Medication Risk Stratification Algorithm for Adverse Event Prediction
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Solution Overview
Problem
Current methods for predicting multi-drug interactions and adverse drug events are complex and inefficient, particularly when patients are taking multiple medications, leading to significant health and financial issues.
Innovation Solution
A system and method for population-based medication risk stratification that uses algorithms to combine pharmacological characteristics of medications with patient drug regimen data, generating a personalized medication risk score to identify high-risk patients and mitigate medication risks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If current methodology for predicting multi-drug interactions is used, then drug-drug interaction detection is performed, but the system becomes immensely complex when patients take multiple medications
Solution Approach 1:
The patent segments the complex drug interaction prediction system into multiple independent risk factors (e.g., anticholinergic burden, sedative burden, QT-prolongation risk, competitive inhibition). Each risk factor is calculated separately using specific algorithms, and then the results are aggregated into an overall risk score. This segmentation reduces the complexity of individual components while maintaining comprehensive prediction capability.
Solution Approach 2:
The patent creates a universal risk assessment framework that can evaluate multiple types of drug interactions and adverse effects using a single integrated system. The same algorithmic structure can assess different risk factors (anticholinergic, sedative, cardiac, metabolic) by simply changing the input parameters, making the system multi-functional without requiring separate specialized systems for each interaction type.
2Measurement precision
If comprehensive drug interaction analysis is performed for all medication pairs, then interaction detection is thorough, but the process becomes time-consuming and inefficient
Solution Approach 1:
The patent performs preliminary classification of drugs into different categories (e.g., anticholinergics, sedatives, QT-prolonging agents, CYP450 substrates/inhibitors/inducers) before conducting interaction analysis. This preliminary sorting allows the system to pre-identify high-risk medication combinations and focus computational resources on the most critical interactions, reducing overall processing time while maintaining detection accuracy.
Solution Approach 2:
The patent changes the approach from analyzing every possible drug pair individually to using parameter-based risk factor calculation. By transforming the analysis into parameter-driven algorithms (e.g., summing anticholinergic scores, calculating QT-prolongation risk based on specific drug properties), the system achieves thorough assessment with significantly reduced computational complexity and processing time.
3Reliability
If population-based risk stratification is implemented, then high-risk patients can be identified, but the system requires complex algorithms to process and analyze data
Solution Approach 1:
The patent segments the risk stratification process into distinct, manageable algorithms corresponding to different risk factors. Each algorithm processes specific data inputs (e.g., medication lists, patient demographics, lab values) and produces specific risk factor outputs. This modular approach maintains high stratification accuracy while making the overall system more manageable and easier to implement.
Solution Approach 2:
The patent introduces intermediate risk factor calculations as mediators between raw input data and final risk stratification. Instead of directly complex algorithms processing all patient data at once, the system uses intermediate representations (e.g., anticholinergic burden score, sedative burden score) that simplify the data structure and enable more efficient processing while preserving the necessary information for accurate risk stratification.
Data Source
AI summary
Embodiments of the invention relate to a system and method for population-based medication risk stratification and for generating a personalized medication risk score. The system and method may pertain to a software that relates pharmacological characteristics of medications and patient's drug regimen data into algorithms that (1) enable identification of high-risk patients for adverse drug events within a population distribution, and (2) allow computation of a personalized medication risk score which provides personalized, evidence-based information for safer drug use to mitigate medication risks.