Distributed Framework for Patient Data Search
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Solution Overview
Problem
There is a need for a distributed framework that can efficiently search and retrieve unstructured and structured patient data across various systems, as existing technologies lack the capability for parallel search and retrieval with hierarchical ranking.
Innovation Solution
A generalized distributed framework utilizing abstract data types (ADT) is implemented, which ingests patient data from multiple sources, populates data models, and stores them in an index for efficient search and retrieval, allowing for hierarchical ranking and handling of structured and unstructured data types through abstract data types and knowledge models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If patient data is stored across multiple distributed systems in structured and unstructured formats, then data accessibility and query power are improved, but search efficiency and retrieval accuracy deteriorate due to lack of unified indexing
Solution Approach 1:
The patent segments patient data into structured and unstructured components, indexing them separately in a unified index structure. Structured data (e.g., demographics, vitals) is indexed with precise field mappings, while unstructured data (e.g., clinical notes) is processed through entity extraction and indexed as structured entities. This segmentation enables efficient targeted retrieval without sacrificing data accessibility.
Solution Approach 2:
The patent introduces an intermediary layer of abstract data types (ADTs) and knowledge models that mediate between distributed data sources and search queries. These ADTs serve as a unified interface, translating diverse data formats into a common structure that maintains accessibility while enabling efficient searching through standardized query mechanisms.
2Measurement precision
If hierarchical ranking with multiple criteria is implemented for search results, then retrieval accuracy is improved, but system complexity increases due to multiple scoring mechanisms
Solution Approach 1:
The patent implements hierarchical ranking by changing the parameter structure of search scoring. Multiple scoring parameters are defined at different hierarchical levels: primary criteria (e.g., exact match on patient ID), secondary criteria (e.g., demographic matches), and tertiary criteria (e.g., clinical data relevance). This parameterized approach improves retrieval accuracy while managing complexity through structured parameter organization.
Solution Approach 2:
The patent performs preliminary action by pre-computing and storing multiple scoring attributes during the indexing phase. Relevance metrics, entity types, and confidence scores are calculated and stored with each indexed document, enabling efficient hierarchical ranking at query time without complex real-time computations, thus reducing system complexity.
3Speed
If parallel search across multiple data zones is implemented, then search speed is improved, but result consistency deteriorates due to independent processing in each zone
Solution Approach 1:
The patent implements feedback mechanisms in the parallel search architecture where search results from multiple zones are collected with their associated scoring metadata. A central ranking system receives feedback from all zones, applies the hierarchical scoring criteria uniformly, and re-ranks results to ensure consistency. This feedback loop maintains result consistency while preserving the speed benefits of parallel processing.
Solution Approach 2:
The patent merges search results from multiple distributed zones into a unified result set using a common ranking framework. Instead of returning independent results from each zone, the system combines all results and applies hierarchical ranking criteria uniformly, ensuring consistency. The merging process preserves parallel processing speed while establishing result uniformity through centralized ranking logic.
4Ease of manufacture
If abstract data types and knowledge models are used to model patient data, then ease of updating data models is improved, but initial implementation complexity increases
Solution Approach 1:
The patent creates universal abstract data types that serve multiple functions: data modeling, search indexing, and result ranking. These ADTs are designed to be configurable and extensible, allowing easy updating of data models by modifying the underlying knowledge graphs and entity schemas. The universal design reduces implementation complexity over time as the system becomes more established and reusable.
Data Source
AI summary
A generalized distributed framework is provided for parallel search and retrieval of unstructured and structured patient data across zones with hierarchical ranking. In various embodiments, patient data is ingested from a plurality of data sources. A plurality of data models is populated based on the ingested patient data, each data model comprising an abstract data type. The plurality of data models is stored in an index. A search request is processed against the index, the search request comprising one or more attribute of the abstract data type.