5 ways Datavant is using AI [Case Study] [2026]

In a world where healthcare data is exploding in volume and complexity, Datavant stands out as a trailblazer in harnessing artificial intelligence (AI) to unlock its full potential. As healthcare systems strive to deliver better outcomes, reduce costs, and personalize care, the ability to connect disparate data sources securely and meaningfully becomes mission-critical. Datavant’s mission—to connect the world’s health data to improve patient outcomes—finds its strongest ally in AI technologies that are transforming everything from patient record linkage to public health surveillance. AI enables Datavant to address some of the healthcare industry’s most stubborn challenges: fragmented data silos, inefficient clinical trial recruitment, lack of longitudinal patient insights, and persistent privacy concerns. Through its AI-driven platforms and partnerships, Datavant empowers stakeholders across the healthcare continuum—including providers, payers, life sciences companies, and public health institutions—to make faster, smarter, and safer decisions based on comprehensive, real-world data. This article dives into five real-world case studies that showcase how Datavant is deploying AI to drive measurable improvements in healthcare outcomes, data usability, and system-wide collaboration. Each example presents a clear challenge, an AI-powered solution, tangible results, and critical takeaways that reflect the growing role of intelligent technology in shaping the future of health data connectivity.

 

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5 ways Datavant is using AI [Case Study] [2026]

Case Study 1: Enhancing Patient Data Linkage Across Silos

Challenge

Healthcare data remains notoriously fragmented. Across the U.S. and globally, critical patient data is scattered across hospitals, clinics, labs, insurance providers, and digital health apps. This fragmentation results in incomplete patient profiles, duplicative testing, and compromised care continuity. For Datavant, the challenge was clear: how to link de-identified patient data across disparate systems without violating privacy or compromising compliance standards. Traditional deterministic or rule-based matching methods often failed due to slight variations in identifiers or missing data points. With more than 70% of clinical decisions reliant on accurate longitudinal data, this gap posed a serious threat to quality care and data-driven innovation.

Furthermore, Datavant’s clients—ranging from health systems and payers to life sciences companies—needed reliable data linkage at scale. The challenge wasn’t just about matching patient records correctly but doing so across millions of records, in near real time, and with a high degree of accuracy and privacy protection.

 

Solution

Datavant applied machine learning-powered entity resolution algorithms to develop its proprietary tokenization and data linkage platform. Instead of using personally identifiable information (PII) directly, Datavant’s solution uses cryptographic tokens derived from de-identified data inputs. AI models, particularly natural language processing (NLP) and supervised learning classifiers, were used to predict probabilistic matches by understanding common misspellings, name permutations, and address variations. These models learned from historical linkage data to continuously improve their predictions, leading to more accurate and scalable linking.

Unlike conventional approaches, Datavant’s AI system adapts to the context of data input—from structured EHRs to unstructured provider notes—and evaluates hundreds of variables and relationships to establish matches. These machine learning models are rigorously trained using gold-standard datasets and validated through real-world audits. The platform’s modular design also allows clients to use AI linkage in private environments, complying with HIPAA and other regulatory requirements.

 

Result

The implementation of Datavant’s AI-powered linkage platform resulted in a 95% match rate across various healthcare data sources—significantly higher than legacy systems. Health systems were able to reduce duplicative records by 40%, thereby improving both operational efficiency and patient care continuity. For one major health plan, the AI-enhanced linkage identified over 1 million previously unlinked member records, streamlining claims processing and improving risk stratification models.

More importantly, Datavant’s solution enabled healthcare organizations to construct longitudinal patient records while preserving privacy and security. These unified records allowed researchers to track disease progression more accurately, identify high-risk patients earlier, and improve the quality of real-world evidence (RWE) used in drug development and epidemiological research.

 

Key Takeaways

AI is revolutionizing data linkage by surpassing the limitations of rule-based matching. Datavant’s success lies in using privacy-preserving machine learning algorithms that adapt to heterogeneous data. This case study highlights the importance of linking patient data not only for individual care but also for systemic improvements in healthcare research, reimbursement, and regulatory compliance.

 

Case Study 2: Accelerating Clinical Trial Recruitment and Diversity

Challenge

Recruiting participants for clinical trials is time-consuming and often biased, with more than 80% of trials in the U.S. failing to meet recruitment timelines. Minority groups, rural populations, and older adults are frequently underrepresented in trials due to lack of access, awareness, or connectivity with research institutions. Pharmaceutical sponsors, CROs (Contract Research Organizations), and regulators alike face challenges in identifying eligible patients who meet narrow inclusion and exclusion criteria.

Datavant identified this as a critical issue: how can AI be leveraged to enhance clinical trial matching and ensure diverse representation in studies? Additionally, the industry required a compliant solution that could tap into real-world datasets—claims, EHRs, and patient registries—without risking patient confidentiality.

 

Solution

Datavant partnered with multiple trial sponsors and real-world data (RWD) providers to deploy AI-driven cohort discovery tools. The company used deep learning and NLP models to analyze unstructured clinical data, extracting relevant phenotypic and biomarker information from patient records. These models parsed physician notes, lab reports, and imaging summaries to construct comprehensive patient profiles beyond structured diagnosis codes.

The AI then scored and ranked patient eligibility against trial criteria using fuzzy logic and adaptive learning. The algorithm was trained using historical trial data to improve precision and recall in identifying eligible patients. Furthermore, Datavant’s federated data model allowed trial sponsors to access de-identified patient pools across hundreds of partner sites—ranging from community clinics to academic hospitals—without ever centralizing sensitive data.

Datavant also built AI-powered visualization dashboards to help sponsors and CROs identify recruitment gaps, compare site performance, and simulate recruitment outcomes under various demographic strategies.

 

Result

The AI-powered recruitment tool cut trial startup time by 45% on average. In one prominent oncology trial, Datavant’s solution enabled the sponsor to recruit a racially diverse participant pool in record time—fulfilling FDA diversity expectations ahead of schedule. For another decentralized clinical trial, Datavant helped identify eligible participants across rural geographies, increasing regional coverage by 300%.

 

AI not only improved trial enrollment speed but also ensured ethical representation and regulatory alignment. Sites using Datavant’s AI platform reported higher retention rates, likely due to more accurate patient-to-trial matching. Additionally, sponsors reduced overall recruitment costs and saw improved outcomes in early-phase feasibility studies.

 

Key Takeaways

AI is transforming clinical research by enabling smarter, faster, and fairer trial recruitment. Datavant’s innovation lies in its use of deep learning and federated analytics to match patients to trials across diverse populations and fragmented systems—without compromising data privacy. The result is more inclusive and efficient research that meets modern regulatory and scientific expectations.

 

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Case Study 3: Optimizing Health Data Exchange for Public Health Surveillance

Challenge

During public health emergencies like pandemics, timely access to high-quality health data is crucial for surveillance, resource allocation, and policymaking. However, many public health agencies operate with outdated infrastructure and face data fragmentation, reporting delays, and inconsistent formats. During the COVID-19 pandemic, these gaps became starkly visible, with inconsistent testing data, underreported outcomes, and slow contact tracing hampering effective response.

Datavant saw an urgent need to support federal, state, and local health authorities in aggregating real-time data from various providers, labs, and government sources. The challenge was to build a reliable, privacy-safe, AI-enhanced data exchange infrastructure that could facilitate near real-time surveillance and response.

 

Solution

Datavant collaborated with the U.S. Department of Health and Human Services (HHS) and leading public health partners to implement a national COVID-19 data exchange framework. AI algorithms were deployed to ingest, cleanse, normalize, and validate data from disparate sources—including lab results, claims data, hospital records, and mortality registries.

Using machine learning models, Datavant’s platform harmonized data formats, corrected anomalies, flagged inconsistencies, and enriched missing values. NLP models were used to extract relevant clinical variables from unstructured public health reports, and geospatial AI was integrated to map outbreaks and community spread. These AI layers sat on top of Datavant’s secure tokenization network, ensuring that no identifiable patient information was shared.

Real-time dashboards powered by AI provided policymakers with visual insights into infection hotspots, hospitalization trends, and population risk stratification—enabling faster, more targeted interventions.

 

Result

Datavant’s AI-enabled surveillance system became a backbone for several COVID-19 response initiatives. The company helped state departments reduce data reporting delays by over 60%, significantly improving decision-making speed. In one case, Datavant’s platform flagged an emerging outbreak cluster in a mid-sized city two weeks before manual reporting systems would have caught it—enabling rapid containment actions.

By providing standardized, analyzable data at scale, Datavant also supported academic institutions and public health researchers in modeling virus transmission dynamics, vaccine efficacy, and policy impact. The solution has since been extended to influenza and RSV surveillance efforts, proving its long-term value for public health infrastructure modernization.

 

Key Takeaways

Public health surveillance requires speed, accuracy, and interoperability—three areas where AI excels. Datavant’s contribution during COVID-19 showcased how AI can strengthen emergency preparedness and response by enabling real-time, privacy-safe data exchange. Its impact extends beyond pandemics, laying the groundwork for a smarter, more responsive public health system.

 

Case Study 4: Enabling Real-World Evidence Generation for Life Sciences

Challenge

Real-world evidence (RWE) is becoming central to drug development, regulatory submissions, and post-market surveillance. However, life sciences companies often struggle with sourcing and analyzing real-world data due to inconsistency, fragmentation, and lack of access to longitudinal patient journeys. Moreover, manual curation of data for observational studies or health economics outcomes research (HEOR) is time-consuming and prone to bias.

For pharmaceutical companies looking to generate credible, regulatory-grade RWE, the challenge is twofold: first, integrating and standardizing datasets from claims, EHRs, labs, and registries; and second, analyzing them efficiently without compromising scientific rigor or patient privacy.

 

Solution

Datavant introduced an AI-powered RWE engine capable of linking, curating, and analyzing diverse data types. Its linkage algorithms established longitudinal records across millions of patient journeys, while AI-driven data transformation pipelines converted raw, messy inputs into clean, analysis-ready datasets.

 

The company integrated causal inference modeling, AI-powered cohort analytics, and unsupervised learning methods to detect population-level treatment patterns and safety signals. These models could automatically suggest relevant covariates, control groups, and statistical adjustments to minimize bias. Researchers were also provided with explainable AI dashboards to explore variable interactions, subpopulation differences, and time-to-event outcomes.

To ensure regulatory readiness, Datavant collaborated with leading biostatisticians and regulatory experts to validate its AI models under frameworks such as GCP, GVP, and 21 CFR Part 11.

 

Result

One top-10 pharmaceutical company used Datavant’s AI platform to analyze treatment patterns in patients with rare autoimmune diseases. What would typically take 12 months of data curation and cleaning was accomplished in just eight weeks. The resulting RWE supported successful label expansion in several global markets, bolstered by clear, reproducible analytics.

Another biotech firm used the platform to conduct a comparative effectiveness study for a new therapy and discovered a statistically significant advantage over standard-of-care treatment—previously undetected in clinical trials. This insight informed pricing, reimbursement, and physician outreach strategies, ultimately increasing market adoption.

 

Key Takeaways

RWE is only as good as the data behind it. Datavant’s AI capabilities bridge the gap between data acquisition and regulatory-grade analytics, enabling life sciences companies to accelerate evidence generation. This case study demonstrates how AI improves the scientific validity, speed, and scalability of observational research and post-market surveillance.

 

Case Study 5: Strengthening Privacy and Compliance in Data Partnerships

Challenge

As healthcare data partnerships expand across payers, providers, tech companies, and research institutions, privacy and compliance risks have multiplied. Organizations fear data breaches, reputational harm, and regulatory penalties under HIPAA, GDPR, and other data privacy frameworks. Traditional data-sharing models often involve raw data transfers and centralized repositories, creating vulnerable points of failure.

Datavant recognized a systemic trust gap. The challenge was to develop a secure AI-supported architecture for data sharing that minimized re-identification risks while allowing analytics and linkage at scale. Clients needed assurance that their sensitive health data could be used for good—without compromising security.

 

Solution

Datavant’s AI-based privacy-preserving technology focuses on secure tokenization, differential privacy, and privacy-enhancing computation. Each partner organization retains data locally, and AI models are deployed in federated environments—meaning computations happen where the data resides, with only aggregate or encrypted outputs shared.

Advanced AI algorithms are used to scan for residual risk of re-identification and continuously update threat models. The system also applies anomaly detection to monitor unusual access or behavior patterns that might signal a privacy breach attempt. Compliance dashboards allow legal and compliance teams to review audit trails, risk scores, and access permissions in real-time.

 

Datavant’s solution is also regularly stress-tested through third-party privacy audits and aligned with NIST and ISO frameworks.

 

Result

Multiple health systems, including some of the largest academic medical centers in the U.S., adopted Datavant’s platform to collaborate with pharmaceutical firms, data analytics vendors, and payers. These partnerships resulted in over 500 million de-identified patient records being securely tokenized and linked for research and analytics—without a single recorded privacy breach.

AI-enabled risk monitoring helped one insurer detect and neutralize a credential abuse attempt before it escalated. Meanwhile, compliance reporting that used to take weeks was reduced to minutes with AI-enhanced dashboards and smart logs.

 

Key Takeaways

Trust is the foundation of health data innovation. Datavant’s use of AI to strengthen data privacy and compliance demonstrates that advanced analytics and ethical data stewardship can coexist. By designing with privacy at the core, Datavant empowers organizations to collaborate without compromising security.

 

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Closing Thoughts

Datavant’s integration of AI into healthcare data connectivity is not just innovative—it’s transformative. By applying advanced machine learning, natural language processing, and privacy-preserving technologies, Datavant is solving critical challenges in data linkage, clinical research, public health surveillance, real-world evidence, and data governance. These real-world case studies highlight the company’s ability to scale AI solutions responsibly, driving both operational efficiency and patient-centered outcomes. As the healthcare ecosystem becomes increasingly data-driven, Datavant offers a compelling blueprint for how AI can securely bridge gaps, enhance decision-making, and foster meaningful collaborations across the entire healthcare value chain.