Agentic Clinical Intelligence Platform

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.

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HOW MEDELOOP ANALYTICS WORKS

Evidence you can defend.

01 · ASK

Describe the question

Use plain language. Medeloop grounds clinical concepts against your data and drafts the protocol.

02 · ASSESS

Check feasibility

The agent checks cohort size, data coverage and statistical power before the study begins.

03 · REVIEW

Approve the plan

Edit cohorts, code sets, assumptions and statistical methods before anything runs.

04 · EXECUTE

Run on the data

Validated queries execute inside your environment, or on licensed data already available.

05 · EVIDENCE

Use the outputs

Receive traceable cohorts, statistics, figures and a manuscript-ready narrative.

Read Product Whitepaper

What you can analyze

Ask hard questions. Get answers that hold up.

A sample of what researchers ask, and the answers they get back.

Comparative Effectiveness

"Compare 30-day hospitalization for GLP-1 vs. SGLT2i in heart failure patients"

GLP-1 patients showed HR 0.77 for MACE vs. SGLT2i in adjusted analysis

Time to Treatment

"Median time from NSCLC dx to first-line immunotherapy by payer type?"

Median 47 days commercial vs. 89 days Medicaid; 23% never received treatment

Adherence & Persistence

"PDC for GLP-1 agonists in year 1, by age group?"

PDC 0.62 for 40–65 cohort; drops to 0.48 for 65+ in the first 12 months

Prevalence & Incidence

"Prevalence of T2D among adults 40–65 with commercial insurance?"

12.8% prevalence identified across 3.2M eligible patients with statistical confidence

Treatment Patterns

"% of RA patients switching methotrexate to biologics in 12 months?"

22.4% switched within 12 months; adalimumab most common biologic at 38%

Healthcare Utilization

"30-day readmission rates for COPD by payer type?"

Medicaid COPD patients showed 31% higher 30-day readmission vs. commercial

Cost & Burden of Illness

"Average annual expenditure for MS patients in first 3 years?"

$68,400 average annual cost in year 1; DMT costs represent 74% of total spend

Cohort Characterization

"Build NSCLC immunotherapy cohort with baseline comorbidities"

42,817 patients identified; 61% male, median age 67; 38% with comorbid COPD

Ready to run your own real-world evidence queries?

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A COMPLETE RESEARCH WORKSPACE

Every study produces work you can inspect, reuse and defend.

Defined cohorts

Index events, lookback, washout and censoring saved as versioned research objects.

Statistical results

Regression tables, survival curves and descriptive analyses with methods attached.

Review-ready figures

Clear visualizations for research teams, IRB review and leadership presentations.

Auditable report

A publication-ready manuscript linked to every cohort, code set and analytical choice — formatted and traceable for journal submission.

Testimonials & case studies

Trusted by researchers at
leading institutions

Case study

Neurology · Stroke

Weeksto hours.

Dr. Adnan Qureshi runs real-world evidence studies himself with Medeloop Analytics, without waiting on a bioinformatics team.

University of Missouri · Zeenat Qureshi Stroke Institute

3 to 4 hrsper round, down from weeks
5+ → 0rounds with an informatics team
2 papersone published, one accepted

Read the story

Professor and Vice Chair of Cardiac Surgery at Cedars-Sinai

Why Medeloop

Built for healthcare from the ground up

Not cobbled together from disconnected tools.

Clinical Rigor

Validated at every step — from clinical code selection to statistical output. Built for the standards your reviewers expect.

AI That You Control

Human-in-the-loop at every step. Full audit trails, editable research plans, and transparent reasoning.

No Harmonization Required

Our semantic engine reads your data natively — no OMOP mapping, no CDM conversion, no months of prep.

Your Data, Your Rules

Compute goes to the data. Whether it's your EHR, claims, or registries — data never leaves your walls.

Pricing

Start free. Scale when you're ready.

Get started on the platform today, and explore affordable pricing options inclusive of rich healthcare data.

Start for Free See full pricing →

Medeloop Analytics

From question to evidence. On any data.

AI research agents that work like an expert team — on your data, in your infrastructure. Federated execution means your data never leaves your walls.

Start for Free Read Product Whitepaper

Need data? Start with Medeloop's preloaded real-world data — 38M patients, ready now.

Research question

Pending

Evaluate feasibility

Pending

Research plan

Pending

Run analysis

Pending

The Medeloop Flow

From question to a network of research outputs.

An agentic loop with human oversight at every step. Queries run on your data and execute in your environment or ours, depending on your preference.

Your Data

Stays in your infra

EHR / EMR

Claims data

Registries

Labs

+ Custom sources

RESEARCH QUESTION

"What is the impact of Drug X on patients with Y?"

AGENTIC LOOP · DEPLOY ANYWHERE

Check feasibility

Assess data + study design

Draft study plan

Protocol + cohort design

Review + edit at any step Audit trail

Execute on your data

Runs in your environment, or Medeloop cloud

Deployment · You Choose

Your environment

Medeloop cloud

RESEARCH OUTPUTS

Publication

Impact of Drug X on Outcome Y

JAMA BMJ STROBE

Dashboard

Drug X

Standard care

Cohorts

102,813 patients

58.7

avg age

54%

female

All-payer

coverage

Platform Capabilities

Built for real-world evidence, on any data, in any environment.

Federated by design

Compute goes to the data. Your data never leaves your infrastructure.

Data-agnostic architecture

Works on EHR, claims, registries, or custom datasets. No CDM conversion required.

Semantic code engine

Maps research concepts to ICD-10, CPT, NDC, SNOMED, and every major coding system automatically.

Validated at every step

Built-in checks on code selection, statistical methodology, and output quality.

Human-in-the-loop reasoning

Editable research plans, full audit trails, and transparent agent actions.

Best model for every task

Uses the right AI model for each step — not locked to a single provider.

What you can analyze

Ask hard questions. Get answers that hold up.

A sample of what researchers ask, and the answers they get back.

Comparative Effectiveness

"Compare 30-day hospitalization for GLP-1 vs. SGLT2i in heart failure patients"

GLP-1 patients showed HR 0.77 for MACE vs. SGLT2i in adjusted analysis

Time to Treatment

"Median time from NSCLC dx to first-line immunotherapy by payer type?"

Median 47 days commercial vs. 89 days Medicaid; 23% never received treatment

Adherence & Persistence

"PDC for GLP-1 agonists in year 1, by age group?"

PDC 0.62 for 40–65 cohort; drops to 0.48 for 65+ in the first 12 months

Prevalence & Incidence

"Prevalence of T2D among adults 40–65 with commercial insurance?"

12.8% prevalence identified across 3.2M eligible patients with statistical confidence

Treatment Patterns

"% of RA patients switching methotrexate to biologics in 12 months?"

22.4% switched within 12 months; adalimumab most common biologic at 38%

Healthcare Utilization

"30-day readmission rates for COPD by payer type?"

Medicaid COPD patients showed 31% higher 30-day readmission vs. commercial

Cost & Burden of Illness

"Average annual expenditure for MS patients in first 3 years?"

$68,400 average annual cost in year 1; DMT costs represent 74% of total spend

Cohort Characterization

"Build NSCLC immunotherapy cohort with baseline comorbidities"

42,817 patients identified; 61% male, median age 67; 38% with comorbid COPD

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.

Benchmarked by design

Every feature is validated against peer-reviewed research before it ships. We reproduce published results and compare outputs to ensure accuracy.

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.

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.

Query Understanding

"Did we interpret the intent correctly?"

Concept Extraction

"Did we identify the right clinical concepts?"

Code Mapping

"Did we pull the right ICD-10, CPT, NDC codes?"

Cohort Construction

"Did we build the right analytical cohort?"

Statistical Analysis

"Did we apply the right methodology?"

Result Verification

"Are the outputs accurate and reproducible?"

Read Product Whitepaper

Our Data · Analytics + Data

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.

No integration required

HIPAA Expert Determination certified

Quarterly refresh

Get started free → Learn more →

HIPAA Expert Determination certified

Research question

"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.

Medeloop Analytics

See what your data can reveal.

Real-world evidence at the speed of a question.

Start for Free

From question to evidence, with data you can trust.

Evidence you can defend.

Ask hard questions. Get answers that hold up.

Every study produces work you can inspect, reuse and defend.

Trusted by researchers atleading institutions

Built for healthcare from the ground up

Start free. Scale when you're ready.

From question to evidence. On any data.

From question to evidence in five steps.

Ask

Clarify

Plan

Execute

Deliver

From question to a network of research outputs.

Built for real-world evidence, on any data, in any environment.

Federated by design

Data-agnostic architecture

Semantic code engine

Validated at every step

Human-in-the-loop reasoning

Best model for every task

Ask hard questions. Get answers that hold up.

Medeloop covers every major research category.

Clinical Outcomes & Safety

Spending & Resource Use

Population Health & Equity

Care Delivery & Workforce

Validated at every step —from question to result.

Benchmarked by design

Validated on your data

Fully auditable

Bring your own data — or start on trusted real-world data.

Built for every research team.

Analytics on your own data

National-scale RWE on demand

Multi-client studies, one platform

From IRB to publication, faster

Frequently asked questions

Does my data ever leave my infrastructure?

Can I choose where Medeloop runs — my environment or yours?

What data sources does Medeloop Analytics support?

Do I need to convert my data to OMOP or another common data model?

Do I need to know SQL, R, or Python to use it?

How do you validate outputs?

What outputs does a completed study produce?

How long does it take to run a study?

Is Medeloop Analytics the same as EvidenceKit?

See what your data can reveal.

Trusted by researchers at
leading institutions

Validated at every step —
from question to result.