ECCD Engine for Contextual Clinical Data Integration
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Solution Overview
Problem
Current medical software systems fail to effectively integrate and present relevant medical information from various data sources in a user-friendly manner, requiring users to navigate multiple systems and search for pertinent data, which is inefficient and impractical.
Innovation Solution
The implementation of an Evolving Contextual Clinical Data (ECCD) engine that analyzes user interactions to generate personalized rules for data retrieval and presentation, integrating data from multiple sources such as EMRs, PACS, LIS, and RIS, and displaying relevant information in a logical and context-specific way within a single viewer.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If multiple data sources are integrated into a single system, then information completeness is improved, but system complexity increases
Solution Approach 1:
The patent introduces an Evolving Contextual Clinical Data (ECCD) engine as an intermediary layer between multiple data sources (EMR, PACS, LIS, RIS) and the user interface. This engine aggregates data from diverse sources, manages complexity internally, and presents unified contextualized information to users, thereby maintaining information completeness while shielding users from system complexity.
Solution Approach 2:
The ECCD engine serves multiple functions simultaneously: it acts as a data aggregator, contextualizer, presenter, and adaptive learning system. By consolidating these diverse functions into a single multi-functional engine, the system integrates information from multiple sources without requiring users to interact with each source separately, thus improving information completeness while managing complexity centrally.
2Loss of information
If all available medical data is displayed to users, then information completeness is improved, but ease of operation deteriorates
Solution Approach 1:
The ECCD engine applies local quality by contextualizing data presentation based on specific clinical scenarios, user roles, and immediate needs. Rather than displaying all data uniformly, the engine tailors the presentation to the local context (e.g., displaying only relevant test results for a specific diagnosis), making the system easier to operate while maintaining access to complete information when needed.
Solution Approach 2:
The system dynamically adapts data presentation based on user interactions, behavioral patterns, and evolving clinical contexts. The ECCD engine learns from user behavior and automatically adjusts what information is displayed, when, and in what format, transforming the static information display into a dynamic, user-adaptive interface that improves ease of operation while preserving information completeness.
3Ease of operation
If the system adapts to individual user behavior patterns, then ease of operation is improved, but device complexity increases
Solution Approach 1:
The ECCD engine implements self-service by automatically observing user interactions, analyzing behavioral patterns, and adapting the system to individual users without requiring manual configuration. The engine learns and evolves autonomously, improving ease of operation for each user while encapsulating the complexity of adaptive algorithms within the engine itself, shielding users from the underlying software complexity.
Data Source
AI summary
A medical information server receives a signal from a client device over a network, representing a first user interaction of a user with respect to first medical information displayed to a user. A user interaction analyzer invokes a first set of ECCD rules associated with the user based on the first user interaction to determine medical data categories that the user is likely interested in. The first set of ECCD rules was generated by an ECCD engine based on prior user interactions of the user. A data retrieval module accesses medical data servers corresponding to the medical data categories to retrieve medical data of the medical data categories. A view generator integrates the retrieved medical data to generate one or more views of second medical information and transmits the views of second medical information to a client device to be displayed on a display of the client device.