Patient Journey.
Our technology empowers healthcare organizations through activating its data, enabling strategic and informed decision-making.
The patient journey encompasses various stages of interaction with healthcare services, making it essential to consider patient experiences for improved care.
IOMED's approach.
IOMED enables healthcare organizations to leverage population data and analyze the patient journey from a patient-centric perspective. By processing and unlocking data, IOMED helps healthcare organizations to identify gaps and generate strategic insights in real-world clinical practices, empowering them to deliver personalized and effective solutions.
+21M
Unique Medical Records
+50
Strategic Partners
+30
Years of Data
+175
Primary Care Centers
Our technology.
Automated Concept
Mapping.
Our technology can automate mapping and structuring of 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.
OMOP
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
Processing.
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
Model.
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.
Cost Savings.
Through a detailed analysis of the patient journey, the hospital can identify opportunities to optimize resources and reduce costs associated with unnecessary tests and procedures.
Early Diagnosis.
By studying the patient journey, the hospital can identify patterns and early signs of the pathology, facilitating early diagnosis. This is critical in improving patient outcomes and enabling more effective treatment from the early stages of the disease.
Patient Safety.
By understanding the patient's journey and critical points of care, the hospital can implement measures to enhance patient safety throughout the healthcare process. This includes avoiding unnecessary tests, reducing medical errors, and ensuring that patients receive appropriate care at the right time.
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cases.
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data-driven future.
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a vast amount of possibilities.
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.
The DERMACLEAR study: Verification results of a natural language processing system in dermatology.
Results from the DERMACLEAR study will increase the real-world evidence of clinical practice, obtaining a large amount of information on patients with the studied diseases. The NLP system used is precise in identifying patients diagnosed with HS, PsO, CU and/or AD, and other medical variables from EHRs, highlighting that it is a valid system to use in the DERMACLEAR study.
August, 2023
An open source corpus and automatic tool for section identification in Spanish health records.
This work shows that it is possible to build competitive automatic systems when both data and the right evaluation metrics are available. The annotated data, the implemented evaluation scripts, and the section identification Language Model are open-sourced hoping that this contribution will foster the building of more and better systems.
September, 2023
NATI (NATural language in ThyroId cancer).
A total of 5137 medical records of patients diagnosed with thyroid cancer between 2015 and 2022 were included. The median follow-up (interquartile range) was 29.7 months (8.8-55.8). The mean age at the time of diagnosis was 55 years (SD 18), and 67% were women. The stage could be classified in a subgroup of 520 patients, of which 60% (n=313) had advanced stages. Metastasis was observed in 2177 patients (42%) during the follow-up, mainly in lymph nodes (44%). It was also identified that the majority of patients (71%; n=3629) had some comorbidity.
September, 2023
Extending the OMOP CDM to store the output of natural language processing pipelines.
Although OMOP CDM provides a NOTE_NLP table to store the outputs of NLP algorithms, queries to this table can become clumsy and slow, so we designed and extended the OMOP CDM with our own NLP schema to store the results generated in the annotation process of NLP. We designed an extension of the OMOP CDM able to store the output of NLP solutions while integrating with the vocabulary normalization process of the OMOP CDM.
September, 2021
A Framework for False Negative Detection in NER/NEL.
Finding the false negatives of a NER/NEL system is fundamental to improve it, and is usually done by manual annotation of texts. However, in an environment with a huge volume of unannotated texts (e.g. a hospital) and a low frequency of positives (e.g. a mention of a particular disease in the clinical notes) the task becomes very inefficient.
June, 2022
Efficient automated mapping of internal source codes to OMOP CDM concepts.
Our automated concept mapping system provides an efficient way of mapping source codes to OMOP concepts. By utilizing text-based vector representations and knowledge transfer, our system can find equivalent mappings from other hospitals, thereby reducing the time and effort required for manual mapping.
June, 2023
ContextMEL: Classifying Contextual Modifiers in Clinical Text.
Taking advantage of electronic health records in clinical research requires the development of natural language processing tools to extract data from unstructured text in dif ferent languages. A key task is the detection of contextual modifiers, such as understanding whether a concept is negated or if it belongs to the past. We present ContextMEL, a method to build classifiers for contextual modifiers that is independent of the specific task and the language, allowing for a fast model development cycle.
June, 2020
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