Medical research is beginning to make use of “real world data,” the data generated from everyday visits to doctors’ offices by normal patients. A new paper in The Oncologist provides a powerful demonstration of how this data can be applied to real studies.
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The gold standard of medical research has been “randomized controlled trials,” clinical trials that test a small group of patients with a particular treatment against a control group; this data is used both for drug approval and informing doctors’ treatment decisions. Increasingly, there has been interest in augmenting this approach with real-world data coming from visits by real-world patients.
Advocates of real-world data studies point to two major advantages to using RWD:
While real-world data studies are promising in theory, in practice there are a variety of major challenges to using this data. The data is messy (there is an incomplete view of the patient’s journey) and the lack of randomization challenges the experimental validity of results.
The Oncologist recently published a great study by Sam Khozin, et al, where real-world data was used to better understand outcomes for lung cancer patients taking one of two PD-1 inhibitors that were recently approved by the FDA. The study is exciting for three reasons:
Here, patients were selected based on a review of unstructured data derived from EHR systems, which supported a more representative and a more accurate approach than relying on cohort selection based on disease codes.
This is pioneering work. Real-world data is messy, but much of that messiness is hopefully solvable by pioneering studies like these and data analysts across the industry. We predict the continued advent of RWD will have major impacts on the industry: within five years, we expect that EHR vendors, claims clearinghouses and other clinical data stores will have adapted to make their data usable (and linkable) for real-world evidence.
As a result of the shift beyond clinical trials, pharma companies and contract research organizations (CRO) will rapidly evolve to include deep expertise in both real-world study design and the underlying data management necessary to execute.
Within ten years, at any given point in time, thousands of these retrospective real-world studies will be running concurrently, enhancing our knowledge of different therapies and treatment protocols. Further studies are underway, and patients will be better off as researchers make better use of the explosion of information happening in the real world.
Editor’s note: This post has been updated on December 2022 for accuracy and comprehensiveness.
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.
Explore how Datavant can be your health data logistics partner.
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