Our story
Thirty seconds with EHRiva
Watch how we turn hospital data into action, in the words clinicians would say it.
AI-narrated concept video. Clinicians shown are AI-generated scenes for illustration.
Hospitals run on data, but most of it never reaches the people who could act on it. EHRiva gives care teams and hospital leaders AI agents that turn that data into action: less time on admin, faster decisions, smarter plans. Built with clinicians, tested before deployment.
GDPR-aligned · OMOP-standardized · on-prem or private cloud · humans always in the loop
Agent families and a research platform, one shared data layer , standardized to the international OMOP model, so the data under every agent is comparable, shareable, and research-grade. Every agent is tested on tasks written by real clinicians before it ever touches a patient, and every decision still ends with a human. We build with clinicians, not hand things to them: they tell us what to build, and they choose what goes live.
Agents that sit beside clinicians, drafting notes, checking protocols, suggesting the next best step, so more time goes to patients, not paperwork.
Agents that spot what's coming, readmission, deterioration, long stays , and quietly nudge the right person in time to act.
Researchers ask questions in natural language and get the data they need: EHR, lab results, and medical images assembled together, ready for analysis or model training.
We treat readiness as connected requirements, not a one-off cleaning task. Before any model is treated as evidence, the data behind it has to pass all five gates — and the same dataset can be fit for one question and unfit for another.
Here is how EHRiva scores every dataset on the shared OMOP data layer.
Person and visit keys join end to end, units and formats are consistent, and every ETL run is recorded and reconcilable against the source.
Example: a lab result whose order-line link is lost would silently vanish — so every run reports “events without a visit”.
Local codes, reference tables and free text are mapped to the OMOP standard vocabulary. A shared column name is not a shared clinical definition.
Example: the pharmacy string “Metformin 850 mg” must resolve to one standard drug concept before it can be compared across hospitals.
Observation periods bound when data exists, and every input for a prediction must have been available at the moment the prediction is made.
Example: an HbA1c drawn after the first prescription is not a baseline value for a pre-treatment decision.
Patient selection, measurement variability and missingness can distort the relationship a study wants to learn. More records cannot compensate for a mismatch between the data and the question.
Example: “patients with diabetes” is not one population — coded diagnoses and exposed prescriptions select overlapping but different people.
Inputs must exist when the decision is made, the outcome must be meaningful, and the population must reflect the people the tool serves.
Example: a readmission-risk estimate only helps if it exists at discharge time and targets the clinic it is meant for.
Watch how we turn hospital data into action, in the words clinicians would say it.
AI-narrated concept video. Clinicians shown are AI-generated scenes for illustration.
We plug into the systems you already use, the electronic health record (EHR), schedules, registries, and transform them into one clean, de-identified data layer on the international OMOP standard. No rip-and-replace.
You choose which agents go live. Each one is tested on real clinician-written tasks first, runs in your environment, and keeps a human in the loop.
Agents suggest, teams decide, results flow back. Readmissions, wait times, freed capacity, visible in one place, month after month.
Less admin, fewer surprises, more time with patients, and agents that explain themselves before they act.
Run on standardized, research-grade data, analytics your whole institution can trust, compare, and act on.
Ask in natural language, get multimodal data, EHR, labs, imaging, and build your medical AI in a secure research environment.
Every agent is gated on tasks written by real clinicians, we only ship what we can prove.
Our clinical language core works across languages and terminologies, one platform, wherever your hospital is.
We transform legacy hospital systems to the OMOP Common Data Model, the standard behind EMA's DARWIN EU, with FHIR for exchange.
GDPR-aligned, deployable on-prem or in your private cloud. Your data stays yours, always.
Headquartered in Istanbul, building for hospitals worldwide , starting with the two people who started EHRiva.
Ahmet lives in data. Big data, integrations, AI, he built the clinical text-to-SQL benchmark (paper in review at IEEE JBHI) that teaches our agents to understand hospital data.
Connect on LinkedIn ↗Suleyman works where care happens, with clinicians every day. Clinical methods and microbiology are his home turf; he makes sure every agent earns the trust it asks for.
Connect on LinkedIn ↗Join the waitlist, hospitals, clinicians, researchers, and partners welcome. We reply personally.
Or email hello@ehriva.com · © 2026 EHRiva · GitHub