Introduction
As Head of Data Strategy at Datavant, I help clients find partners with relevant real-world data (RWD) for linkage to their own health data or to create multi-partner linked datasets. Clients include pharmaceutical companies, health systems, government, payers, and data analytics platforms. Connecting data helps them create a robust picture of patient health to accelerate research, improve care, and lower healthcare costs. Clients come with questions like:
I’ve heard hundreds of client questions and learned a lot about our partners’ datasets. I’d like to share my experience and help others find data needed to improve patient health.
Healthcare is the Biggest Producer of Data
New health policies, healthcare technologies, and scientific discoveries have resulted in a massive increase in the volume and variety of health data. The 2009 HITECH Act accelerated the use of electronic health records (“EHRs”). The decreased cost of genomic sequencing and increase in biomarker-specific drugs has led to growth in genetic testing.1 Smartwatches, wearables, and health apps are now commonplace.2 The pandemic accelerated the use of sensors and remote technologies. Cloud and data science enable storage and analysis of data at scale.
RBC Capital Markets estimates that healthcare generates the largest volume of data. At a 36% compound annual growth rate by 2025, health data is growing faster than data in any other industry.3
Source: https://www.rbccm.com/en/gib/healthcare/episode/the_healthcare_data_explosion
New Health Data Analytics Companies Are Formed Each Year
With more health data, new analytics companies have sprung up to analyze it. From patient recruitment and clinical trial technologies to population health and value-based care analytics, thousands of organizations serve biopharma, device makers, payers, patients and providers.
As a result, Datavant now connects the largest health data ecosystem with thousands of data originators, data recipients and data platforms across the healthcare industry. As we head into 2022, here are the most common types of data our clients sought out last year and the questions they were trying to answer.
Health Data Demand Trend 1: Demographics and Social Determinants of Health (SDOH)
As COVID entered its second year, the problem of health inequity in America became salient.4 Many groups such as Blacks, Latinos, Asians, women and other vulnerable populations are neither adequately represented in research nor have equal access to quality care.5 Government,Life Sciences, health systems and payers all want to understand the factors associated with worse health outcomes in these groups to try and close these gaps.
Demographic variables include inherent characteristics in people (e.g., age, race, gender, and marital or family status), whereas SDOH are the conditions under which people live, work, and learn, etc. (e.g., education level and employment status). Datavant has numerous partners with demographic and SDOH data. The utility of specific SDOH attributes depends on the question a client is trying to answer. I’ll often work with clients to understand their health-related questions and help them find the best SDOH data variables to answer them.
Health Data Demand Trend #2: COVID Vaccination & Variant Data
Since the pandemic, data on COVID diagnoses, vaccination rates and variants have been in high demand and we’ve seen an uptick with the Omicron variant. Unfortunately, this data remains fragmented given the state-by-state approach to reporting COVID cases. Some of our largest data aggregator partners have vaccination status on just ~20% of all 200mm vaccinated individuals in the US, so linking data for a national view of U.S. patients is critical. Datavant lends its linking technology to support the NIH’s National COVID Cohort Collaborative (N3C). Our connectivity technology also supports the national Covid Research Database (CRDB), which is used by academic researchers and was recently recognized by the Reagan Udall Foundation with an award for Innovations in Science. If you are interested in the application process to use the CRDB you can find more details here.
Health Data Demand Trend #3: Oncology Patient Journey
Linking data to understand the full oncology patient journey is the most frequent type of linkage we see. The oncology patient journey is complex. Patients may present at a primary care office and be referred to an oncologist who orders labs such as a biopsy, diagnostic imaging or genetic testing. They may visit radiologists, undergo surgery, and take drug regimens that include infused and oral drugs. While survival is a key outcome in oncology studies, mortality data is not always captured in EHR datasets.6
Understanding the oncology patient journey often requires linking claims data including medical, retail pharmacy and specialty pharmacy claims, to EHR data from ambulatory care, integrated delivery networks, and community oncology clinics. In particular, clients struggle to find oncology EHR data from academic medical centers, which are hospital systems that are affiliated with a research university. Academic medical centers (AMCs) are very important care settings that conduct research, provide education, and deliver clinical care. They have access to cutting-edge technologies and can serve patients with rare cancers by offering advanced procedures like bone marrow transplants and novel drug trials. Clients seek pathology, radiology, labs, imaging and genetic data which necessitates linking data from multiple partners specializing in cancer. In addition to mortality data that covers more than 85% of weekly death events, the Datavant ecosystem includes many oncology-specific data partners:
— 5 community-focused oncology RWD providers,
— 3 AMC-focused oncology RWD providers,
— 8 genomic data providers (3 of whom also have germline testing data),
— 9 providers of ambulatory EHR data, and
— 6 of the top 10 AMCs in cancer.
Health Data Demand Trend 4: Specialty Drug Data
Specialty drugs dominate pharmaceutical company pipelines.7 These drugs are distributed by specialty pharmacies (SPs). Many SPs only provide usage data on that drug back to the drug manufacturer for Limited Distribution Drugs (“LDDs”). In such cases, the drug and often the entire drug class is not available to aggregators and represents a gap in commercial databases. To compensate, life science companies connect their 1st party SP data feeds to 3rd party datasets for a more complete picture of patients’ adherence to therapy and their drug’s utilization versus the competition.
Health Data Demand Trend 5: Rare Disease Patient Data
Rare diseases affect fewer than 200,000 people in the U.S.8 Rare disease patients often endure a diagnostic odyssey that runs 5-7 years. They may see more than 7 specialists before being properly diagnosed which creates a highly fragmented data journey.9 Lastly, drugs for these patients are primarily specialty pharmacy-distributed, so data is often unavailable within commercial datasets. Clients seek our help linking data across many partners to find patients for clinical trials and observational studies.
Health Data Demand Trend 6: Linking First-Party Data
Biopharma clients have begun linking internal 1st party data to external 3rd party data assets they license. First-party data includes clinical trial data, specialty pharmacy data, HUB services data, patient registries, sponsored testing data, digital engagement data, and other assets. Linking 1st party and 3rd party data creates a comprehensive view of patients’ health. For example, connecting data from HUB services to Rx claims can shed light on the time between a patient’s attempt to get on therapy to script fulfillment and ongoing adherence. Connecting clinical trial data to EHR and claims datasets extends data collected for long-term outcomes analyses. There has been significant demand in linking these pharma-proprietary data assets to create more value and insights from enterprise data.
Health Data Demand Trend 7: Enterprise Data Linking
One of the most exciting trends we are seeing with biopharma clients is an enterprise-level data strategy that includes tokenizing every clinical trial and health economics and outcomes research (HEOR) study. Tokenizing refers to de-identification of patient identifying information (PII) and assignment of a hashed, encrypted token to represent the patient. Centralized evidence generation teams are using the approach to maximize external partnerships as a data partner may have data that matches patients across several studies. Tokenizing all HEOR and clinical studies enables faster partner identification and negotiation of data for multiple studies at one time.
Summary: Implications for Health Data Users, Data Originators and Platforms
This year we expect to see growth in use cases that require granular clinical, genetic, pathologic, imaging, and biomarker features. Those seeking health data need to define clear criteria for the population of interest, the questions they’re trying to answer, and the use case. Preparing these in advance will accelerate finding a partner with the best data to meet those needs.
For data originators, aggregators and analytics providers, understanding health data demand trends can help them maximize the relevance of their data to answer key questions. Making tokenized data available for data users to run on-demand overlaps enables rapid identification of relevant partners that have shared patients. In early partnership diligence conversations, sharing information about data content, quality and curation advances partnerships quickly.
At Datavant, we believe that every decision in healthcare should be powered by complete patient data, and the more we can help partners find each other, the more we enable everyone’s effort to improve patient outcomes. I’ll be back soon with an update on trends in new data types (supply-side trends). In the meantime, feel free to reach out to me and provide feedback at su@datavant.com.
Edited by Elenee Argentinis, Head of Marketing, Datavant
This is the first in a two-part series on health data trends. Click here to read Part II: New Data Types in the Datavant Ecosystem.
Editor’s note: This post has been updated on October 20, 2022 for accuracy and comprehensiveness.
References:
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)
