Automated ECG Data Categorization and Specialist Auction System
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Solution Overview
Problem
Current systems for ambulatory ECG monitoring generate large amounts of patient-specific data that are difficult to efficiently pre-sort and triage, particularly when handling data from devices like event recorders, which are not well-suited for large volumes of information.
Innovation Solution
The proposed solution involves categorizing ECG data into groups based on shared characteristics using automated processes, allowing for efficient analysis and auctioning to qualified specialists, with features like filtering, annotating, and truncating ECG traces, and determining accuracy ratings for specialists to ensure high-quality interpretations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated solutions are used to review and analyze medical data, then the volume of data that can be processed increases, but the accuracy and reliability of interpretation decreases
Solution Approach 1:
The patent segments medical data into categories based on shared characteristics (e.g., by patient demographics, condition type, data complexity). This allows automated systems to process large volumes of data efficiently while maintaining accuracy by directing specialized human reviewers to specific segments that require expert attention, thus resolving the contradiction between processing volume and interpretation accuracy.
Solution Approach 2:
The patent introduces an intermediary categorization layer between automated data collection and human interpretation. This intermediary system organizes and pre-sorts data into meaningful groups, enabling automated processing at scale while ensuring that human reviewers receive pre-organized data that maintains its diagnostic accuracy and clinical relevance.
2Reliability
If medical data is outsourced to specialists for interpretation, then the quality of analysis improves, but the cost and time required for processing increases
Solution Approach 1:
The patent segments data into categories that can be processed by different levels of specialists. Routine categories can be handled by less expensive reviewers while complex categories are directed to specialized experts. This segmentation enables quality maintenance through specialized review where needed while reducing overall processing time and cost by not requiring expert review for all data.
Solution Approach 2:
The patent performs preliminary categorization and organization of data before it reaches specialists for interpretation. This pre-processing step organizes data into meaningful groups and prioritizes it based on characteristics, allowing specialists to receive pre-sorted data that reduces their processing time while maintaining high analysis quality.
3Quantity of substance
If large amounts of medical data are handled, then the comprehensiveness of review increases, but the complexity of the system increases
Solution Approach 1:
The patent applies segmentation by dividing the large volume of medical data into manageable categories based on shared characteristics. This segmentation simplifies system architecture by creating standardized processing streams for different data types, making the system more scalable and less complex as data volume increases.
Solution Approach 2:
The patent creates a universal categorization framework that can handle multiple types of medical data (ECG, imaging, lab results) using the same organizational principles. This multi-functional approach reduces system complexity by using a single standardized system architecture that can process diverse data types without requiring separate specialized systems for each data type.
Data Source
AI summary
Described herein are apparatuses (e.g., systems, devices) and methods for processing, distributing, and analyzing medical data. In particular, apparatuses and methods for processing and distributing tasks or jobs for completion by a qualified worker or specialist are described. Crowdsourcing data to specialists for interpretation can be accomplished using an auction system to match the specialists with discrete portions of the data. The data can be bundled based on one or more categories or characteristics, and then placed on an auction where the specialists can bid for each bundle by submitting a payment price. The data can be medical data, such as ECG data, imaging data and test data.