System and method for early identification of difficult venous access of a patient

Abstract

A system and method for determining a difficult venous access of a patient. Logic processes meta data acquired by a vasculature assessment device coupled with the patient. The meta data, e.g., vessel diameter, vessel depth, vessel wall thickness, vessel wall elasticity, tissue elasticity, tissue profusion, blood flow rate, or hydration level. The logic further determines a difficult venous access by performing an algorithm on the meta data. The algorithm is defined utilizing machine learning techniques applied to an ongoing collection data sets acquired from a plurality of systems across a plurality of patients undergoing catheter insertion events. The data set may also include patient data such as weight, age, etc. The vasculature assessment device may include ultrasound imaging, infrared imaging, molecular imaging, Raman spectroscopy, or optical coherence tomography to acquire one or both of three-dimensional imaging data and the meta data.