Trusted
Every output is anchored in source data and validated steps, not model intuition.
Making patients computable.
RespondHealth turns medical records and clinical notes into an analysis-ready model of the patient, that you can explore, analyze, and reason over, with every fact traced to its source.
We turn the messy reality of the electronic health record into a single, auditable model of the whole patient. A knowledge graph fuses a coded chart with millions of findings read from clinical notes, and the biology the records never state. A self-supervised foundation model learns all of it and predicts where a patient is heading, then runs in reverse to generate synthetic cohorts, trial arms, and disease subtypes.
Every answer traces back to its evidence.
One platform. Three integrated layers of clinical reasoning.
Think of it as a dynamic GPS for the patient.
Context
Context is the map.
The map is medical knowledge: ontologies, literature, regulatory labels, ecological data.
Observed
Observed is the current status.
A Knowledge Galaxy of where patients have been and are currently, drawn from longitudinal records.
Expected
Expected is the route ahead.
The foundational model generates probabilities of where patients are heading.
RespondHealth Studio
An analytics platform with a Knowledge Galaxy™ at its core.
Clinical data from any source. Organized into one connected Knowledge Galaxy™. With a clinical research agent on top. Reducing 1 year timelines to 1 week.
Clinical Research Agent
Reaches into context, observed, and expected for each question.
Foundational Patient Model
Predicts patient trajectories from the longitudinal record.
Expected
In R&D
At scale
Real-world depth, not just volume.
Longitudinal records that carry scientific weight, with the unstructured layer that other tools cannot read.
2B+
Patient records in the data lake
40,000+
Provider sites covered
35M+
Patients used to train and validate the platform's models
~80%
Of clinical knowledge lives in unstructured notes
36
EHRs integrated
How we build
Trusted. Transparent. Traceable.
Three commitments sit underneath everything the platform does, by design and from the start.
Transparent
Users see the code, the intermediate tables, and the path from raw note to final result.
Traceable
Every claim links back to its source span in the original record.
What makes us different?
Analysis-ready data at scale.
Your Knowledge Galaxy™.
- Multidimensional data.
- The full patient journey.
- Grounded in medical knowledge.
Built for your research workflow.
Your clinical research agent.
- Decides what tool to use for your question.
- Works inside your existing workflow.
- You stay in the loop.
State of the art accuracy.
Trustable, traceable, transparent.
- No black box.
- Always reviewable & reproducible.
- Every claim is source-linked.
Who it's for
Insights for clinical researchers at speed and scale.
Pharmaceutical & Biotech
Label expansion, post-market surveillance, treatment-response subgroup analyses, comparative effectiveness, and rare-disease characterization.
Academia
Investigator-initiated studies, grant-supported work, longitudinal cohorts, and manuscripts where reproducibility and citation-level evidence matter.
Health Systems & Providers
Surface clinical complexity hidden in narrative notes. Improve revenue capture and care management without adding physician burden.
Defensible evidence for label, market, and regulator.
Studio is the engine. Cohort answers and evidence packages in days. Every concept traceable to source, built to hold up under FDA review, peer review, and contract audit.
Patient finding and cohort discovery
Identify patients who match clinically meaningful definitions, including criteria locked in unstructured notes that structured-only tools miss.
Treatment response and subgroup analysis
Evaluate response across longitudinal trajectories. Find the subgroup where the signal lives and validate it with conventional statistics.
Label expansion and post-market surveillance
Real-world data packages built to match FDA expectations for source data, validation, and reproducibility.
Comparative effectiveness
Head-to-head comparisons across treatments, with cohort definitions, code, intermediate tables, and outputs all reviewable.
Rare-disease characterization
Assemble defensible cohorts in disease areas where structured data alone is sparse, by extracting from the notes that contain the diagnosis.
Health economics and outcomes research
Outcomes, utilization, and cost analyses linked across EHR, claims, and other sources via privacy-preserving tokenization where needed.
Talk to our team.
Whether you are scoping a study, building an evidence package, or evaluating a platform, we would like to understand your question.
Get in touch