare Advantage members.
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:
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:
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.
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.
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