Medication Use Duration Determination Through Iterative Outcome Linkage
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Solution Overview
Problem
Existing methods fail to accurately determine the optimal duration for medication use to ensure patient safety and efficacy, potentially leading to harmful health effects from prolonged medication use.
Innovation Solution
A computer-implemented method using data linkage assessment and causality inference techniques to iteratively determine a medication's optimal use duration by analyzing observational health data, adjusting thresholds based on linkage measure values until a predefined risk assessment threshold is met.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If medication is used for longer duration to ensure treatment effectiveness, then treatment efficacy is improved, but patient health risks increase due to potential harmful effects from prolonged use
Solution Approach 1:
The patent determines optimal medication duration by analyzing the relationship between medication use duration and health outcomes from observational data, identifying specific duration thresholds (e.g., 30 days, 90 days, 180 days) where the beneficial effects plateau or begin to diminish. This transforms the medication duration from an indefinite parameter to a precisely controlled parameter with optimal values identified through data analysis.
Solution Approach 2:
The system uses observational health data to establish feedback loops that continuously assess the relationship between medication duration and health outcomes. By monitoring linkage measure values and comparing them against risk assessment thresholds, the system provides feedback that guides clinicians in determining the appropriate duration of medication use, adjusting recommendations based on evidence from the data analysis.
2Ease of operation
If fixed duration thresholds are used for medication prescriptions to simplify clinical decision-making, then ease of operation is improved, but measurement precision deteriorates because individual patient variations are not accounted for
Solution Approach 1:
The patent implements a dynamic threshold adjustment mechanism that adapts duration recommendations based on specific medication types, patient populations, and outcome measures. Rather than applying a single fixed threshold to all medications, the system dynamically determines appropriate thresholds through data analysis, allowing the optimal duration to vary based on the specific clinical context while still providing actionable guidance.
Solution Approach 2:
The system segments the medication usage data by different medication types, patient characteristics, and outcome measures to identify specific duration thresholds for each segment. This segmentation allows for more precise duration recommendations tailored to specific clinical scenarios while maintaining the simplicity of threshold-based decision-making. The data frame is divided into subsets based on covariates and outcomes to determine medication-specific optimal durations.
Data Source
AI summary
A processor determines use duration for a medication of interest for providing patient treatment. Using a specified duration threshold, the processor performs a data linkage assessment on a data frame that includes at least covariates and medical outcome to determine data linkage between the covariates and the medical outcome, and the specified duration threshold. Based on the data linkage, the processor determines a linkage measure value that determines strength of an effect of taking the medication of interest, on the medical outcome. Until one or more stopping criteria is met, the processor performs an iterative processing of: performing of the data linkage assessment using different specified duration threshold and filtered data frame, and determining of the linkage measure value. Responsive to the one or more stopping criteria being met, the processor identifies the current iteration's specified duration threshold as a target use duration of the medication of interest.