Hashing Electronic Health Records for Fast Retrieval
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Solution Overview
Problem
Current retrieval systems for electronic health records (EHRs) are inefficient, often requiring healthcare providers to sift through vast amounts of irrelevant information when searching for relevant medical data for patient care decisions, due to the high dimensional feature spaces and lack of tailored search terms.
Innovation Solution
A method involving hashing electronic health records to generate hashing indexes that facilitate quick retrieval of relevant health data by performing encounter analysis, feature extraction, and applying hash functions to attributes, allowing for cross-document reasoning and informed decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional retrieval systems are used to search EHRs, then complete medical data can be accessed, but the search time increases and relevance decreases due to high dimensional feature spaces
Solution Approach 1:
The patent applies preliminary action by pre-computing hashing indexes for EHR attributes before actual retrieval operations. The system performs encounter analysis, extracts attributes, and generates hashing indexes in advance, so that when a retrieval query is made, the system can quickly compare query hashes against pre-computed indexes rather than searching through all raw EHR data, thereby reducing search time while maintaining relevance
Solution Approach 2:
The patent applies extraction by identifying and extracting key attributes from high-dimensional EHR data that are most relevant for retrieval. Instead of searching through all EHR features, the system extracts essential attributes (such as encounter type, patient demographics, diagnosis codes) and creates hashing indexes only for these extracted features, reducing the search space while preserving retrieval quality
2Productivity
If hashing indexes are generated for all EHR attributes, then retrieval speed improves, but system complexity increases
Solution Approach 1:
The patent applies local quality by applying hashing index generation selectively to specific EHR attributes rather than uniformly to all attributes. The system identifies which attributes are most important for retrieval (such as encounter type, patient identifiers, diagnosis codes) and generates hashing indexes only for these local areas of the data, improving retrieval speed for critical attributes while avoiding the complexity overhead of indexing every possible attribute
Solution Approach 2:
The patent applies segmentation by dividing the EHR data into distinct attribute categories and generating separate hashing indexes for each category. Instead of creating one monolithic index for all EHR data, the system segments attributes into groups (demographics, encounters, diagnoses, medications) and creates specialized indexes for each segment, making the overall system more manageable and less complex while still achieving fast retrieval
Data Source
AI summary
Provided is a method, computer program product, and system for hashing electronic health records. A processor may collect a set of electronic health records (EHRs). The processor may perform an encounter analysis on the set of EHRs to determine a set of attributes associated to the set of EHRs. The processor may hash the set of attributes to generate one or more hashing indexes that correspond to the set of EHRs. The processor may store the one or more hashing indexes in a list used for document retrieval.