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Case studies - Healthcare

See how we produce optimal AI solutions for our clients.

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Transforming Complex Medical Data into Clinical Insights with Jackalope

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.

iconHealthcare
iconData Science
iconAI / ML
AI-Powered Claim Denial Management System in Healthcare

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.

iconHealthcare
iconComputer Vision
iconAI / ML
iconNLP
iconData Science
RxNorm Extension: tool to standardize source drug data using OMOP CDM

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.

iconHealthcare
iconAI / ML
iconData Science
Optimizing Clinical Trial Performance with AWS QuickSight Analytics and Dashboards

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.

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iconData Science
Psychopathological Assessment Visualization

When it comes to health, compromises are unacceptable. Clear and timely communication, trust between a patient and a doctor, and correct treatment dynamics explained means a lot. One of our clients had an idea of a web application to facilitate everyone engaged in psychopathology treatment. Read on to discover how we developed this app, considering privacy by default.

iconHealthcare
iconData Science
Personified Blood Test Analysis Solution for Remote Medicine

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.

iconHealthcare
iconData Science
iconNLP
Personalized Treatment Prediction

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!

iconHealthcare
iconData Science
Patient Similarity Networks Development to Guide Clinical Decision-Making

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.

iconHealthcare
iconData Science
OHDSI Medical Vocabulary Development

Any country's healthcare systems and institutions have their classifications applicable locally. Instead of a single united system, multiple vocabularies pose obstacles to patient data exchange, global healthcare big data analysis, and slow down research. So it is evident that researchers and healthcare providers lack a common medical vocabulary model that will meet all the requirements. One of our clients decided to create a comprehensive model representing the data from different sources and ontologies used in various fields of medical science and the healthcare industry. Check out how we contributed to this project!

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iconData Science
Medical Image Recognition

Visualization of pathological processes plays a crucial role in establishing most clinical diagnoses. However, sometimes the results could be controversial. Various factors influence it: the quality of the equipment, lack of medical skills and knowledge, limited time, etc. One of our clients decided to develop an algorithm for a medical image segmentation solution to distinguish between normal and abnormal chest X-rays (CXR) images. Read on to find out details.

iconHealthcare
iconComputer Vision