MirrorHealth is the infrastructure that securely activates and standardizes clinical data. We mediate between healthcare providers and researchers to generate real-world and in-silico evidence without compromising privacy.
Through generative Artificial Intelligence and automated semantic standardization, we build bridges of trust and interoperability. We transform fragmented records into actionable clinical insights without data ever leaving its source infrastructure.
We align clinical data to meet the critical needs of the pharmaceutical industry, healthcare centers, and medical Artificial Intelligence developers.
Simulate patient cohorts, predict behaviors of synthetic control arms, and model complex clinical trajectories before, during, and after clinical trial development.
Convert heterogeneous clinical data and unstructured medical notes into secure, interoperable models ready for scientific research and care improvement through federated networks based on the OMOP-CDM standard.
Train, fine-tune, and validate your diagnostic or predictive models with synthetic populations of high biological fidelity. Ensure balanced, realistic datasets that are 100% free of patient re-identification risks.
We design systems that automate health data ingestion, standardization, and exploitation processes without extracting information outside the hospital firewalls.
# Declarative mapping specification
class_derivations:
Measurement:
populated_from: LabEvent
slot_derivations:
person_id:
populated_from: subject_id
measurement_datetime:
populated_from: charttime
value_as_number:
populated_from: valuenum
The traditional process of transforming clinical data (ETL) into common data models like OMOP is costly and fragile. In this paper, Alberto Labarga presents an AI agentic architecture that transforms standardization into a declarative and reproducible workflow driven by LinkML and BAML.
Analysis of how realistic synthetic populations and digital twins bypass recruitment bottlenecks and comply with EHDS.
Semantic standardization and interoperability across federated hospital networks using the OHDSI Common Data Model (CDM).
A paradigm shift replacing fragile imperative SQL/Python code with declarative YAML mappings generated by AI.
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We combine more than three decades of experience in medical technology and digital transformation for the pharmaceutical industry.
Get in touch with us to schedule a personalized demo of our clinical data space mediation. We will be glad to study your needs.