Biomedical Innovation Datametrics Dashboard Simulation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for government incentivization of private sector investment in biomedical innovation face challenges such as complexity, uncertainty, and computational intractability in predicting responses to policies, leading to difficulties in balancing competing objectives like DALY burden reduction, public health costs, and industry NPV maximization.
Innovation Solution
A microsimulation system using nested stochastic optimization and perturbation algorithms to simulate the biomedical innovation ecosystem, incorporating data from various sources to evaluate government policy decisions and private sector responses, and identify satisficing solutions that balance multiple objectives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If government policies are implemented to incentivize private sector biomedical innovation, then industry NPV and public health outcomes can be improved, but the complexity and uncertainty of predicting private sector responses increases
Solution Approach 1:
The system segments the complex prediction problem into multiple discrete components: policy parameter inputs, private sector response models, simulation engines, and outcome metrics. Each component processes specific aspects of the policy-innovation ecosystem independently, then integrates results to provide comprehensive predictions while managing overall system complexity.
Solution Approach 2:
The patent introduces computational simulation models as intermediary tools between government policy decisions and private sector responses. These simulation intermediaries translate policy parameters into predicted industry reactions, allowing policymakers to anticipate outcomes without direct observation of private sector decision-making processes.
2Reliability
If multiple policy objectives are pursued simultaneously (DALY burden reduction, public health costs, industry NPV), then comprehensive public health benefits are achieved, but the difficulty of detecting and measuring optimal policy combinations increases
Solution Approach 1:
The simulation system performs multiple evaluation functions simultaneously: it assesses DALY burden reduction, public health cost impacts, and industry NPV effects within a single integrated model. This multi-functional approach allows comprehensive policy evaluation without requiring separate analysis systems for each objective, reducing the overall difficulty of measuring and optimizing multiple competing goals.
Solution Approach 2:
The system enables policymakers to adjust policy parameters (funding levels, incentive structures, regulatory approaches) and immediately observe their effects on multiple objectives through simulation. This parameter sensitivity analysis helps identify optimal policy combinations by showing how changes in one parameter affect multiple outcomes simultaneously, making the measurement and optimization process more tractable.
3Measurement precision
If detailed simulation models are used to predict private sector responses, then prediction accuracy is improved, but computational intractability increases
Solution Approach 1:
The simulation model implements partial detailing by focusing computational resources on the most critical aspects of private sector decision-making while using simplified representations for less influential factors. This selective level of detail maintains prediction accuracy for key outcomes while reducing overall computational burden to tractable levels.
Solution Approach 2:
The system uses automated computational algorithms that self-optimize the simulation parameters and computational depth based on the specific policy scenario being analyzed. The model automatically adjusts its own computational requirements, allocating more resources to complex interaction areas and fewer resources to straightforward predictions, thereby maintaining accuracy while managing computational intractability.
Data Source
AI summary
Systems and methods for implementing a biomedical innovations datametrics dashboard are presented. The dashboard can utilize data from real-world data sources to inform a simulation model which performs simulations of the biomedical innovation pipeline and related entities and processes. The dashboard can utilize the simulation model as well as feedback solicited from a user to assist in performing multi-objective optimization according to the user's preferences. The dashboard can help policy decision-makers and other stakeholders decide on a combination of policies that best incentivizes private sector biomedical innovation in order to achieve a satisfactory set of outcomes, which may include, but is not limited to, reductions in DALYs, healthcare costs, increases in industry NPV, and improved clinical trial quality.