Healthcare Object Recognition for Relevant Data Routing
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Solution Overview
Problem
Existing healthcare systems lack the ability to autonomously determine if raw data collected is medically relevant, relying solely on a priori assumptions, as seen in systems like Flom and Tran, which fail to discriminate between generic or ambient data and healthcare data.
Innovation Solution
An HCO discrimination system with a sensor interface and recognition platform that analyzes digital representations of real-world scenes to identify and classify objects as healthcare objects (HCOs), using discriminator characteristics and routing them to appropriate destinations based on predefined rules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If existing healthcare systems collect and incorporate all raw data into medical records, then data completeness is improved, but data accuracy and relevance deteriorate due to inability to distinguish healthcare data from generic data
Solution Approach 1:
The system segments data into distinct categories by introducing healthcare object templates that classify data as either healthcare-related or generic. The discrimination platform divides incoming data streams into separate processing paths based on template matching, ensuring only relevant data is incorporated into medical records while maintaining complete capture of all data types for analysis.
Solution Approach 2:
Healthcare object templates serve as intermediary structures between raw data collection and medical record incorporation. These templates act as filtering mediators that evaluate data against predefined healthcare criteria, allowing the system to maintain data completeness for analysis while ensuring accuracy by only incorporating validated healthcare-related data into records.
2Speed
If existing systems rely on a priori assumptions to determine healthcare data relevance, then processing speed is improved, but reliability deteriorates due to inability to discriminate generic data from healthcare data
Solution Approach 1:
The system performs preliminary action by pre-defining healthcare object templates that encode healthcare relevance criteria before data arrives. These templates are prepared in advance with specific attributes and validation rules, enabling the discrimination platform to quickly match incoming data against established patterns rather than making assumptions, thus maintaining both speed and reliability.
Solution Approach 2:
The system implements feedback mechanisms where the discrimination platform continuously evaluates data against healthcare object templates and adjusts its classification based on matching results. This feedback loop ensures that only data meeting predefined healthcare criteria is incorporated into medical records, maintaining reliability while the automated template-matching process preserves processing speed.
3Loss of information
If existing systems incorporate all collected data into medical records, then data availability is improved, but data quality deteriorates due to inclusion of irrelevant generic data
Solution Approach 1:
The system segments data incorporation into two distinct pathways: healthcare-related data is incorporated into medical records with full quality assurance, while generic data is stored separately for reference purposes. This segmentation maintains data availability by preserving all collected information while ensuring data quality by only incorporating validated healthcare data into formal medical records.
Solution Approach 2:
Healthcare object templates serve as intermediary filters that assess data quality before incorporation into medical records. The templates evaluate data against healthcare relevance criteria, acting as a quality gate that maintains high data quality standards in medical records while preserving availability of all data in appropriate storage locations.
Data Source
AI summary
Healthcare object (HCO) discriminator systems and methods are presented. Systems can obtain a digital representation of a scene via a sensor interface. An HCO discriminator platform analyzes the digital representation to discriminate objects within the scene as being associated with a type of HCO or as being unrelated to a type of HCO. Once the HCO recognition platform determines that a type of HCO is relevant, it instantiates an actual HCO. The HCO can be routed to one or more destinations based on routing rules generated from a template or based on the manner in which the objects in the scene were discriminated.