Relational algorithm interpreting symptoms to demographic sign of patient medical history

Doctor-patient communication is a skill to be thought in medical education. Challenges in performing an effective communication pressurizes medical students to focus on how to earn accurate patient history by using different methods of communication which is a challenging issue toward quality of care. Time pressure of waiting patients, irrelevant patient dialogue requiring sympathy, less accurate patient response, none categorized set of quick answers which needs to be specified and might be totally irrelevant to the care are sets of examples of frustration in communication within clinical care. Although many studies through machine learning, artificial intelligent and virtual reality addressed communication challenges, there are still rooms yet to establish an accurate communication channel for improving quality of care. This paper introduces a new method as a domain concept to interpret sign values as graphical interface for medical care providers so that information exchange with an enhanced and clear history taking symptoms relevant to both patients and disease category for an improved quality care environment. Designing a medical terminology interpretation software that relates medical history to graphical interface of various and easy to understand signs to reduce communication error of patients having difficulty relating different signs to their existing symptoms.