Leveraging Snomed Ct Within Standardized E-Clinical Pathways: A Key For Extensive Healthcare Big Data Analytics

Automating healthcare facilities is a complex task aimed at optimizing a sector inundated with vast amounts of data.A significant challenge lies in digitizing paper-based clinical pathways (CPs), which are crucial for understanding patient treatment plans.These CPs, traditionally paper-based, contain valuable data that can inform health policy and research.By developing an algorithm -based model that standardizes CP data using SNOMED CT, we enable machine learning algorithms to effectively analyze CP-based datasets.Our approach addresses data missingness issues, facilitates detailed statistical analyses, and enhances the efficacy of data analytics algorithms.Our experiments, particularly in predicting the Length of Stay (LOS) for stroke patients using an e-clinical pathway dataset, exhibit superior performance compared to traditional Electronic Health Record (EHR) datasets.