Electronic health record (EHR) data is collected during the ordinary course of a patient’s interactions with the healthcare system. It encompasses a wide range of information collected during patient care and medical research, including:
EHR data typically go beyond standard clinical data collected by a healthcare provider and include a broader view of patient care. EHRs are built to share information with other healthcare providers, such as laboratories and specialists, so they contain information from all the clinicians involved.
With technological advancements and guidance from the FDA, common themes surrounding EHR data are emerging.
Combining EHR data with real-world data provides a wealth of information. It offers researchers an opportunity to uncover new findings and improve patient care. Key applications of clinical data include:
Emerging uses for EHR data center around improving clinical trial efficiency through faster patient identification or synthetic control arms, pharmacovigilance and safety monitoring, and applying AI to create models to predict disease or outcomes. EHR software companies, health systems, and specialty provider networks are becoming active collaborators in supporting research initiatives, expanding choice of data partner beyond traditional EHR data aggregators.
Researchers use clinical data to evaluate the effectiveness and safety of new treatments, medications, or medical devices. By analyzing patient outcomes, adverse events, and other relevant variables, researchers can assess the impact of interventions and make evidence-based recommendations.
Situation: In a Phase IV study, a biopharmaceutical company assessed a newly approved anti-seizure medication as adjunctive therapy for focal seizures. The results showed reduced seizure frequency and high patient retention, indicating potential benefits for managing focal seizures.
Need: The client aimed to gain insights, focusing on several key aspects:
Solution: The biopharmaceutical company de-identified and linked EHR data with claims data using Datavant’s data connectivity technology. This empowered the company to:
Clinical data facilitates comparative effectiveness research, where clinical outcomes of different interventions or treatment strategies are compared to determine which provides the best results for specific patient populations. This research aids in making informed decisions about treatment options and resource allocation.
Situation: A biopharma company wanted to evaluate clinical and economic outcomes associated with lower disease activity states for patients with rheumatoid arthritis.
Need: The company needed to understand whether disease activity in rheumatoid arthritis was associated with adverse events, specifically hospitalization, emergency department visits, mortality, and medical costs.
Solution: By connecting clinical data from a disease registry with claims data, the biopharma company was able to conduct clinical and economic assessments. This resulted in:
Real world evidence studies utilize clinical data to assess how interventions perform in real-world settings, beyond the controlled environment of clinical trials. This helps bridge the gap between efficacy and effectiveness and provides insights into how treatments work in diverse patient populations.
Situation: A top 5 pharma company aimed to conduct a clinical study for an experimental treatment in a solid tumor indication. They needed to establish an external control arm to support the clinical study.
Need: The pharma company needed access to EHR data. This would empower them to compare real-world outcomes with those enrolled in the clinical trial.
Solution: The pharma company accessed data from three electronic medical records through the Datavant ecosystem. This allowed them to analyze the patient population, compare outcomes, and evaluate real-world effectiveness. The external control arm enhanced the standard of care, resulting in:
Understanding patients through clinical data can help accelerate trial recruitment. By analyzing the characteristics and outcomes of participants in previous trials, researchers can identify potential candidate pools and optimize recruitment strategies for patients that can be found in real-world settings. This improves enrollment rates, reduces recruitment time, and ensures a diverse and representative participant population.
Situation: Recruiting patients for Nonalcoholic fatty liver disease (NAFLD) and Non-alcoholic steatohepatitis (NASH) trials is challenging due to the need for both lab test results and specialized liver imaging, which are often collected separately.
Need: A pharma company needed to link patient data from labs and imaging centers to confirm NAFLD and NASH diagnoses and recruit eligible patients for their trial.
Solution: By connecting blood tests from labs to imaging results, the company was able to confirm a NALFD or NASH diagnosis. The company was able to streamline clinical trial recruitment at sites with a higher concentration of NAFLD and NASH cases.
Privacy and data protection are paramount when working with clinical data to ensure confidentiality and comply with ethical guidelines. Here are some measures that should be implemented:
Organizations can access a wealth of EHR data through the Datavant ecosystem. Datavant connects disparate data sources, empowering organizations to gain a comprehensive view of patient populations, uncover new insights, and drive better outcomes.
Datavant ecosystem provides access and connectivity to various data sources, including:
Clinical data holds immense potential in transforming healthcare and addressing health disparities. With Datavant, healthcare organizations can securely access EHR data, empowering them to make data-driven decisions and improve patient outcomes.
Researchers and organizations can also access electronic health records through the following avenues:
Clinical data offers a trove of insights and possibilities for advancing medical knowledge and improving patient outcomes, driving medical innovation and enhancing evidence-based practices.
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.
Contact us
.svg)
