Causal Mapping Search Resolution Engine
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Solution Overview
Problem
Traditional search engines struggle to accurately resolve search queries in complex domains like clinical settings, as they rely on syntactic similarities and fail to account for causal relationships between diagnosis and procedures, leading to inefficient retrieval of relevant healthcare providers.
Innovation Solution
The development of an automated system that maps related codes, such as diagnosis and procedure codes, based on historical correlations, generating a cross-code dataset to improve search query resolutions by enabling the production of search results that solve the user's intent without explicit instructions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional search engines rely on syntactic similarities between search queries and potential results, then the search system remains simple and easy to operate, but the retrieval performance deteriorates in complex domains like clinical settings where diagnosis codes and procedure codes lack semantic relations
Solution Approach 1:
The patent introduces an intermediary component (search resolution engine with causal relationship module) that mediates between the user's search query and the search results. This intermediary analyzes causal relationships between diagnosis codes and procedure codes, transforming syntactic matching into semantically meaningful connections, thereby improving retrieval performance without directly complicating the core search engine architecture
Solution Approach 2:
The search system is segmented into distinct functional modules: a traditional search engine component for basic query processing, and a separate search resolution engine for enhancing results through causal relationship analysis. This segmentation allows the system to maintain simplicity in the core search functionality while adding complexity only where needed for improved retrieval performance
2Loss of information
If traditional search engines expand search results by providing causally related conditions, then the breadth of information increases, but the user burden increases due to additional potential conditions without offering solutions
Solution Approach 1:
Instead of expanding search results to show all causally related conditions and hoping users find relevant information, the patent inverts the approach by analyzing the user's query and directly providing resolved search results that connect diagnosis codes to appropriate procedure codes and healthcare providers. This inversion reduces user burden while maintaining information completeness
Solution Approach 2:
The search resolution engine provides feedback mechanisms that analyze user queries, identify causal relationships between codes, and adjust search results accordingly. This feedback loop ensures that complete information is provided in a structured, user-friendly manner that reduces burden while maintaining comprehensiveness
3Measurement precision
If search engines are limited to the context provided by users in complex domains, then the search system remains simple to operate, but the accuracy deteriorates when users lack knowledge of clinical specialties and enter incomplete or misleading search descriptions
Solution Approach 1:
The search resolution engine performs preliminary actions by pre-analyzing causal relationships between diagnosis codes and procedure codes before users submit queries. This preliminary preparation enables the system to accurately interpret user queries even when users lack clinical knowledge, improving search accuracy while maintaining operational simplicity
Solution Approach 2:
The search resolution engine acts as an intermediary that bridges the gap between simple user queries and complex clinical information. It translates user-friendly search terms into accurate clinical code connections, improving accuracy without requiring users to understand complex clinical specialties
Data Source
AI summary
Various embodiments of the present disclosure provide computer interpretation techniques for implementing a query resolution process to improve upon traditional search resolutions within a search domain. The techniques may include receiving a plurality of interaction data objects comprising a plurality of assessment codes and a plurality of intervention codes. The techniques may include generating a frequency distribution comprising a plurality of code pairs based on a plurality of cooccurrences of the plurality of assessment codes and the plurality of intervention codes within the plurality of interaction data objects. The techniques may include generating, using the frequency distribution, a cross-code dataset comprising one or more mapped code pairs from the plurality of code pairs based on a threshold cooccurrence value. The techniques may include initiating the performance of a query resolution operation for a search query based on the cross-code dataset.