Sterile Processing Analytics Using 3D Activity Recognition
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Solution Overview
Problem
Existing technologies lack automated systems for evaluating and optimizing the efficiency and effectiveness of the sterilization and cleaning processes of medical instruments, which are essential for preventing healthcare-acquired infections, as current methods rely on in-person observations and manual tracking are inadequate, costly, and prone to human error and subjectivity.
Innovation Solution
A system utilizing multi-modal sensors and machine-learning models to analyze and generate real-time metrics and recommendations for sterilization processes, including activity recognition and analytics to optimize efficiency and efficacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual tracking and in-person observations are used to evaluate sterilization processes, then implementation complexity is low, but measurement precision and reliability are insufficient and prone to human error
Solution Approach 1:
The patent replaces manual tracking and in-person observations with an automated computer vision system using depth cameras and machine learning models. The system automatically detects, tracks, and analyzes sterilization process actions through image processing, eliminating human error and subjectivity while improving measurement precision and reliability of sterilization evaluation.
Solution Approach 2:
The system enables self-service monitoring where the automated vision system independently evaluates sterilization processes without requiring continuous human observation or intervention. The machine learning models automatically interpret sensor data, generate metrics, and provide feedback, allowing the system to self-monitor and self-evaluate the sterilization efficacy.
2Reliability
If automated sensor-based monitoring is implemented, then measurement precision and reliability improve, but device complexity and implementation cost increase
Solution Approach 1:
The patent employs a multi-functional sensor system where depth cameras serve multiple purposes: capturing spatial information, detecting objects, tracking movements, and analyzing actions throughout the sterilization process. This universal sensor approach improves reliability while managing complexity by consolidating multiple functions into a single system rather than requiring separate specialized sensors for each function.
Solution Approach 2:
The machine learning models act as intermediaries between the raw sensor data and the sterilization evaluation metrics. The models process and interpret the complex sensor information, transforming it into meaningful actionable metrics. This intermediary layer simplifies the overall system architecture by abstracting the complexity of data processing while maintaining high reliability in the evaluation results.
3Productivity
If real-time analytics and feedback are provided, then productivity and efficiency of sterilization processes improve, but loss of time for data processing and analysis increases
Solution Approach 1:
The system performs preliminary processing of sensor data in real-time during the sterilization process itself. The machine learning models continuously analyze incoming sensor streams, detect actions, and generate metrics concurrently with the sterilization operations. This preliminary action eliminates the need for post-processing delays, allowing real-time feedback to be provided without time loss, thereby improving productivity.
Solution Approach 2:
The patent implements continuous real-time analysis where the vision system and machine learning models operate continuously throughout the sterilization process without interruption. The system maintains constant monitoring and generates metrics continuously, ensuring that feedback is always available and that no useful action time is lost to data processing delays, thus maximizing sterilization efficiency.
Data Source
AI summary
The arrangements disclosed herein relate to systems, apparatuses, methods, and non-transitory processor-readable media for receiving, from one or more first sensors located in a decontamination room, multi-modal data comprising three-dimensional data of at least one sterile processing (SP) procedure performed in a decontamination room, determining, using a first activity recognition machine-learning model, one or more SP actions based at least in part on the multi-modal data, determining, using an SP analysis machine-learning model, an SP metric value based at least in part on the one or more SP actions, wherein the SP metric value is indicative of at least one of efficiency or efficacy of the at least one SP procedure, and providing the SP metric value to a display device for display.