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IOMED’s DSP allows you to streamline clinical research by rapidly identifying eligible patients through real-time access to structured and unstructured health data. It also enables you to generate valuable clinical insights by analyzing large datasets, supporting data-driven decisions in healthcare.
Identify the suitable patients quickly and efficiently with IOMED Data Space Platform
Patient identification is crucial for the success of clinical trials, ensuring that trials enroll the right participants. IOMED’s DSP helps leverage comprehensive patient data to precisely identify eligible patients, reducing recruitment times and improving trial outcomes.
Uncover key insights into disease mechanisms and progression using IOMED's DSP.
Understanding the complexities of diseases is fundamental for developing effective treatments. IOMED’s DSP provides access to rich, real-world data that helps researchers and clinicians delve deeper into disease mechanisms, epidemiology, and patient outcomes, leading to more targeted therapies and improved clinical practices.
Gain comprehensive insights into patient experiences with IOMED DSP.
Understanding the patient journey—from diagnosis to treatment—is critical for optimizing the drug lifecycle. IOMED’s DSP helps track and analyze patient experiences, providing RWD ready to generate insights.
Aligning payment models with patient outcomes through reliable data.
IOMED’s DSP ensures access to the data needed to evaluate and report outcomes accurately, supporting fair and transparent agreements.
Gain valuable insights from historical data to inform current and future clinical decisions.
Retrospective observational studies are crucial for understanding long-term treatment outcomes and disease trends. IOMED’s DSP allows researchers to access and analyze historical patient data, helping to uncover trends, validate hypotheses, and support evidence-based medicine.
Identify and understand your target population with IOMED’s comprehensive data solutions.
Understanding the demographic and clinical characteristics of the target population is essential for a successful drug launch. IOMED’s DSP provides in-depth population profiling, helping to better define their target market and tailor launch strategies accordingly.
The utilization of this export process has been successful with cohorts exceeding 15000 patients, integrating data from over 10 distinct sources. This implementation facilitated validation within a targeted cohort and enabled the successful dissemination of patient-level results to stakeholders, while ensuring compliance with privacy and integrity requirements.
Integration of NLP-derived results into the OMOP CDM led to significant enhancements in data richness. Firstly, we identified 224% more patients across four different hospitals in Spain who met the inclusion criteria thanks to NLP-derived data. Moreover, the dataset incorporating NLP demonstrated a substantial increment in the proportion of records across different OMOP domains compared to the dataset without NLP. The structured inclusion of NLP-derived results facilitated more comprehensive analyses, enabling deeper insights into treatment patterns and patient outcomes.
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.
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.
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.
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.
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.
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.
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.
