Automated Patient Triage System for Dynamic Waiting List Prioritization

The system enables automated self-service functionality where the triage system automatically monitors patient data, updates risk probabilities, and reassigns positions on waiting lists without requiring manual intervention. The system serves itself by continuously collecting data from patient devices, processing it through machine learning models, and automatically adjusting prioritization, thereby resolving the contradiction between efficiency and complexity.

The patent replaces manual mechanical processes with an automated electronic system that uses machine learning models and data processing algorithms to manage waiting lists. The system substitutes human operators with computational mechanisms that automatically evaluate patient risk, calculate probabilities of adverse events, and dynamically reposition individuals on lists based on real-time data, thus improving productivity while maintaining manageable complexity through standardized algorithms.

The system implements dynamic prioritization where positions on the waiting list are not fixed but continuously adjusted based on changing patient conditions and risk probabilities. The machine learning model processes ongoing data from patient devices and automatically repositions individuals on the list in real-time, ensuring that those at highest risk receive services first. This dynamic approach replaces static first-come-first-served allocation, improving both reliability of resource allocation and reducing waiting times for high-risk patients.

The system incorporates continuous feedback loops where patient data is constantly monitored, risk probabilities are recalculated, and list positions are automatically adjusted. The machine learning model receives feedback from ongoing patient monitoring and uses this information to dynamically reprioritize the waiting list, ensuring that resource allocation accurately reflects current patient needs and risk levels, thereby reducing unnecessary waiting time while improving allocation reliability.

The system uses a universal machine learning model that can process multiple types of patient data and perform various risk assessment functions through a single integrated platform. The model is designed to handle diverse input data from different patient devices and conditions, calculating probabilities of adverse events across multiple patient populations simultaneously. This multi-functional approach improves measurement precision while avoiding the need for separate complex monitoring systems for each patient type.

The system uses digital copies of patient data from electronic health records and patient devices to perform risk assessments without requiring direct physical monitoring of all patients. The machine learning model processes replicated data sets and simulated scenarios to refine risk probability calculations, enabling precise risk assessment while reducing the complexity of direct real-time monitoring infrastructure. Data copying allows the system to analyze multiple patient states simultaneously without proportionally increasing monitoring complexity.