Variable List Caching for Personalized Patient Rule Evaluation
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Solution Overview
Problem
Current patient care plans are not personalized to individual patients' unique lifestyles and medical conditions, failing to account for personal circumstances, available resources, and environmental factors, leading to inadequate adherence and management of chronic illnesses.
Innovation Solution
A data processing system generates personalized patient care plans by integrating patient demographic and medical data with lifestyle information from various sources, including geospatial, environmental, and behavioral data, to create tailored plans that consider individual lifestyle conditions and available resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If generic patient care plans are used, then implementation simplicity is maintained, but patient adherence and treatment effectiveness deteriorate due to lack of personalization
Solution Approach 1:
The patent segments patient information into structured categories (demographics, medical history, lifestyle factors, environmental data) and organizes them into discrete data elements. This segmentation allows the system to systematically process and personalize care plans by selecting relevant segments, rather than dealing with unstructured generic information.
Solution Approach 2:
The system dynamically changes care plan parameters based on individual patient data. Clinical rules are evaluated against specific patient parameters (age, condition severity, lifestyle factors) to automatically adjust care plan recommendations, transforming static generic plans into dynamic personalized ones.
2Measurement precision
If multiple data sources are integrated for personalization, then care plan accuracy improves, but data processing complexity increases
Solution Approach 1:
The patent introduces an intermediary processing layer that standardizes data from multiple sources before analysis. This intermediary layer transforms heterogeneous data from different providers and formats into a unified structure, simplifying subsequent processing while maintaining high accuracy from diverse sources.
Solution Approach 2:
The system employs universal data structures and standardized formats that can accommodate multiple data sources. This multi-functional approach allows the same processing framework to handle various types of patient information (clinical, lifestyle, environmental) without requiring separate processing pipelines for each source.
3Measurement precision
If comprehensive patient information is collected, then rule evaluation accuracy improves, but information processing time increases
Solution Approach 1:
The system performs preliminary organization and categorization of patient information before clinical rule evaluation. By pre-structuring data into relevant categories and pre-filtering information based on the specific clinical rules to be applied, the system reduces processing time during actual evaluation while maintaining comprehensive data coverage for accurate results.
Data Source
AI summary
Mechanisms are provided for caching patient information in a variable list and using the variable list to evaluate patient rules. The mechanisms generate a patient registry comprising a plurality of patient registry records, each patient registry record being associated with a corresponding patient and comprising personal and medical information about the corresponding patient. The mechanisms compile at least one variable list for an input variable of one or more clinical rules. The variable list comprises a plurality of instances of personal and medical information from the plurality of sources that are associated with the variable. The mechanisms also process at least one clinical rule in the one or more clinical rules that references the variable as a criteria for evaluating the at least one clinical rule, based on the variable list associated with the variable to determine if the criteria is satisfied by one or more instances.