Metadata-Driven Code Generation for Clinical Trial Analysis
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Solution Overview
Problem
Clinical data analysis in traditional clinical trials is inefficient due to the need for manual coding and frequent re-coding of software programs for each new trial, consuming significant computing resources and programmer time.
Innovation Solution
A metadata-driven approach to generate program code that analyzes clinical data, using a code generation engine to create artifacts such as reports, where metadata describes the input data, operations, and output artifacts, allowing for the reuse of code and reducing the need for manual re-coding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual coding is used for each clinical trial, then the software can be customized to specific trial requirements, but the time and computing resources required increase significantly
Solution Approach 1:
The patent applies preliminary action by pre-defining standard artifacts, data structures, and analysis templates before clinical trials begin. These pre-configured elements can be reused across multiple trials, eliminating the need to manually code from scratch for each new trial while still allowing customization through configuration rather than programming.
Solution Approach 2:
The patent implements copying by creating reusable templates and standard artifacts that can be copied and adapted for different clinical trials. Instead of writing unique code for each trial, the system copies proven templates and modifies them through configuration parameters, dramatically reducing programming time while maintaining trial-specific requirements.
2Productivity
If standard artifacts are reused from study to study, then efficiency improves, but the ability to handle sufficiently different data and artifacts decreases
Solution Approach 1:
The patent applies dynamics by making the system configurable and adaptable rather than static. The standard artifacts include parameters and structures that can be dynamically adjusted through configuration files and metadata, allowing the same core template to handle different data types, analysis requirements, and output formats for sufficiently different studies.
Solution Approach 2:
The patent implements parameter changes by using configuration files and metadata that allow standard templates to be customized through parameter adjustment rather than code modification. This enables the same artifact template to handle different clinical trials by changing parameters such as data sources, analysis methods, and output formats without sacrificing efficiency.
3Reliability
If manual software writing is performed for each trial, then specific trial requirements are met, but computing resources and programmer time are consumed excessively
Solution Approach 1:
The patent applies universality by creating a multi-functional platform that can handle multiple clinical trials with different requirements using a common set of standardized artifacts and templates. This universal system reduces computing overhead by avoiding redundant code writing and compilation for each trial while maintaining the ability to meet trial-specific requirements through configuration.
Data Source
AI summary
Techniques are described for metadata-driven code generation to generate code for analyzing data from clinical trial(s). A code generation engine may generate program code based on metadata that is input to the engine. The metadata may describe the data to be input to the generated code, and one or more artifacts to be output by the generated code on execution. The metadata may also describe one or more operations to be performed on the input data and/or intermediate data. The metadata may include one or more of the following: inline code to be included in the generated code; references to stored code to be included in the generated code; and/or instructions to be run to generate the code. Artifact(s) may include, but are not limited to, reports such as tables, figures, and/or listings that describe the results of analyzing or otherwise processing the data by the generated program code.