Nursing Assessment Vector Search for Reliable Similar-Patient Matching
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Solution Overview
Problem
Existing assessment systems for nursing records lack reliability in searching for similar patients due to insufficient consideration of multifaceted information, leading to low performance in creating assessments for target patients.
Innovation Solution
An assessment support system that includes an assessment prediction unit to vectorize patient information, a degree-of-similarity calculation unit to determine similarity between patients, and a search unit to identify similar patients based on calculated similarity, utilizing a prediction model trained on actual patient data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If only symptom vectors are used to search for similar patients, then the search process is simple, but the reliability of assessment creation support is insufficient
Solution Approach 1:
The patent transitions from one-dimensional symptom vector comparison to multi-dimensional assessment vector comparison. By vectorizing assessments into high-dimensional spaces that capture nuanced nursing judgments and multifaceted patient information, the system achieves more reliable similarity detection while maintaining computational feasibility through established vector space methodologies.
Solution Approach 2:
The patent combines multiple types of patient information (symptoms, nursing assessments, care plans) into a composite assessment vector. This composite approach integrates diverse data sources to create a comprehensive representation of patient state, improving reliability by considering multifaceted information rather than isolated symptoms.
2Reliability
If multifaceted nursing information is considered for assessment creation, then the quality of assessment support improves, but the search performance decreases
Solution Approach 1:
The patent replaces traditional mechanical search methods with machine learning-based vectorization and similarity calculation. By training assessment prediction models that learn from historical nursing data, the system efficiently processes multifaceted information and identifies similar patients through mathematical similarity metrics rather than exhaustive comparison.
Solution Approach 2:
The patent transforms qualitative nursing assessments into quantitative vector representations. By converting subjective nursing judgments into numerical vectors with defined dimensions and metrics, the system enables efficient computational comparison while preserving the richness of multifaceted nursing information.
Data Source
AI summary
An assessment support system includes: an assessment prediction unit that predicts, based on patient information about a target patient who is a creation target for an assessment in a nursing record, an assessment vector obtained by vectorizing the assessment of the target patient, as a prediction assessment vector; a degree-of-similarity calculation unit that calculates a degree of similarity of the assessment vector to the prediction assessment vector, based on a relationship between the predicted prediction assessment vector and the assessment vector of a patient having the assessment recorded in the nursing record; and a search unit that searches for and outputs at least one similar patient who is similar to the target patient, based on the degree of similarity.