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Life Sciences
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Patient-centered Care Models Taking Center Stage in Life Sciences
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25 Oct 2022
Simplifying the edit check configuration
In pharma manufacturing, decision making for drug development relies heavily on the safety and efficacy analysis of the medicines. The quality and integrity of data sourced from clinical trials is vital for the success of such analyses. During clinical data management, data is captured into electronic case report forms (eCRFs). Edit check programs or algorithms validate the data for any errors, alert the user when there is a questionable data entry, and ensure the clinical trial data adhere to standards.
Edit check configuration comes with its own set of challenges. Clinical trial managers get into an arduous cycle of working with multiple stakeholders from the application development team and developers involving multiple iterations of testing and approvals. The other approach calls for a low-code no-code build interface in the application and specific skill sets about the tool and in low-code no-code edit check building. Both these approaches take a long time as well as demand specialized skill sets.
Proposed approach
A new approach that combines natural language input, natural language processing, and generative AI addresses this challenge and accelerates edit check even by clinical trial managers that don’t have the necessary skill sets. Following is the sequence in the new approach:
- Accept custom edit check requirement from user in plain natural text.
- Leverage natural language processing to understand the requirement and context.
- Use Generative AI to create algorithm/ code that can perform the edit check.
- Embed the machine-generated code with the eCRF forms so that when data entry operators submit the form, the edit checks are validated.
The solution can also analyze the eCRF form to show context-aware smart recommendations to the user to suggest edit checks that might be useful for that eCRF form.
On average, an edit check development takes weeks and months. This proposed solution can reduce this time to minutes or even hours, which has the potential to transform the clinical trial edit check configuration landscape.
The generative AI model will generate a JavaScript code which, after few sanity checks, will be embedded with the eCRF form.
When the user submits the form, the JavaScript code will execute and perform the field validation checks and show appropriate validation error message to the user if validation fails.
To read the whole paper, click here.
About the authors
Image of Vandita Tripathi
Vandita Tripathi
Vandita Tripathi is working as the Product and Portfolio Head for TCS ADD™, part of the Life Sciences unit at TCS.
Image of Manas Saha
Manas Saha
Manas is a .NET and Azure architect and Technical Lead with 16+ years of experience in developing and designing software solutions using Microsoft technologies.
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