Realyze Intelligence develops an AI-driven platform that utilizes natural language processing to analyze electronic medical records and extract relevant clinical data for patient screening in oncology clinical trials. This technology enables healthcare providers to quickly identify eligible patients, enhance diversity in trial participation, and streamline the recruitment process, ultimately improving treatment outcomes for chronic diseases and cancer.
Funding
Funding not disclosed
Founders
Product
Problem
Oncology clinical trials face challenges in efficiently screening patients due to the time-consuming and complex nature of analyzing electronic medical records. This can lead to delays in identifying eligible patients, limiting diversity in trial participation, and hindering overall treatment outcomes.
Solution
Realyze Intelligence offers an AI-powered platform designed to streamline patient screening for oncology clinical trials. By leveraging natural language processing (NLP) and machine learning (ML) algorithms, the platform analyzes structured and unstructured data within electronic health records (EHRs) to identify potential trial candidates. This technology enables healthcare providers to accelerate the screening process, ensure inclusive patient screening, and unlock real-world data insights, ultimately improving patient care and research capabilities. The platform offers solutions for clinical trial matching, patient cohort identification, patient data summarization, and data transmission to other systems using the FHIR standard.
Target Audience
The primary target audience includes oncologists, researchers, principal investigators, and sponsors/CROs involved in oncology clinical trials, as well as healthcare providers seeking to improve patient care and research capabilities.
Features
- AI-driven analysis of structured and unstructured data in electronic medical records
- Natural language processing (NLP) to extract relevant clinical information
- Machine learning (ML) algorithms for patient matching and cohort identification
- Automated patient screening for clinical trial eligibility
- Identification of eligible patients pre-visit
- Tools to eliminate implicit bias in clinical research processes
- Support for decentralized clinical trial initiatives
- Real-world data (RWD) generation and summarization at the patient level
- FHIR-standard data transmission to EMR, EDW, and CTMS systems
- Rapid platform configuration to fit specific clinical practice needs