Population-Based Medical Product Selection for Patient Outcome Optimization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Care providers face challenges in optimizing patient outcomes across diverse populations with varying medical profiles due to environmental and socioeconomic factors, leading to inefficiencies in medical product deployment and increased costs.
Innovation Solution
A system and method for dynamically selecting and adjusting medical products based on a population's changing medical profile over time, using a computer processor to optimize the deployment of medical products by maximizing effectiveness while minimizing costs, considering factors like prophylactic and therapeutic impact, and incorporating real-time feedback loops.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If care providers deploy a standardized fleet of medical products across all patient populations, then device complexity is reduced and ease of operation is improved, but patient outcomes deteriorate due to lack of optimization for specific population needs
Solution Approach 1:
The patent segments the patient population into distinct groups based on medical profiles (e.g., obesity-related conditions, respiratory illness, diabetes). Each segment receives a customized subset of medical products optimized for their specific needs, rather than a standardized fleet. This segmentation allows care providers to maintain ease of operation through standardized processes while improving patient outcomes through targeted product selection for each population segment.
Solution Approach 2:
The system dynamically adjusts the medical product fleet allocation based on changing population health profiles over time. As population characteristics evolve (e.g., increasing obesity rates, changing disease prevalence), the optimization algorithm recursively updates which medical products are deployed to each population segment. This dynamic adaptation maintains reliability of patient outcomes while preserving ease of operation through automated decision-making.
2Reliability
If care providers optimize medical product selection for specific population profiles, then patient outcomes are improved, but device complexity and decision-making complexity increase
Solution Approach 1:
The system implements self-service through an automated optimization algorithm that autonomously analyzes population health profiles and determines the optimal medical product fleet composition. The algorithm recursively processes population data, evaluates multiple medical products against specific population needs, and generates deployment recommendations without requiring complex manual analysis by care providers. This automation reduces the perceived complexity for users while maintaining optimized patient outcomes.
Solution Approach 2:
The system incorporates feedback loops where patient outcomes and population profile changes are continuously monitored and fed back into the optimization algorithm. This feedback mechanism allows the system to learn from actual performance data and adjust medical product selections accordingly, simplifying the decision-making process by using data-driven insights rather than complex manual evaluation. The feedback-driven approach maintains high patient outcomes while reducing selection complexity through evidence-based automated decisions.
3Productivity
If care providers use risk sharing models with payers, then cost effectiveness is improved, but the pressure to prevent recurring care increases complexity in managing minimum standards of care
Solution Approach 1:
The system changes key parameters by shifting from population-wide standardized care to segmented, optimized care based on specific health profiles. By analyzing population characteristics (e.g., obesity prevalence, respiratory illness rates, diabetes incidence) and adjusting medical product selection accordingly, the system achieves cost-effectiveness through targeted interventions. This parameter change from uniform to customized care reduces overall costs while managing complexity through data-driven decision-making that identifies high-impact interventions for each population segment.
Solution Approach 2:
The system performs preliminary action by proactively identifying population health needs and deploying appropriate medical products before problems escalate. By analyzing population profiles and predicting future health challenges (e.g., increasing obesity-related conditions), the system pre-positiones relevant medical products and interventions. This preliminary action prevents recurring care needs and reduces complexity by addressing issues early through structured population health management rather than reactive care.
Data Source
Figure 1
Figure 2
Figure 3A~3C
AI summary
A system and method for improving patient outcomes by dynamically selecting the medical products used for a population to optimize patient outcomes is disclosed.