PATIENT DECISION SUPPORT SYSTEMS AND METHODS UTILIZING AUGMENTED REALITY AND MACHINE LEARNING

Smart overview of the Invention

The disclosed systems and methods relate to the application of augmented reality (AR) and machine learning (ML) in emergency medical services. The focus is on improving the accuracy and speed of patient measurements, such as height and weight, which are crucial for determining appropriate medical interventions in high-stress environments.

Background

Emergency Medical Services (EMS) face challenges in obtaining precise patient measurements quickly. Traditional methods, such as resuscitation tapes for pediatric patients and estimation techniques for adults, can be inaccurate and time-consuming, leading to variability in treatment and potentially impacting patient safety. There is a need for improved methods to enhance decision-making and patient outcomes.

Summary

The proposed method utilizes AR and ML to determine patient metrics in emergency situations. A mobile device's camera system captures patient height using AR, while a deep learning-based neural network estimates weight. A decision support system then recommends treatment parameters, such as medication dosages and equipment sizing, based on these metrics. This approach aims to automate critical calculations, improving precision and efficiency.

Detailed Description

The system integrates AR and deep learning technologies to streamline patient measurement in EMS settings. AR components measure patient height, and deep learning algorithms calculate ideal and absolute body weights. These measurements are used to provide accurate care, replacing traditional methods and reducing cognitive load on EMS providers. The system enhances emergency response by standardizing measurements and reducing variability in care.

Exemplary Embodiments

• Inaccurate Measurements: Provides a reliable method for obtaining accurate patient metrics, enhancing emergency care precision.
• Time-Consuming Processes: Streamlines measurement processes, saving valuable time in emergencies through rapid, automated measurements.
• Variability in Care: Standardizes measurement processes with AR and deep learning, ensuring consistent and accurate care.
• Limited Applicability: Offers improved performance over traditional tools, applicable to both pediatric and adult patients, enhancing field usability.