P-488: Expert in the Loop - AI assisted generation of Real-World-Evidence

Introduction: syndena is a technology company that provides an innovative Expert-in-the-Loop (EITL) data extraction tool to extract data from electronic health records (EHRs) and medical files. The EITL tool aims to improve patient outcomes in various medical fields e.g. hematology, oncology, neurology, cardiology, and nephrology by generating real-world evidence and preventing misdocumentaions, as well as relieving medical staff of the burden of managing large and complex data sets.

Methods: Syndena's EITL technology utilizes advanced natural language processing (NLP) and machine learning algorithms to extract relevant data points from unstructured medical records with expert oversight. Healthcare professionals can leverage the technology to gain insights from a broad range of data sources, including lab results and clinical notes. Syndena is already able to structure and find relevant clinical information in EHRs of patients with various medical conditions, making the technology applicable across multiple medical fields. The EITL tool has been successful in extracting and analyzing large and complex data sets from EHRs and medical files at much higher speeds than traditional methods. The data gathered is saved in the Observational Medical Outcomes Partnership Common Data Model (OMOP CDM) automatically, which provides several advantages, including a standardized, widely accepted, data structure.

Results: syndena's EITL technology can lead to improved patient outcomes by timely generating real-world evidence for all medical fields including hematology. The technology has been tested in various healthcare settings and has been successful in extracting and analyzing large amounts of complex data from source files (EHRs) at high speeds with expert oversight. The EITL tool can also prevent misdocumentations by identifying and correcting data inconsistencies and errors in EHRs and medical files, using plausibility controls. Moreover, the automatic saving of data in the OMOP CDM provides several advantages, including a standardized data structure that allows for efficient and effective data analysis, data normalization, and interoperability across different data sources.

Conclusions: syndena's EITL technology is a significant advancement in the use of technology to optimize the timely generation of real-world data from large medical data sets and primary source files in a highly compatible fortmat (OMOP CDM) Resulting in standardized data sets that allow for efficient and effective data analysis, data normalization, and interoperability across different data sources. Syndena's EITL technology has demonstrated its potential to improve QoL and understanding of outcomes across multiple medical fields . By facilitating personalized medicine and improving clinical trial design, the EITL tool can inform healthcare policies and drive evidence-based decision-making. Fubased data sets. rthermore syntetic data can easily be generated from EITL based data sets.