Health Data Risk Stratification via Segmented Regression
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The United States experiences high maternal and infant mortality rates, with significant disparities across demographic and socioeconomic factors, indicating a need for effective risk stratification methods to identify high-risk expectant mothers and improve healthcare outcomes.
Innovation Solution
A data analytic approach that aggregates health data from various sources, adjusts variables to focus on specific populations, and executes regression analysis to identify causal effects between health outcomes, enabling the stratification of risk factors for maternal and infant mortality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If comprehensive health data aggregation is performed across diverse populations, then measurement precision of health outcomes is improved, but device complexity and data processing requirements increase
Solution Approach 1:
The patent segments the aggregate population into distinct demographic groups (e.g., by race, socioeconomic status, geographic location) and performs separate regression analyses for each segment. This allows precise measurement of adverse health outcomes within each group while managing data complexity through structured categorization rather than treating all data uniformly.
Solution Approach 2:
The patent introduces demographic dimensions (race, socioeconomic status, geography) as additional analytical layers. By adding these dimensions to the health outcome data, the system achieves more precise measurements of disparities while organizing complexity through multidimensional data structuring rather than simple aggregation.
2Manufacturing precision
If variable adjustment is performed to eliminate subjects outside area of interest, then manufacturing precision of analysis focus is improved, but loss of information occurs
Solution Approach 1:
The patent extracts and removes subjects outside the area of interest (e.g., non-resident populations, unrelated demographic groups) from the analysis dataset. This sharpens the focus on the target population while the extracted data is preserved separately for reference, balancing analytical precision with information retention.
Solution Approach 2:
The patent applies different analytical treatments to different demographic segments within the data. By adjusting variables specifically for the area of interest while preserving other demographic information in structured form, the analysis achieves local precision for the target population without completely discarding broader contextual information.
3Reliability
If regression analysis is executed to identify causal effects, then reliability of risk identification is improved, but extent of automation and computational requirements increase
Solution Approach 1:
The patent performs preliminary data preparation steps including demographic segmentation, variable adjustment, and data cleaning before executing regression analysis. This preliminary structuring improves the reliability of causal effect identification by ensuring data quality, while reducing the computational burden during the actual regression phase through pre-organized datasets.
Solution Approach 2:
The system employs automated regression analysis algorithms that self-adjust and identify causal relationships without extensive manual intervention. The automation handles computational complexity while the structured approach to data organization ensures reliable results, balancing automated processing with methodological rigor.
Data Source
AI summary
Methods and systems to stratify risks for adverse health outcomes include aggregating data sets culled over a predetermined period of time; adjusting at least two variables within the aggregated data sets; and executing a regression analysis to identify an average causal effect between aggregated variables against at least one particular adverse health outcome.