Medical Information Navigation Engine for Patient Outcome Optimization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The medical information management system lacks consolidation and reliability, leading to inefficiencies and uncertainties in patient care, as payoffs for medical encounters are not tied to outcomes, incentivizing providers to prioritize quantity over quality.
Innovation Solution
A Medical Information Navigation Engine (MINE) system that computes payoff by comparing actual and optimal patient encounter vectors, using an intelligent matrix to optimize medical information management and provide actionable insights for improving care efficiency and quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If medical information is consolidated and managed through a centralized system, then reliability and accessibility of patient information improve, but system complexity and implementation costs increase
Solution Approach 1:
The system segments medical information management into distinct functional modules: information consolidation engine, encounter vector computation module, payoff calculation module, and optimization module. Each module handles specific tasks independently, reducing overall system complexity while maintaining reliability through specialized processing.
Solution Approach 2:
The patent introduces an intermediary computational layer (encounter vectors and intelligent matrices) that mediates between raw medical information and decision-making processes. This intermediary structure standardizes information representation and simplifies complex queries, improving reliability without proportionally increasing system complexity.
2Productivity
If payoffs for medical encounters are tied to outcomes, then care quality and efficiency improve, but measurement and evaluation complexity increase
Solution Approach 1:
The system transforms complex medical encounter evaluations into standardized parameter representations (encounter vectors) with defined dimensions and weights. By changing the representation parameters from unstructured clinical data to structured vectors, the system enables automated payoff calculation that improves care efficiency while keeping evaluation complexity manageable through mathematical standardization.
Solution Approach 2:
The patent implements a feedback mechanism where payoff calculations based on outcome comparisons feed back into the medical information system, continuously optimizing encounter vectors and intelligent matrices. This feedback loop automates the evaluation process, improving care efficiency over time while the system learns to manage complexity through adaptive optimization.
3Reliability
If comprehensive patient information is collected and analyzed, then patient outcomes improve, but information processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-computing and storing encounter vectors and intelligent matrices based on historical medical information. When new patient encounters occur, the system leverages these pre-computed structures to rapidly analyze comprehensive patient data without processing everything from scratch, thus improving outcome reliability while reducing real-time processing time.
Solution Approach 2:
The patent employs dynamic optimization techniques where encounter vectors and intelligent matrices are continuously updated and refined based on new data and outcomes. This dynamic approach allows the system to adapt to changing patient information efficiently, maintaining high outcome reliability while optimizing computational resource usage through iterative improvement rather than static comprehensive processing.
Data Source
AI summary
A medical information navigation engine (“MINE”) is provided. In some embodiments, the system computes a current patient encounter vector for a current patient encounter, and then an optimal patient encounter vector is computed by assuming a best case patient encounter in accordance with the organizational objectives. The system is then able to compute the difference between the best case encounter and the current patient encounter. This difference is used to compute a corresponding payoff using an intelligent matrix.