Geographical Attribute Mapping With Data Gap Imputation
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Solution Overview
Problem
Healthcare organizations face challenges in accurately identifying and addressing health disparities and social vulnerabilities among their patient populations due to incomplete data and fragmented measures of social determinants of health, which complicates efforts to improve health equity and resource allocation.
Innovation Solution
A system that integrates mapping data, population data, and domain data to generate a vulnerability index, using imputation algorithms to fill data gaps and provide neighborhood-level insights, leveraging clinical data from Vizient, Inc.'s CDB to analyze socioeconomic, transportation, and health factors, and display actionable insights for healthcare providers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If publicly available data from Medicare, Medicaid, and CDC is used to identify health issues and social vulnerabilities, then data availability is improved, but measurement precision of patient-specific obstacles to care deteriorates due to fragmented and incomplete data
Solution Approach 1:
The patent combines multiple data sources including Medicare, Medicaid, CDC Social Vulnerability Index, Distressed Communities Index, and Area Deprivation Index into a unified vulnerability index. This integration merges fragmented data from different programs and sources to create a comprehensive view of patient social determinants of health, resolving the contradiction between data availability and measurement precision by synthesizing quantity into quality through unified analysis
2Loss of information
If multiple layers of social determinants of health are included in equity measurements, then completeness of equity assessment is improved, but device complexity of the measurement system deteriorates
Solution Approach 1:
The patent segments the complex measurement system into distinct modules: data collection module, vulnerability index calculation module, and equity assessment module. Each module handles specific social determinants of health layers separately, allowing the system to process multiple layers of complexity while maintaining manageable system architecture through functional segmentation
3Measurement precision
If social needs factors are included in risk adjustment measures, then risk adjustment accuracy is improved, but masking of social needs and potential inequities occurs
Solution Approach 1:
The patent implements feedback mechanisms that continuously monitor and report on the distribution of vulnerability indices across different provider populations. This feedback loop allows the system to identify when social needs factors are being masked by aggregate risk adjustment measures, enabling corrective actions to maintain both accuracy and equity visibility simultaneously
Data Source
AI summary
In some embodiments, the system includes one or more computer implemented algorithms that when executed combine various sources of data onto a map. In some embodiments, the system is configured to modify existing map boundaries to include and/or exclude areas. In some embodiments, the system imputes missing data within tracts by extending and/or including data from surrounding tracts. In some embodiments, the system can link disconnected tracts for the purpose of imputing missing data.