Uniform Database Schema for Clinical Study Data Comparison
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Solution Overview
Problem
The drug development process is lengthy and costly, with significant time spent on data reformatting due to lack of a common format for clinical and non-clinical study data, hindering efficient analysis and comparison across studies.
Innovation Solution
A database schema is developed to house research studies in a uniform manner, allowing for efficient analysis and comparison of data across different products and studies, with the ability to relate planned and actual study activities and interventions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data from multiple studies are stored in different formats, then each study can be conducted with its own methodology, but data reformatting time increases significantly
Solution Approach 1:
The patent implements a universal database schema that can accommodate multiple study formats and methodologies through standardized field definitions. The schema uses flexible data types and optional fields to accept diverse input formats while maintaining a consistent internal structure, allowing the same database to handle different study types without requiring separate storage systems for each format.
Solution Approach 2:
The patent introduces a data transformation layer that acts as an intermediary between diverse study data sources and the standardized database schema. This mediator automatically converts various input formats into the unified schema structure, eliminating manual reformatting efforts and reducing time loss while preserving the adaptability to handle different study methodologies.
2Productivity
If a uniform database schema is implemented, then data analysis efficiency improves, but data storage structure complexity increases
Solution Approach 1:
The patent divides the database schema into distinct modular sections or tables, each handling specific types of data (e.g., study metadata, patient information, treatment details, outcomes). This segmentation allows for efficient querying and analysis by focusing on relevant data subsets while reducing the apparent complexity through organized, manageable units that can be independently understood and maintained.
Solution Approach 2:
The patent applies different data structure characteristics to different sections of the database schema based on local requirements. Critical analysis fields use highly structured formats for efficient querying, while optional or variable fields use more flexible structures. This local optimization maintains high analysis efficiency for essential data while managing overall schema complexity through differentiated design.
3Reliability
If detailed tracking of planned vs actual activities is implemented, then study protocol compliance can be monitored, but data collection complexity increases
Solution Approach 1:
The patent implements pre-defined activity templates and protocols within the database schema that automatically track planned activities. By establishing the expected workflow and data collection points in advance, the system automatically captures compliance information during normal data entry without requiring additional manual tracking efforts, thus monitoring protocol adherence while minimizing added complexity.
Data Source
AI summary
A computer is provided for processing data from a plurality of studies of investigational products in a manner that allows the data from one study to be compared to one or more other studies. Each study includes a plurality of planned activities, a plurality of actual activities, and a plurality of assessments. The computer includes a memory, a database schema and a database. The memory is configured to store an operating system which includes an object-oriented database engine. The database schema is maintained by the object-oriented database engine of the operating system. The database schema has a plurality of uniquely defined database objects. For each study, the uniquely defined database objects include respective sets of objects that store the plurality of planned activities, actual activities, and assessments. The database is populated with data associated with the plurality of planned activities, actual activities, and assessments. The respective sets of objects that store the plurality of planned activities, actual activities, and assessments for each study share common attributes and relationships. Each planned activity, actual activity, and assessment has an associated data type that is the same for different studies.