Our technology is capable to unlock variables among millions of data points, facilitating patient recruitment in observational studies by swiftly pinpointing the most relevant factors from vast datasets.

Conta​​ct u​​​​s​​​​​​

Observational studies are essential in medical research, allowing the analysis of the natural progression of diseases and the effects of treatments on real populations.

IOMED's approach.

We are leaders in Natural Language Processing applied to medical data, and we become a strategic ally to structure and  standardize  information from a wide range of hospital data sources to find relevant variables. Efficiently and rapidly, our technology identifies variables which meet the specific criteria for the study.

Our technology.

Automated Concept 

Our technology can automate mapping and structuring data from all hospital data sources, including laboratory results, radiology procedures or pharmacy information. This automated process streamlines the structucturing of diverse data formats into a standardized model

Common Data Model.

Our technology standardizes all hospital data into OMOP Common Data Model. Disposing of standardized data allows healthcare organizations to harness its potential through collaborations with healthcare players and build a data-driven healthcare ecosystem.

Natural Language 

Our AI-powered technology and Natural Language Processing systems can comprehend free-text inputs from clinical notes and extract relevant information contextually. It's not just about keyword extraction, it's about a deep understanding of the information, ensuring its high-quality through a double quality assurance.

Data Federation 

Our federated data model ensures robust data security by keeping all data within hospital's facilities. This empowers hospitals to engage in research and collaboration while maintaining the utmost data protection, compliant with GDPR.

Significant outcomes.

Accelerated Data 
Collection for CRF.

One of the key outcomes of using IOMED's Natural Language Processing technology in observational studies is a remarkable acceleration in the patient recruitment process. By efficiently and rapidly identifying relevant variables from millions of data points that meet the study criteria, IOMED streamlines the recruitment of eligible patients for the study. This leads to quicker data collection and enables researchers to initiate and complete their studies in a more time-efficient manner.



 IOMED's efficient patient recruitment process also results in cost reduction for researchers and medical institutions. By automating the identification of suitable candidates for the observational study, it reduces the need for extensive manual screening and administrative tasks. This cost-saving aspect allows researchers to allocate their resources more effectively, potentially leading to more studies being conducted and contributing to further advancements in medical research.

Improved Data 

The extensive representation of patient samples obtained through
IOMED's Natural Language Processing systems enhances the validity and relevance of the study's results. By obtaining data from all hospital data sources, researchers can gain strategic insights. This improved data quality helps build a more robust evidence base, fostering advancements in medical knowledge.

Explore other use 

Explore other data-driven use cases.

Explo​​re​​​​ ​​​​​​

Build your 
data-driven future.

Leverage the potential of healthcare data through exploring 
a vast amount of possibilities.

Con​​tact u​​s​​​​​​​​

Protected data. 
Empowered healthcare.

We are fully committed to the principles and regulations set forth by the General Data Protection Regulation (GDPR). Our stringent compliance measures ensure that individual rights and data security are upheld at every step. We are committed to maintain the highest ethical standards while facilitating healthcare players leverage the power of data to drive meaningful healthcare insights.

Main References.

What's new.

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