Diagnostic Algorithm Correction via Feedback Loop
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Solution Overview
Problem
Current medical monitoring and processing systems lack an efficient mechanism for updating diagnostic algorithms based on real-time correction indications from healthcare professionals, leading to potential inaccuracies in patient diagnosis.
Innovation Solution
A system comprising a monitoring and processing apparatus that senses patient data, a local computing device for generating diagnostic results, and a remote computing device for updating diagnostic algorithms based on correction indications from multiple patients, using a network to disseminate the updated algorithms to other monitoring devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If diagnostic algorithms are updated based on correction indications from multiple patients, then diagnostic accuracy is improved, but device complexity increases
Solution Approach 1:
The system implements a feedback mechanism where correction indications from healthcare professionals are collected and used to update diagnostic algorithms. The remote computing device receives correction indications from multiple local computing devices, processes this feedback, and generates updated diagnostic algorithms that are then distributed back to monitoring devices, creating a continuous improvement loop that enhances diagnostic accuracy while managing system complexity through automated feedback processing
Solution Approach 2:
A remote computing device acts as an intermediary between local computing devices and the diagnostic algorithm update process. This intermediary collects correction indications from multiple sources, processes the feedback centrally, and distributes updated algorithms to relevant monitoring devices, thereby simplifying the overall system architecture by centralizing the complex update logic in a dedicated intermediary component rather than distributing complexity across all devices
2Reliability
If real-time correction indications are collected and processed, then diagnostic reliability is improved, but data transmission requirements increase
Solution Approach 1:
The system extracts only the essential correction indication data from the feedback process, separating the critical diagnostic correction information from other unnecessary data transmissions. By extracting and transmitting only the relevant correction indications rather than complete patient datasets, the system improves diagnostic reliability through accurate feedback while minimizing data transmission requirements and information loss
3Measurement precision
If updated diagnostic algorithms are distributed to multiple monitoring devices, then overall system accuracy is improved, but network communication requirements increase
Solution Approach 1:
The updated diagnostic algorithms are designed with universal applicability, allowing a single algorithm update to be distributed to and implemented by multiple different types of monitoring devices across the network. This multi-functional approach enables one algorithm to serve multiple devices and purposes, improving overall system accuracy while reducing network communication complexity by avoiding the need for device-specific algorithm variations
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This system enables continuous improvement of diagnostic accuracy by updating diagnostic algorithms based on correction indications, enhancing the reliability of patient monitoring and treatment decisions.
Implementation Method 1
The monitoring and processing apparatus sensor may configured to sense the patient data using one or more electrodes coupled to the monitoring and processing apparatus. The patient data may include an electrocardiograph (ECG) signal.
Data Source
AI summary
Methods, apparatus, and systems for medical procedures include a monitoring and processing apparatus that includes a memory configured to store a diagnostic algorithm, a sensor configured to sense a patient data of a first patient, a processor configured to generate a first diagnostic result based on the patient data and the diagnostic algorithm. A local computing device is provided and includes a processor configured to receive the first diagnostic result via a first network, receive a first correction indication that includes a correction of the first diagnostic result and transmit the first correction indication via a second network. A remote computing device may be provided and be configured to generate an updated diagnostic algorithm that is updated based on the first correction indication and transmit the updated diagnostic algorithm via the second network.