Epic Cosmos brings together de-identified EHR data from participating Epic organizations into one of the largest clinical datasets available for research. For researchers, data scientists and AI teams, it offers scale that single health systems cannot match. It also comes with access rules, privacy controls and limitations that shape what projects are realistic. This guide explains how Cosmos works, who can use it, what it suits and how teams combine it with their own data. For wider Epic connectivity, see our Epic integration services. Planning a research or AI data project? Request a free data strategy review.
What Epic Cosmos Is
Cosmos is a data platform built by Epic in collaboration with health systems that use its EHR. Participating organizations contribute data, and in return their users can explore the combined dataset. Understanding this participation model explains both the platform’s scale and its access restrictions.
A community dataset from Epic organizations
Organizations choose to participate in Cosmos and submit data from their Epic systems. Hundreds of organizations contribute, creating a dataset covering a very large patient population across many care settings. The breadth supports studies on rare conditions and outcomes that local data cannot answer.
How data reaches Cosmos
Published descriptions explain that participating systems send data to Epic in encrypted clinical document formats, where it is deduplicated and stored in SQL databases. Contributing organizations work with Epic to configure submissions, including historical data and ongoing updates triggered by clinical events.
Privacy protections
Cosmos applies strong privacy controls. Line-level research data is expert-determined de-identified, and access occurs within secure, monitored environments. User actions are logged and audited. These protections let organizations share data for research while reducing re-identification risk for patients. Users also complete privacy training.
Tools for exploring data
Users explore Cosmos through Epic tools such as SlicerDicer for aggregate questions, while trained data architects can query de-identified data with SQL in a secure virtual environment. Insights can also surface inside Epic, supporting clinicians with information about similar patients.
Who Can Access Cosmos
Access to Cosmos is tied to participation. This is the most important fact for vendors and outside researchers, because it determines whether direct access is possible or whether collaboration with a participating organization is the only practical route to using the data.
Users at participating organizations
Clinicians and researchers at participating Epic organizations can request access according to their institution’s processes. Requirements often include local Epic accounts, training and approvals. Access levels vary, with broader exploration available to most users and SQL access reserved for trained roles.
Data architects and scientists
Some institutions designate data architects who use SQL within Cosmos’s secure data science environment to build datasets and support studies. These roles require additional training. Institutions may also face cost-sharing arrangements with Epic for certain funded research projects, so confirm local policies.
Outside researchers and vendors
Researchers or companies without a participating organization typically cannot access Cosmos directly. Common routes include collaborating with researchers at a participating institution or proposing study ideas through Epic Research channels. Vendors should plan partnerships rather than assume direct data access.
Institutional approvals
Even with platform access, studies usually need institutional review, governance approval and alignment with research policies. Build these steps into timelines. Clear documentation of study goals, data needs and privacy safeguards speeds approvals and supports responsible use of shared data.
What Cosmos Is Good For
Cosmos excels at questions that benefit from scale and diversity of care settings. Knowing its strengths helps research and AI teams choose suitable projects, while recognizing when local data, registries or other real-world data sources would serve better.
Hypothesis generation and feasibility
Researchers use Cosmos to test whether enough patients meet criteria for a study, explore patterns and refine hypotheses quickly. This speeds early research phases and reduces wasted effort on studies that local data could never support with adequate sample sizes.
Observational and outcomes research
Large, diverse populations support observational studies on treatment patterns, outcomes and disparities. Many peer-reviewed studies have used Cosmos data. Careful study design remains essential, because observational EHR data carries biases that must be addressed in analysis and interpretation. Involve statisticians early in study planning.
Rare conditions and subgroups
Single health systems often see too few patients with rare conditions for meaningful analysis. Cosmos’s scale allows researchers to study rare diseases and small subgroups. Combining aggregate exploration with careful line-level analysis supports more robust conclusions than local data alone.
Clinical insights at the point of care
Cosmos also supports clinicians through features that show outcomes for similar patients inside Epic workflows. These insights help inform decisions when guidelines offer limited direction. Clinical teams should understand data limitations before relying on these comparisons for individual patient care.
Limits AI and Research Teams Should Know
Cosmos is powerful but not unlimited. Understanding its constraints prevents teams from designing projects that cannot be completed or drawing conclusions the data cannot support. These limits matter especially for AI teams hoping to train models on large clinical datasets.
Single-vendor population bias
Cosmos includes only organizations using Epic and choosing to participate. Populations, care settings and regions may not represent the full healthcare system. Consider how this affects generalizability, and validate findings with other data sources when results will influence broad decisions.
Documentation variation and errors
EHR data reflects documentation habits, which vary across organizations and clinicians. Missing data, coding differences and errors affect analysis. Use data dictionaries, quality checks and sensitivity analyses to understand how documentation variation might influence your results. Document every data quality decision you make.
De-identification constraints
Privacy protections obscure details such as precise dates and locations, which limits certain analyses. Studies requiring exact timing, geography or linkage to outside data may not be feasible. Confirm data availability early using Cosmos documentation before committing to study designs.
Model development restrictions
Training AI models on Cosmos data depends on access permissions, environment capabilities and institutional and Epic policies. Data typically cannot leave the secure environment freely. AI teams should confirm what is permitted before planning model development around Cosmos. Get those confirmations in writing.
Combining Cosmos With Local and External Data
Most mature research and AI programs use Cosmos alongside local data and other real-world sources. Each dataset answers different questions. Designing a combined strategy helps teams move from broad exploration to validated, deployable insights within their own organizations.
Local data for deployment readiness
Models and findings must eventually work on local populations and workflows. Use Cosmos for exploration and comparison, then validate with local Epic data from Clarity or Caboodle. Local validation reveals performance differences that matter before clinical deployment. Repeat validation after workflow changes.
Standardized research models
Many institutions convert local data into common data models such as OMOP to support multi-site research. Our OMOP implementation services help organizations standardize data for collaborative studies alongside Cosmos-based exploration. Standardized data also makes it easier to compare local results with broader community findings and published studies.
Real-world evidence platforms
Life sciences and research teams often combine EHR data with claims, registries and other sources for real-world evidence. Our real-world evidence platform work shows how these datasets are integrated, governed and analyzed responsibly. Each platform includes clear lineage, access controls and documentation for regulators and reviewers.
Governance across datasets
Combining datasets requires consistent governance covering approvals, privacy, access and documentation. Establish clear policies for how each source may be used and shared. Our healthcare AI governance framework supports responsible use of data in AI programs. Review policies as programs expand.
How Taction Helps Research and AI Teams
Taction has delivered 200+ healthcare projects, including data platforms, research infrastructure and AI solutions built on clinical data. We help research teams and health systems combine Cosmos insights with local data, build compliant pipelines and develop models ready for real-world deployment.
Free data strategy review
Share your research or AI goals, data access and current infrastructure. We assess feasibility, recommend how Cosmos, local data and external sources fit together, and outline governance needs. You get a practical plan. Request your free review. We also flag quick feasibility checks.
Local research data platforms
We build research data platforms using Epic reporting data, common data models and secure analytics environments. These platforms complement Cosmos, letting teams validate findings locally and develop models with data they control under institutional governance. Every platform includes access controls and audit logging.
AI model development and validation
We develop and validate clinical AI models using local and approved external data, with careful attention to leakage, bias and deployment readiness. Every model includes documentation that supports clinical review, governance approval and ongoing monitoring after deployment. Bias testing is included in every project.
Data scientists on demand
Need additional research capacity? Hire healthcare data scientists experienced with EHR data, observational research and clinical AI. They work within your governance processes and document methods thoroughly for reproducibility. Engagements scale with your research roadmap, from a single feasibility study to a full clinical AI program.
Frequently Asked Questions
These answers address common questions about Epic Cosmos for research and AI. Because access rules and capabilities evolve and vary by institution, confirm details with your organization’s Cosmos contacts and research office before planning projects.
What is Epic Cosmos?
Epic Cosmos is a research data platform combining de-identified EHR data contributed by participating Epic organizations. Users at those organizations can explore the dataset with Epic tools, supporting research, feasibility studies and clinical insights across a very large patient population.
Who can access Epic Cosmos?
Access is generally available to users at participating Epic organizations, subject to institutional requirements, training and approvals. Outside researchers and vendors typically need to collaborate with a participating organization or propose studies through Epic Research rather than accessing the data directly.
Is Cosmos data identifiable?
Research data in Cosmos is protected by strong privacy controls, including expert-determined de-identification for line-level analysis in secure environments. User activity is monitored and audited. These protections allow research while reducing re-identification risk for patients. Users also complete required privacy training.
Can we train AI models on Cosmos data?
It depends on permissions, environment capabilities and institutional and Epic policies. Data generally cannot leave secure environments freely. Confirm what is permitted with your Cosmos contacts before planning, and consider local data for model development and validation. Get those confirmations in writing.
What are the main limitations of Cosmos?
Key limitations include single-vendor population bias, documentation variation, de-identification constraints on dates and locations, and restrictions on data export. Careful study design, quality checks and validation with other data sources help address these limitations. Confirm data availability early using Cosmos documentation.