Developing open-source infrastructure for collecting, standardizing, and analyzing health data.
Healthcare systems generate vast amounts of valuable data that remain untapped due to fragmented technologies, disconnected systems, and multiple data standards.
Built on HL7® FHIR® and the OMOP Common Data Model, Data4Life provides the infrastructure required to support interoperable and reproducible clinical research worldwide.
Our solutions reduce technical complexity by bringing disconnected research processes into an integrated environment that is easy to deploy and manage.
Ongoing ProjectsHasso Plattner Foundation. Long-term funding allows us to develop open-source software for real scientific impact, independent of any commercial return.
Data4Life develops open-source software that streamlines the secondary use of health data, from collection and harmonization to quality assessment and analysis.
Through open standards and intuitive interfaces, our integrated ecosystem helps research teams prepare and assess fit-for-purpose Real-World Data (RWD) for specific research questions and generate reproducible Real-World Evidence (RWE).
Flexible deployment options support your organization's security, governance, and compliance requirements, including those related to GDPR and HIPAA, while enabling research across teams and institutions worldwide.
Collect Multimodal Health Data
Design and conduct clinical or epidemiological studies through a secure researcher workspace and participant mobile application. Capture patient-reported outcomes (PROs), questionnaires, wearables, sensors, and other multimodal data in HL7® FHIR® format. D4L Collect supports decentralized clinical trials with workflows designed for GCP-aligned research and ICH E6(R3) requirements.
Standardize and Analyze Healthcare Data
Transform source data into the OMOP Common Data Model, run analyses, and generate reproducible Real-World Evidence (RWE) in one integrated workspace. Data2Evidence brings ETL mapping, quality assessment, cohort definition, OHDSI analytics, secure governance, federated studies, and AI-assisted tools together, reducing manual handoffs and time to evidence.
Discover Research-Ready Datasets
Explore high-quality, multimodal health datasets for secondary research through a searchable data catalog. D4L Data Library makes data from research collaborations, institutional repositories, and curated public sources discoverable to accelerate reproducible evidence generation.
Advance Your Research Career
Receive free access to Data4Life software, cloud infrastructure, product customization, and hands-on scientific and technical support throughout your research. The D4L Impact Program also covers publication and conference travel costs for selected early-career researchers.
Time is critical in clinical research. Remove technical bottlenecks to investigate more ambitious research questions, work with more representative health data, and generate reproducible evidence that stands up to scientific scrutiny.
Behind our advanced technology is a multidisciplinary team of scientists and engineers working alongside researchers, healthcare organizations, life sciences companies, and global health authorities to address complex research questions.
Researchers & Academic Institutions
Design multicenter studies, collect multimodal health data, and generate reproducible RWE using open standards and OHDSI tools in one integrated ecosystem. Reduce time spent preparing data and focus on publishing high-impact research, securing competitive grants, and building international collaborations.
Healthcare Organizations
Transform routine clinical data into interoperable datasets that support observational research, quality improvement, and learning health systems. Benchmark outcomes, participate in multicenter studies, and generate evidence to continuously improve patient-centered care.
Pharma & Medical Device Companies
Conduct observational studies, build external control arms, and support HEOR using standardized RWD, including PROs. Generate fit-for-purpose evidence for PASS and PAES in Europe, FDA postmarketing requirements and commitments (PMRs and PMCs) for drugs and biologics, and post-approval studies and postmarket surveillance for medical devices.
Regulators & Global Health Authorities
Access and evaluate transparent, reproducible evidence to support regulatory assessment, benefit-risk assessment, health technology assessment (HTA), pharmacovigilance, and public health policy. Analyze RWD securely across institutions using internationally recognized standards while protecting patient privacy and data sovereignty.
Public trust remains a major barrier to the secondary use of real-world health data. Concerns about privacy, transparency, and commercial interests can limit people's willingness to participate in research and share their data.
Data sovereignty enables individuals to remain in control of how their data are collected, used, and shared, supporting transparent governance and secure collaboration across institutions and borders. Greater trust can increase research participation and improve the availability of representative health data.
Data4Life develops software solutions that support the ethical and transparent use of health data for research.
OHDSI (Observational Health Data Sciences and Informatics) is the world's leading open-science community for observational health research. Through open-source software, shared methodologies, and the OMOP Common Data Model, the community enables researchers to generate transparent, reproducible, and interoperable Real-World Evidence (RWE) at global scale.
As co-lead of OHDSI Germany, Data4Life contributes open-source software and technical expertise, supports the adoption and use of the OMOP CDM, and participates in international network studies and working groups. We connect researchers and institutions in Germany with the global OHDSI community to advance collaborative, reproducible research across borders.
Join The Journey
AI is only as good as the data behind it. Most health datasets currently used to develop AI models represent only a small fraction of the world's population. This lack of representation can introduce bias, reduce clinical performance, and limit the applicability of AI across diverse populations. Improving the diversity, quality, and representativeness of health data is essential to developing AI that is fair, reliable, and clinically useful for everyone.
Explore Our Solutions
Of the world's population is underrepresented in health AI.
ARDC and Data4Life are partnering to build digital health data infrastructure that strengthens research and improves global health outcomes through better use of data.
Read MoreData4Life and Remedi partner to develop an integrated, portable X-ray imaging solution paired with mobile point-of-care data collection, enabling decentralized clinical workflows.
Read MoreBook a personalized demo to explore our platforms, discuss your needs, and see how our software can support your organization.
