Case Studies: Beyond Manual Processing, an Automated Path to Clinical Accuracy.
Artificial intelligence is leveraged to replace manual, error-prone clinical processes with highly accurate, automated solutions. Key capabilities include using self-learning algorithms for multi-source anomaly detection and auto-encoders for the accurate medical coding of adverse events.
This domain also encompasses the simulation of clinical testing data, generating vast arrays of randomized subject journeys and outliers to comprehensively support Clinical Programming processes.
Key Differentiators: Intelligent Control & Automation
-
AI/ML — Anomaly Detection in Clinical Data
Challenge: Managing large pools of clinical data with deviations and outliers manually.
Solution: Anomaly detection using rule-based, self-learning algorithms. Multi-source data integrity. Automated query raising at source.
Results: Repeatable tasks reduced. User efficiency increased. Collaborative query resolution accelerated. -
AI/ML — Medical Coding Auto-Encoder
Challenge: Manual medical coding of AEs, SAEs, MH, CM data — mandatory and error-prone.
Solution: Auto Encoder programs match verbatim terms to dictionary terms. AI-driven validation and auto-coding.
Results: Reduced traditional coding effort. Increased accuracy and efficiency. -
Data Simulation — Clinical Testing Data
Challenge: Generating test data for Clinical Programming (CP) processes — time-consuming, limited scenario coverage.
Solution: Randomisation approach with outlier/missing data support. Auto gen of subject IDs. Study-level configurations.
Results: Entire journey data creation driven through configurations.