Clinical Data Abstraction · AI + Human-in-the-loop
3 months of chart review. Done in 2 weeks.
IO extracts variables from unstructured EMRs automatically, your team reviews and confirms, and publication-ready TFLs are generated end-to-end.
Get StartedNo setup required · HIPAA-eligible See it in a 15-min demo
93–100%
Extraction accuracyValidated at ASCO 2024 · ESMO 2025
3,000+
EMR records processed
100%
Audit trail guaranteed
Case Study · Yonsei University Health System
"We built a 3,000-patient cohort and generated all TFLs in 2 weeks. It used to take 6 months."
Research Team, Yonsei University Health System Top-5 Cancer Center in South Korea, comparable in scale to MD Anderson
12×
Faster
3K
EMRs processed
Sound familiar?
"I came here to do research. Why am I fighting the data pipeline all day?" — The hypothesis was ready months ago, but you're stuck in chart review, waiting in the stats queue, and burning energy on format alignment.
Weeks lost to chart reviewUnstructured EMR cleanup is a full-time jobOutsourced CDA — months of wait, then re-QCMulti-site format alignment bottleneckOne tweak → rerun everything2-week stats team queue
Researchers aren't slow. The data pipeline is.
How it works
A platform built around how clinical data actually works
IO's AI Agent handles the full pipeline — from raw EMR to publication-ready analysis.
01🤖
Clinical Data Abstraction (CDA)
Unstructured EMR → structured research variables
▼
Key differentiator
IO's AI reads physician notes, pathology reports, and operative summaries — then automatically extracts the variables your study needs. IO combines LLMs trained on clinical text with a human-in-the-loop review layer.
Variables extracted automatically from free-text EMR fields
Expert review flags on low-confidence extractions
Full audit trail per variable, per patient
IO's CDA is not a black box. Every AI extraction is reviewable, correctable, and logged.
02🌐
Data Standardization
mCODE & FHIR native conversion
▼
IO's Agent converts extracted data into mCODE and HL7 FHIR standards — eliminating weeks of format alignment in multi-site RWE studies.
Native mCODE and HL7 FHIR support
Agent-driven alignment across institutions
Multi-site cohorts merged instantly
03📊
TFL Generation
Hypothesis → publication-ready output
▼
Run survival analysis, Cox regression, Kaplan-Meier curves, and subgroup analyses directly in IO — no stats team queue, no R scripts.
Publication-ready TFLs generated by IO's Agent
Agent-powered analysis workflow
One hypothesis change updates all outputs instantly
04🔍
Full Reproducibility
Every step logged and traceable
▼
Reviewers will ask how you ran your analysis. IO answers that question before they ask it.
Full automatic log from extraction → standardization → analysis
Version control per parameter
Methods section writes itself
Why not just use a CDA vendor — or build it in-house?
Speed, accuracy, scalability — IO is the only option that delivers all three.
Traditional CDA Vendors
In-House Team
IO
Speed
Weeks to months
Depends on bandwidth
Days
Accuracy
Manual abstractor
Manual + QC
AI + human review
Scalability
High cost per chart
Headcount-limited
Scales instantly
Auditability
Inconsistent
Variable
Full audit trail, always
Analysis
Data handoff only
Separate tools
End-to-end platform
Standards
Rare
Manual
mCODE & FHIR native
Specialty
Multi-specialty
All departments
Oncology-focused
Who uses IO
Who uses IO
You have hypotheses. You shouldn't need a 6-month pipeline to test them.
IO takes you from EMR to analysis-ready dataset — without waiting on abstractors, data teams, or stats requests.
What changes
Test a new hypothesis the day you have it. Generate a TFL before your next team meeting.
Before → After
Chart review→Stats request→2-week wait
avg. 6 months+
Enter hypothesis→Auto extract→Done
within 2 weeks
Chart review is the hardest, most undervalued part of clinical research.
IO doesn't eliminate your role — it eliminates the tedious parts. AI handles the initial abstraction. You review and correct where it matters.
What changes
Less time on manual data entry. More time on patient care and study coordination.
How your work shifts
↓Manual chart entry
↓Repetitive data alignment
↑High-judgment review work
↑Patient care & study coordination
RWE study timelines are driven almost entirely by data acquisition and abstraction speed.
IO compresses both. From site data to analysis-ready RWE dataset — automated, standardized, auditable.
What changes
Faster RWE generation. Shorter R&D cycles. More defensible datasets.
Supporting multiple PIs across multiple studies with limited staff.
IO is how you scale without headcount. Extraction, standardization, and analysis — across studies, across sites, in one platform.
What changes
One team supports multiple PIs simultaneously. Role-based access keeps multi-user environments secure.
Before → After
3-person team→2 PIs→Bottleneck
Repeated delays
3-person team→IO automation→5+ PIs
2.5× throughput · no new hires
Frequently asked questions
The questions researchers ask most about clinical data abstraction and IO.
What is clinical data abstraction (CDA)?+
Clinical data abstraction is the process of extracting structured data from unstructured medical records — including physician notes, discharge summaries, and pathology reports. It is required in most observational and retrospective studies, and is typically the most time-consuming step.
How does AI-powered CDA work?+
IO uses LLMs trained on clinical text to read and interpret unstructured records, then extract predefined variables automatically. Unlike rule-based NLP, IO understands clinical context — abbreviations, implicit negations, and variable documentation styles.
Is AI-powered CDA accurate enough for clinical research?+
IO uses a human-in-the-loop model. AI performs the initial abstraction, and human reviewers validate — especially for low-confidence or high-stakes variables. This hybrid approach achieves accuracy comparable to trained abstractors.
What is the difference between IO and a traditional CDA vendor?+
Traditional CDA vendors rely on manual abstractors — slow, expensive, hard to scale. IO automates with AI, then layers in human expert review for QA. The result: faster turnaround, full auditability, seamless analysis integration.
Does IO support mCODE and FHIR standards?+
Yes. IO converts extracted clinical data into mCODE and HL7 FHIR standards natively — enabling multi-site data harmonization without manual format alignment.
Is IO HIPAA-compliant?+
IO runs on AWS HIPAA-eligible infrastructure, with AES-256 encryption and de-identification applied by default.
What types of analysis can I run in IO?+
IO supports survival analysis (Kaplan-Meier, log-rank), Cox proportional hazards regression, subgroup analysis, descriptive statistics, and custom no-code workflows. Outputs are generated as publication-ready TFLs.
Security & Compliance
Built for medical data from the ground up
IO is not a general-purpose analytics tool adapted for healthcare. It was built specifically for clinical research environments.
🔐
AES-256 Encryption
At rest and in transit
🎭
De-identification Default
Applied to all patient data
☁️
AWS HIPAA-eligible
HIPAA-compliant infrastructure
📋
Full Audit Trail
Every extraction & analysis step
👥
Role-based Access
For multi-user research teams
You already have the hypothesis. Now you need the tool to test it.
See how IO works in a 15-minute demo. No setup fees, no abstractor contracts.