Clinical Data Product Equity Assessment for Algorithmic Bias
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Solution Overview
Problem
Existing clinical decision support (CDS) tools using AI models face challenges in systematically detecting and monitoring bias due to inadequate patient population coverage, leading to inconsistent quality of results across different demographics, which can exacerbate healthcare disparities and legal liabilities.
Innovation Solution
A health equity assessment system that summarizes clinical contexts, performs health equity assessments using disparity assessment models, and calculates a confidence score to indicate the degree of algorithmic bias, enabling efficient and standardized data collection and feedback aggregation to identify and rectify biases in clinical data products (CDPs).
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If clinical studies are conducted with limited patient population coverage, then the clinical studies can be completed with reasonable resources, but the AI algorithms may exhibit bias towards certain patient populations
Solution Approach 1:
The system performs health equity assessments and calculates confidence scores for AI algorithms before they are deployed to clinical use. Disparity assessment models evaluate potential biases across different patient populations in advance, allowing developers to identify and address algorithmic bias issues before the algorithms cause harm in practice.
Solution Approach 2:
The system continuously monitors AI algorithm performance across diverse patient populations and provides feedback through confidence scores that indicate the degree of algorithmic bias. This feedback loop enables ongoing adjustment and improvement of algorithms to reduce bias while maintaining clinical effectiveness.
2Measurement precision
If clinical experts manually flag and analyze AI insights for bias, then detailed assessment can be performed, but the process is time consuming and burdensome for already overworked clinicians
Solution Approach 1:
The system replaces manual clinical expert review with automated disparity assessment models that use machine learning to evaluate AI algorithms for bias. These computational models analyze patient data and algorithm outputs to identify potential biases, providing accurate assessments without requiring clinicians to spend additional time on manual review.
Solution Approach 2:
The system enables AI algorithms to self-assess their own potential biases through the disparity assessment models. The confidence scores are automatically generated by the system itself, allowing continuous monitoring without external manual intervention, thus eliminating the time burden on clinicians while maintaining assessment quality.
Data Source
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AI summary
A confidence and health equity assessment system (102, 214) is provided for AI clinical data products (CDPs (1102, 206, 175, 402)) that may be launched via a clinical workflow application (160, 212). When a CDP (904, 1102, 206, 175, 302, 402) is launched, a rating tool micro-application of the confidence and health equity assessment system (102, 214) may be launched. When conditions are met, the rating tool micro-application may request clinical feedback from the user regarding the equitability, suitability, appropriateness, accuracy, or quality of an output of the CDP (904, 1102, 206, 175, 302, 402). The clinical feedback may include demographic data with respect to various disparity factors of a patient population analyzed by the CDP (904, 1102, 206, 175, 302, 402). After aggregation of clinical feedback, the confidence and health equity assessment system (102, 214) performs a health equity assessment (408) of the CDP (904, 1102, 206, 175, 302, 402) and generates a confidence rating of the CDP (904, 1102, 206, 175, 302, 402), where the confidence rating is a multi-dimensional score indicating a degree of confidence that the output of the CDP is not affected by algorithmic bias for various disparity factors.