Pharma Life Science AI solution

Dyviron conducted comprehensive functional and data quality testing, developed a robust test strategy, and implemented formal procedures, resulting in the successful implementation of solution for converting healthcare information into immediate business insights.

Client

The client in this project was a leading provider of AI solutions for the healthcare industry, specializing in converting healthcare information into immediate business insights.

Client's Challenge

Client aimed to develop and test a framework that leverages deep learning systems to analyze global healthcare records and in-house clinical study data. The challenge was to refine collaboration and involvement approaches throughout a product's life span across various sectors in healthcare, such as pharmaceuticals, educational institutions, medical equipment manufacturers, and investment companies. Additionally, the client sought to drastically reduce the time and expense required for data scrutiny while enhancing the capacity to synchronize real-time business tactics among their R&D, Product, and Commercialization units, as well as external teams.

Industry:
Pharma
Location:
US
QA Team:
5
Duration (month):
12

What We Did

  • Conducted comprehensive functional testing to ensure that the framework developed and effectively converted healthcare information into immediate business insights, meeting the diverse needs of clients across different sectors in healthcare.
  • Recognizing the critical importance of data accuracy and reliability, we conducted rigorous data quality testing to validate the integrity of both global healthcare records and in-house clinical study data processed by the framework.
  • Collaborating closely with the client, we developed and implemented a robust test strategy tailored to the unique requirements of the framework, ensuring thorough coverage of all functionalities and potential use cases.

Results

The implementation of the solution led to significant improvements in several key areas:

  • Facilitated refined collaboration and involvement approaches throughout a product's life span, enabling organizations to synchronize real-time business tactics among their R&D, Product, and Commercialization units, as well as external teams.
  • Organizations were able to drastically cut the time and expense required for data scrutiny, thanks to the efficiency and effectiveness of the framework in converting healthcare information into immediate business insights.
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