Methods to Incorporate AI in Medication Adherence

Finarb incorporates end-to-end solutions for AI based Medication Adherence, utilizing advanced tech tools, data-driven insights, and targeted interventions. Robust Patient PDC (Proportion of Days) Predictive models are constructed using ML algorithms and NLP techniques specifically designed to accurately forecast patient adherence patterns. Performance enhancement of these models is achieved via data augmentation techniques and feature engineering tasks, enhancing the predictive accuracy of the model.

Data analysis is conducted on structured and unstructured datasets to identify drivers influencing medication adherence. Patient risk segmentation forms a critical part of the approach, enabling the design of personalized interventions based on the degree and driver of non-adherence risk. The models undergo continuous refinement, assuring accurate predictions and optimization of adherence strategies. Finarb also integrates intuitive patient follow-ups, custom intervention recommendations, refill reminder systems and various patient empowerment modules through gamification and incentives to enhance the efficacy of AI-based medication adherence.

Finarb makes use of NLP with BERT and Transformer Models, and Machine Learning algorithms like XGBoost and Random Forest as the foundation of our Medication Adherence models. Our models are built to run on cloud-based platforms such as Google Cloud Platform (GCP) and BigQuery. Data pipeline management is handled through Azure Dataflow (ADF), while data visualization is conducted with PowerBI.