Case studies

The OMOP CDM is widely recognized as the industry standard for observational health research. It provides a standardized data model that facilitates data integration and sharing across different sources, enabling researchers to conduct studies at scale. ATLAS is a central tool used for research within the OMOP CDM ecosystem, providing a user-friendly interface for querying the data model and creating visualizations. Building well-defined cohorts is a critical first step in conducting research using the OMOP CDM. By selecting and defining cohorts of patients with specific characteristics or conditions, researchers can ensure that their studies are focused and relevant, and can generate reliable evidence.

We have extensive experience in automating mappings between medical coding and classification systems by computationally processing their semantic meaning, which can be extracted using NLP methodology or explicitly available from the classification model. Our successful applications include mappings made from ICD-10-PCS, LOINC, and ICD-O-3 systems for OHDSI OMOP Standardized Vocabularies.

The client is a healthcare company focused on medical imaging and diagnostics, working to improve the diagnosis of lung diseases like tuberculosis and COVID-19. They wanted to create an AI-based algorithm that could detect abnormal changes in chest X-rays and automatically prioritize the most urgent cases. It helps to make the diagnostic process faster and more accurate by identifying abnormalities and highlighting critical cases. It reduces the workload for radiologists and pulmonologists by automating routine tasks, so they can focus on more complex cases. By solving issues like delayed diagnoses and heavy workloads, the solution ensures patients with serious conditions get quicker and more reliable care.

A web application designed to facilitate communication in psychopathology treatment by summarizing mental health test results and illustrating treatment dynamics.

Health data is becoming highly digested, and data science and predictive modeling enter the picture to facilitate precision oncology. One of our clients decided to turn tons of unstructured medical data into a predictive modeling solution for cancer progression. Since relying on seasoned professionals in medical data engineering, data analysis, and machine learning (ML) is crucial in this case, we took on a challenge. Read on to discover how we came up with a solution to cancer progression prediction for lung cancer and lymphoma patients.

Our product brings a new healthcare claims management approach with AI-driven capabilities. It streamlines claim processing and reduces denials, improving efficiency and financial outcomes for healthcare providers.

The RxNorm Extension, our innovative product, transforms drug data management by seamlessly integrating international drug classifications and historical data into the OMOP ecosystem for generating evidence from observational data. Developed with advanced AI and database technologies, this toolset ensures comprehensive standardization and integration of drug data, fulfilling essential healthcare and pharmaceutical sector needs.

In the rapidly evolving healthcare industry, data plays a critical role in improving patient outcomes, optimizing operations, and enhancing overall healthcare delivery. AWS QuickSight, a robust business intelligence and data visualization tool offered by Amazon Web Services (AWS), has emerged as a transformative solution for healthcare organizations seeking to harness the power of data-driven insights.