Iterative Cohort Refinement via Visual Analytics
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Solution Overview
Problem
Traditional methods for defining and analyzing patient cohorts in retrospective cohort analysis are limited, as they require manual selection of attributes from large lists and rely on 'black box' batch analytics, making it difficult for users to apply domain expertise and visualize data effectively.
Innovation Solution
An integrated system that uses visual exploration and data analytics to iteratively refine cohorts by defining initial seeding, applying filters, and expanding cohorts through interactive visualization and analytics, allowing users to request on-demand processing and make new discoveries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual selection of attributes is used to define cohort constraints, then users can apply domain expertise to refine cohorts, but it becomes difficult to select attributes from large lists of hundreds or thousands of patient attributes
Solution Approach 1:
The patent segments the attribute selection process into multiple stages: initial automatic attribute identification, intermediate visual exploration with filtering, and final cohort definition. This breaks down the complex task of selecting from hundreds of attributes into manageable steps, allowing users to focus on refining rather than discovering attributes from scratch.
Solution Approach 2:
The patent introduces visual exploration tools and analytics as intermediary components between the raw data and the final cohort definition. These intermediaries automatically process and filter attributes, presenting refined options to users through visual interfaces, thereby reducing the cognitive load of manual attribute selection while preserving domain expertise application.
2Productivity
If batch analytics are used to determine patient cohorts, then computational determination of meaningful groups can be achieved, but users have few ways to apply their domain expertise to influence the process
Solution Approach 1:
The patent transforms the static batch analytics process into a dynamic interactive process. Users can iteratively refine cohort definitions by applying filters, adjusting parameters, and receiving immediate visual feedback. This dynamic interaction allows domain expertise to continuously influence the analytics process rather than being confined to pre-defined batch processing steps.
Solution Approach 2:
The patent implements feedback loops where users apply filters and adjustments to cohort definitions, and the system immediately visualizes the impact of these changes. This feedback mechanism allows users to see the effect of their domain expertise applications in real-time, enabling iterative refinement while maintaining computational efficiency through optimized query processing.
3Reliability
If traditional cohort analysis methods are used, then retrospective analysis can be performed, but it lacks integrated visual exploration and interactive refinement capabilities
Solution Approach 1:
The patent merges multiple functional components into a single integrated system: visual exploration tools, analytics processing, cohort management, and result visualization. This consolidation allows reliable retrospective analysis while managing complexity through unified architecture, where components communicate through standardized interfaces and share common data models.
Data Source
AI summary
Methods and apparatus are provided for iterative refinement of cohorts using visual exploration and data analytics. A cohort comprised of multiple data objects is defined by obtaining an initial cohort seeding; visualizing the initial cohort using a selected view to present a current cohort; reducing the current cohort using one or more visual filters; visualizing the current cohort using a selected view; expanding the current cohort using one or more selected analytics; and determining whether the current cohort should be further modified using one or more of additional reductions and additional expansions. Cohorts can be passed between views and analytics via drag-and-drop interactions as an analysis unfolds.