From question to evidence, with data you can trust.
An open analytics platform for clinical research. Use preloaded, research-grade real-world patient data across millions of patients — or bring your own.
Cost of hospital stayHealth spending trendsPharmaceutical utilizationWait times
Population Health & Equity
Health equity stratificationAvoidable deathsPatient-reported outcomes
Care Delivery & Workforce
LTC quality indicatorsWorkforce metricsContinuing care performance
Trust & Credibility
Validated at every step — from question to result.
We benchmark the pipeline at every checkpoint, from how we interpret your question to how we verify the final numbers. No black boxes.
01
Benchmarked by design
Every feature is validated against peer-reviewed research before it ships. We reproduce published results and compare outputs to ensure accuracy.
02
Validated on your data
Every dataset is different. Onboarding benchmarks validate the pipeline against your specific data structure and coding practices before you run a single query.
03
Fully auditable
Every query produces a complete audit trail: traceable, reviewable, reproducible. Every cohort definition and statistical choice is documented.
Validation Pipeline
Every stage is benchmarked against gold-standard references, published literature, and expert-curated annotations.
Bring your own data — or start on trusted real-world data.
Use your EHR, claims, registries or custom datasets in place. If your data isn't ready, start immediately on Medeloop's preloaded, research-grade real-world data.
"What are the treatment patterns for newly diagnosed Type 2 Diabetes patients aged 40–65 with commercial insurance?"
Mapping cohort
Running analysis
Generating output
Results · 3 min 42 sec
Cohort size2.8M patients
First-line metformin67.3%
GLP-1 initiation18.9%
All-payer claims · Jan 2020–present
Use Cases
Built for every research team.
Health Systems
Analytics on your own data
Run analytics on your own EHR and claims data with AI agents. Identify care gaps, benchmark outcomes, and drive population health programs, accelerating work your team can build on.
Pharma & Life Sciences
National-scale RWE on demand
Connect proprietary datasets for validated real-world evidence. HEOR studies, treatment pattern analysis, and comparative effectiveness at national scale.
CROs
Multi-client studies, one platform
Multi-client data analytics on a single platform. Run studies across client datasets with full isolation, audit trails, and publication-ready outputs for every engagement.
Academic Medical Centers
From IRB to publication, faster
Faster research on IRB-approved institutional datasets. Ask a question in plain English, review the plan, and publish the results, augmenting the work your research team already does.
Frequently asked questions
Does my data ever leave my infrastructure?
Never. Medeloop uses a federated execution model — the compute goes to your data, not the other way around. Your data stays in your infrastructure, and only aggregated, de-identified results are returned. No raw patient data ever leaves your walls.
Can I choose where Medeloop runs — my environment or yours?
Yes. Medeloop deploys in your environment (AWS, Azure, GCP, or on-prem) or in the Medeloop cloud; you pick based on your compliance, security, and operational preferences. In either configuration, queries run on your data and only results are returned.
What data sources does Medeloop Analytics support?
Medeloop works with EHR, claims datasets, disease registries, labs, and institutional databases, including custom schemas. If you have structured clinical data, Medeloop can run on it.
Do I need to convert my data to OMOP or another common data model?
No. Our semantic engine reads your data natively: no OMOP mapping, no CDM conversion, no months of prep. If you already have OMOP, I2B2, or another common data model in place, Medeloop works with those too.
Do I need to know SQL, R, or Python to use it?
No coding required to get started. You describe your research question in plain English and the agentic pipeline handles cohort definition, query execution, statistical analysis, and output generation. For technical teams, every step is fully inspectable, with editable code, full audit trails, and the ability to drop into Python for deeper customization.
How do you validate outputs?
Every stage of the pipeline is benchmarked against gold-standard references, published literature, and expert-curated annotations. We validate six checkpoints: query understanding, concept extraction, code mapping, cohort construction, statistical analysis, and result verification. A third-party validation paper is available on request.
What outputs does a completed study produce?
Each study produces a defined patient cohort, the full query execution log, statistical outputs (Kaplan-Meier curves, regression tables, descriptive statistics), data visualizations, and a manuscript-ready narrative report. Every output is traceable, reviewable, and reproducible, designed to hold up to peer review, IRB review, or board presentation.
How long does it take to run a study?
Most analyses complete in minutes. Complex multi-step studies with large cohorts may take longer depending on your data infrastructure. The agentic pipeline runs all steps automatically. You review the plan, approve it, and the system handles execution.
Is Medeloop Analytics the same as EvidenceKit?
Analytics is the platform. It runs on your own data, or on Medeloop's preloaded real-world data — 38 million de-identified patients from HealthVerity's all-payer claims dataset. If you don't have your own institutional data yet, the preloaded dataset is the fastest way to start.
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