AI Clinical Data Product Bias Assessment With Equity Feedback
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Solution Overview
Problem
Clinical Decision Support (CDS) tools using AI models face challenges in detecting and monitoring bias across diverse patient populations due to inadequate coverage in clinical studies, leading to inconsistent quality of results and potential health disparities, which current methods struggle to address efficiently and accurately.
Innovation Solution
A health equity assessment system that includes a processor and memory storing instructions to summarize clinical contexts, perform health equity assessments using disparity assessment models, calculate confidence scores for algorithmic bias, and store results, facilitating rapid and standardized data collection and analysis of CDPs to identify and rectify biases.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If clinical experts manually flag and analyze AI insights for bias detection, then measurement precision of algorithmic bias is improved, but loss of time and productivity deteriorate significantly
Solution Approach 1:
The system enables self-service bias detection by automatically performing health equity assessments on CDPs without requiring manual clinical expert intervention. The automated pipeline processes clinical studies, identifies population coverage gaps, and generates bias assessments independently, freeing clinicians from time-consuming manual analysis while maintaining detection accuracy through systematic automated evaluation.
Solution Approach 2:
The patent replaces the manual mechanical process of clinical expert review with an automated computational system. The system uses processors to execute algorithms that summarize clinical contexts, assess health equity, and calculate confidence scores automatically, substituting human manual labor with machine-based processing to resolve the time loss contradiction.
2Reliability
If comprehensive clinical studies with adequate population coverage are conducted, then reliability of AI algorithms is improved, but loss of time and resources for data collection deteriorate
Solution Approach 1:
The system performs preliminary action by automatically assessing the population coverage and identifying potential bias risks before AI models are deployed or updated. By analyzing clinical study designs and patient cohort representations in advance, the system flags coverage gaps that could lead to reliability issues, enabling preventive measures without requiring extensive additional clinical studies.
Solution Approach 2:
The patent introduces an intermediary health equity assessment system between clinical study execution and AI model deployment. This intermediary layer evaluates whether clinical studies have adequate population coverage and identifies bias risks, serving as a mediator that reduces the need for excessively comprehensive studies while maintaining algorithm reliability through systematic gap identification.
3Measurement precision
If manual labeling of training data for diverse patient cohorts is performed, then measurement precision of health equity assessment is improved, but productivity and ease of operation deteriorate
Solution Approach 1:
The system performs self-service data processing by automatically summarizing clinical contexts and identifying relevant patient cohort characteristics from unstructured clinical study data. The automated pipeline extracts demographic information, disease conditions, and population representation metrics without requiring manual labeling, maintaining measurement precision through systematic computational analysis while dramatically improving productivity.
4Reliability
If systematic detection and monitoring of bias at scale is implemented, then reliability of health equity assessment is improved, but device complexity and resource consumption increase
Solution Approach 1:
The patent applies segmentation by dividing the complex bias detection task into distinct modular components: clinical context summarization, health equity assessment, confidence score calculation, and result storage. Each module performs a specific function independently, allowing the system to achieve reliable comprehensive bias detection while managing complexity through modular architecture that can be implemented and maintained separately.
Data Source
AI summary
A confidence and health equity assessment system is provided for AI clinical data products (CDPs) that may be launched via a clinical workflow application. When a CDP is launched, a rating tool micro-application of the confidence and health equity assessment system 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. The clinical feedback may include demographic data with respect to various disparity factors of a patient population analyzed by the CDP. After aggregation of clinical feedback, the confidence and health equity assessment system performs a health equity assessment of the CDP and generates a confidence rating of the CDP, where the confidence rating is multi-dimensional score indicating a degree of confidence that the output of the CDP is not affected by algorithmic bias for various disparity factors.