Health Economics and Outcomes Research (HEOR) plays an essential role in assessing the value of healthcare interventions by analyzing their costs, clinical effectiveness, and impact on quality of life. HEOR researchers leverage a variety of real-world data sources, including electronic health records (EHR), claims data, and mortality data, to draw comprehensive insights about healthcare practices and policies. These insights are crucial for making informed decisions that enhance patient outcomes and optimize healthcare spending. Historically, researchers have often been confined to analyzing data sources in isolation, which significantly limits their ability to capture the full patient journey and understand the interdependencies of various health interventions and outcomes.
The ability to link data sources, across an ecosystem such as Datavant’s, offers researchers unparalleled opportunities to extract valuable, multifaceted insights from disparate data sets. As HEOR relies on diverse data sources to form its analyses, the challenge of maintaining patient confidentiality while merging these sources becomes significant. This is where tokenization comes into play. Tokenization is a data security method that replaces sensitive data elements with non-sensitive equivalents, known as tokens, which have no exploitable value. This process allows data linkage across different databases in a manner that preserves privacy, ensuring compliance with regulations like HIPAA in the United States.
One pivotal application of data linkage in HEOR is the integration of claims data with mortality data. Claims data provides detailed records of the healthcare services patients receive, while mortality data indicates patient death dates and causes. By linking these datasets, researchers can evaluate the long-term effectiveness and survival outcomes of treatments across various patient populations. This is especially crucial for assessing life-extending treatments in chronic diseases, where understanding the real-world effectiveness of healthcare interventions can directly influence clinical guidelines and patient care strategies.
Another significant use case involves the linkage of EHR, claims, and pharmacy data. This integration provides a holistic view of a patient's medical journey, offering insights into diagnosis, treatment patterns, medication adherence, and clinical outcomes. For instance, by analyzing linked data, researchers can identify discrepancies between prescribed medications and actual patient adherence, assess the impact of medication on patient outcomes, and detect potential drug interactions. Such analyses are invaluable for improving medication management protocols and enhancing patient safety and treatment effectiveness.
A third use case is the linkage of internal primary data, such as data from clinical trials, with claims data. This linkage can enrich the understanding of clinical trial cohorts by providing additional insights into patients' pre- and post-trial healthcare utilization and outcomes. For pharmaceutical companies and healthcare providers, understanding the broader impacts of a drug or treatment beyond the controlled trial setting into the real world can improve market strategies, patient monitoring programs, and post-market surveillance efforts.
The linkage of real-world data sources using privacy-preserving methods like tokenization is indispensable in HEOR. It enables researchers to conduct thorough, multidimensional analyses while safeguarding patient privacy. The ability to seamlessly integrate diverse datasets opens up new vistas for understanding drug effectiveness, treatment patterns, and health outcomes in real-world settings. As healthcare continues to evolve towards more data-driven decision-making, the strategic use of data linkage in HEOR will be critical in shaping future healthcare policies and practices that are both effective and economically viable.
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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