Case studies

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

Jackalope transforms complex medical data into OMOP CDM and SNOMED Clinical Terms (CT) standardized formats. This enables healthcare providers, practitioners, and scientists to effectively use real-world medical data for research and delivering enhanced patient care.

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.

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.

We designed an ML-powered system for personified lab test results. It boosts the efficiency of physician office laboratories (POLs) and increases the speed of the medical service delivery system. Moreover, the system that we created became a part of a broader telemedical patient care delivery system. Read on to find out how we deal with challenges like this one.

Since the time of Hippocrates clinicians all over the world strive to cure the patient, not the illness. Since a human is a highly organized creature with its features, everyone develops the disease and reacts to medical treatment differently. One of our clients decided to develop a treatment prediction solution based on personalized EHR/EMR data. Check out how our experienced team contributed to this project!

A modern approach to treatment, as well as medical research, requires a lot of data about a patient for a better outcome. More profound knowledge about the human body, genetic, phenotypic, or psychosocial characteristics could ensure precise identification of patient risk factors, early diagnosis of a disease, or treatment targeting. One of our clients decided to use medical data to build patient similarity networks to define the safety of hydroxychloroquine, a drug to treat rheumatoid arthritis. We determined whether it is safe to use hydroxychloroquine (alone and in combination with azithromycin) via building patient similarity networks.