Care Plan Recommendation System Using Feature Hash Vectors
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Solution Overview
Problem
Existing Care Management Systems face challenges in efficiently querying and identifying successful or unsuccessful care plans due to complex and costly data warehousing solutions that are not agile or easy to reconfigure.
Innovation Solution
A computer-implemented method that generates a feature hash vector for a model care plan, compares it with existing care plans using cosine similarity, and provides recommendations based on best matches, allowing for agile and resource-friendly care plan recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data warehousing solutions are used to query care plans, then comprehensive data analysis capability is improved, but system complexity and cost increase significantly
Solution Approach 1:
The patent extracts only the essential features of care plans into feature hash vectors, rather than storing and querying entire care plan datasets. This selective extraction enables efficient comparison while reducing system complexity and eliminating the need for complex data warehousing infrastructure.
Solution Approach 2:
The patent transforms care plan data from its original complex format into feature hash vectors using hashing functions. This parameter transformation converts unstructured or semi-structured care plan data into fixed-dimensional vectors that can be efficiently compared using cosine similarity, dramatically simplifying the querying process.
2Quantity of substance
If data warehousing solutions are implemented, then data storage capability is improved, but implementation cost and setup complexity increase
Solution Approach 1:
The patent creates simplified copies of care plans in the form of feature hash vectors that capture the essential characteristics without requiring storage of the complete original data structures. These vector representations can be stored in simple data structures and compared efficiently without needing complex warehousing systems.
3Loss of information
If traditional querying methods are used, then data completeness is maintained, but query speed and responsiveness decrease
Solution Approach 1:
The patent performs preliminary transformation of care plans into feature hash vectors in advance, organizing the data in a format optimized for rapid comparison. When queries are executed, the system can quickly compute cosine similarity between vectors without needing to process large amounts of raw data, achieving both speed and completeness.
4Measurement precision
If complex data warehousing systems are deployed, then analytical capability is improved, but system agility and reconfigurability decrease
Solution Approach 1:
The patent implements a dynamic feature extraction process where the feature hash vectors can be easily reconfigured by changing the hashing parameters or feature selection criteria. This allows the system to adapt to different analytical needs and care plan types without requiring complex system reconfiguration, maintaining high agility while preserving analytical capability.
Data Source
AI summary
Finding care plan(s) matching desired aspects includes receiving a selection of a model care plan from a user; generating a feature hash vector representing the model care plan, resulting in a model feature hash vector; comparing feature hash vectors of existing care plans to the model feature hash vector to find care plan(s) best matching the desired aspect(s) of a user, resulting in best match(es); and providing one or more care plan recommendation(s) to the user based on the best match(es).