Generic Aliasing Scheme for Clinical Data Extraction
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Solution Overview
Problem
Existing integrated data capture systems for clinical studies are limited in their ability to extract and transcribe clinical event data from electronic medical records (EMRs) to case report forms, as they can only handle a limited number of discrete data elements and are not designed to group data or document task completion.
Innovation Solution
The use of a generic aliasing scheme that allows for the specification of clinical event types or groups, specific items of clinical event data, and optional suffixes to describe how the data is grouped, enabling efficient extraction and transcription of clinical event data to case report forms across multiple clinical studies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a limited number of discrete data elements are specified for extraction from EMR systems, then the system complexity is reduced and ease of operation is improved, but the adaptability and versatility of the system deteriorate as it cannot handle studies requiring more data elements or different data grouping
Solution Approach 1:
The patent implements a universal aliasing scheme that can be applied across multiple disparate clinical studies. The aliasing scheme includes fields for specifying clinical event types, particular data items within groups, and suffixes for grouping descriptions. This single system can handle various data extraction requirements without needing custom programs for each study, making the system both easy to operate and highly adaptable.
2Adaptability or versatility
If custom programs are created for each clinical study requiring more than the limited number of data elements, then the adaptability and versatility of the system is improved, but the device complexity and loss of time increase due to the time-consuming and expensive development process
Solution Approach 1:
The system provides a universal data extraction platform that can handle multiple clinical studies with different data requirements through a single configurable aliasing scheme. This eliminates the need to create separate custom programs for each study, reducing both device complexity and development time while maintaining high adaptability to different study requirements.
Solution Approach 2:
The aliasing scheme is designed to be dynamically configurable for different clinical studies. The system allows specification of clinical event types, data items, and grouping suffixes that can be adjusted based on study requirements, enabling the same system to adapt to various data extraction needs without requiring structural changes or custom program development.
3Device complexity
If previous programs were designed to extract only discrete data elements, then the device complexity is reduced and ease of operation is improved, but the ability to group clinical event data and gather task documentation deteriorates
Solution Approach 1:
The aliasing scheme segments data extraction into hierarchical levels: clinical event types (e.g., vital signs), particular data items within those groups, and optional grouping suffixes. This segmentation allows the system to extract discrete data elements while simultaneously organizing them into meaningful groups based on time, task completion, or other criteria, enhancing data grouping capability without excessive complexity.
Data Source
AI summary
Methods, systems, and computer-storage media are provided for using a generic aliasing scheme to facilitate electronic transcription of groups of clinical event data extracted from an electronic medical record to case report forms associated with clinical studies. The generic aliasing scheme is also used to electronically transcribe documentation of task completion to case report forms associated with the clinical studies.