Personalized Co-Therapy Regimen Generation System
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Solution Overview
Problem
Current medical treatments often rely on generic dosage regimens based on clinical trials, failing to account for individual patient needs, particularly for patients with co-morbidities or complex conditions, leading to suboptimal care and decreased compliance due to ineffective or poorly tailored therapies.
Innovation Solution
A method and system that generate personalized co-therapy regimens by processing patient-specific data, including physiological, environmental, and behavioral measurements, to create tailored treatment plans that can be dynamically adjusted to optimize patient outcomes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If generic dosage regimens based on clinical trials are used, then treatment coverage is broad and applicable to many patients, but treatment efficacy for individual patients decreases
Solution Approach 1:
The patent segments the population into different patient groups based on genetic profiles, disease characteristics, and response patterns. By dividing the homogeneous generic treatment approach into heterogeneous personalized groups, the system achieves both broad coverage through multiple segments and high efficacy within each segment through tailored regimens.
Solution Approach 2:
The patent applies local quality by customizing treatment parameters (dosage, frequency, duration) specifically for each patient's local characteristics such as genetic makeup, disease severity, and comorbidities. This allows the treatment to be broadly applicable across populations while being locally optimized for individual efficacy.
2Adaptability or versatility
If co-therapy regimens are prescribed without rigorous clinical trials, then treatment options are expanded for complex conditions, but treatment reliability decreases
Solution Approach 1:
The patent incorporates feedback mechanisms where treatment responses are continuously monitored and used to adjust and optimize co-therapy regimens. Real-world outcome data feeds back into the system to validate and refine combination therapies, ensuring reliability even for innovative treatment approaches not yet subjected to extensive clinical trials.
Solution Approach 2:
The patent performs preliminary screening and modeling of co-therapy combinations using available data before full implementation. By pre-assessing potential treatment combinations through computational models and preliminary studies, the system expands treatment options while maintaining reliability through advance validation.
3Ease of operation
If treatment regimens are not dynamically adjusted, then treatment simplicity is maintained, but treatment efficacy over time decreases
Solution Approach 1:
The patent implements dynamic treatment regimens that automatically adjust dosage and parameters based on real-time patient data, disease progression, and response measurements. This dynamic adaptation maintains efficacy over time while the system manages the complexity through automated algorithms, preserving ease of operation for the patient.
Solution Approach 2:
The patent enables self-service through automated treatment adjustment systems that use patient-generated data to automatically optimize regimens without requiring constant healthcare provider intervention. This maintains simplicity for patients while achieving dynamic optimization for efficacy.
4Measurement precision
If patient data collection is comprehensive, then personalized treatment accuracy is improved, but system complexity increases
Solution Approach 1:
The patent employs universal data collection platforms and standardized processing algorithms that can handle multiple data types (genetic, clinical, lifestyle) through a single integrated system. This multi-functional approach achieves high personalization accuracy while managing complexity through unified interfaces and standardized protocols.
Data Source
AI summary
The present disclosure relates to methods and systems suitable for use in identifying and providing personalised medicine to a patient. In some aspects, systems and method generate a co-therapy regimen for a patient suffering from a disease or condition. An identification of a co-therapy suitable to treat the disease or condition is received. A desired patient endpoint and a patient position are received, wherein the patient position is defined relative to the desired patient endpoint. A dataset relating to the patient is stored. The dataset comprises one or more patient data based on patient-related measurements. The dataset, the patient position and the desired patient endpoint are processed to generate a regimen for the co-therapy. The regimen is stored in a database.