Medical Data Visualization via Normalization and Mediator Patterns
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Solution Overview
Problem
The healthcare industry faces challenges in comparing and integrating medical data from various entities due to differing dataset formats and sources, leading to discontinuity and conflicting information, especially with the shift towards affiliated medical practitioners and the impact of the Patient Protection and Affordable Care Act.
Innovation Solution
A system comprising a database, application layer, and dashboard that normalizes medical data from multiple sources, enabling automated comparison and visualization using Venn diagrams, scatter plots, and orbit plots, allowing users to interactively explore patient populations and trends across different medical providers and payor organizations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If medical data is stored in separate databases by different entities (providers, payors, labs), then each entity can manage its own data independently, but data continuity and integration across entities deteriorate
Solution Approach 1:
The patent implements an intermediary layer (application layer with standardized data exchange protocols) between separate entity databases that enables data integration without compromising individual entity autonomy. This mediator translates and normalizes data from different sources into a unified view, resolving the contradiction between data independence and data continuity.
2Productivity
If practitioners move from independent practice to affiliated organizations, then practice group comparison capabilities improve, but data privacy and autonomy concerns worsen
Solution Approach 1:
The system applies local quality by allowing different levels of data access and aggregation - individual practitioner data remains private at the local level while enabling comparative analysis at the organizational level through standardized metrics. This resolves the contradiction by maintaining data autonomy locally while enabling productivity improvements globally.
3Quantity of substance
If multiple dataset formats from different sources are integrated, then comprehensive patient information improves, but data normalization and processing complexity worsen
Solution Approach 1:
The patent transforms heterogeneous data from multiple sources by changing parameters through standardized data models and normalization protocols. This allows comprehensive patient information to be aggregated while managing processing complexity through consistent data transformation rules across all data sources.
4Loss of information
If detailed patient data from multiple providers is aggregated, then comprehensive patient view improves, but data conflicts and inconsistencies worsen
Solution Approach 1:
The system implements feedback mechanisms that detect and resolve data conflicts through validation rules, cross-referencing, and reconciliation protocols. When inconsistencies arise from multiple provider sources, the feedback loop identifies and corrects discrepancies, maintaining both information completeness and data consistency.
Data Source
AI summary
The systems and methods of the present application includes embodiments that allow users to more easily and efficiently compare medical data in an automated, computerized system using a variety of visualization tools, by operation on datasets sourced from a variety of entities.