METHOD AND SYSTEM FOR USING ARTIFICIAL INTELLIGENCE TO TRIAGE TREATMENT PLANS FOR PATIENTS AND ELECTRONICALLY INITIATE THE TREAMENT PLANS BASED ON THE TRIAGING

Smart overview of the Invention

A method for managing patient treatment plans involves receiving user profiles that detail specific conditions of patients. This includes gathering healthcare professional profiles to identify qualified providers who can interact with the patients. Additionally, information about treatment devices suitable for the patients is collected, ensuring that the resources align with the users' needs.

Resource Deployment Prediction

The system employs an artificial intelligence engine that utilizes machine learning models to generate predictions regarding resource deployment. These predictions are derived from the user profiles, healthcare professional information, and treatment device data. The AI-driven approach aims to enhance the accuracy and efficiency of treatment plan selection for individual users based on their unique circumstances.

Creating Treatment Plans

Based on the resource deployment predictions, tailored treatment plans are generated for each user. These plans may encompass various protocols, including schedules for exercises or medical procedures, dietary recommendations, and medication regimens. The goal is to provide a comprehensive and personalized approach to patient care.

System Components

The described system comprises a processing device and memory that work together to execute the methods outlined. Instructions stored in the memory enable the processing device to perform operations related to patient triage and treatment initiation effectively. This system architecture supports seamless integration and functionality in a telemedicine context.

Data Utilization and Analysis

The method emphasizes the importance of analyzing diverse patient data, including personal demographics, performance metrics, and vital signs. By leveraging this information, healthcare professionals can better understand patient needs and improve treatment outcomes. The approach facilitates a more data-driven strategy in remote medical assistance, ultimately enhancing patient care quality.