AICommon Sense AINLP

Accelerating Quality Investigations in Pharma

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Jean-Jo Adjizian
Managing Partner - Common Sense AI

Challenge – 

A leading pharmaceutical company is faced with prolonged cycle times in processing quality investigation (QI) records, a crucial component of its escalation process. The cycle time measures the duration between the awareness of a product complaint and its resolution, a timeline that has grown increasingly lengthy. This delay poses significant risks, particularly in scenarios requiring rapid response, such as the fight against COVID-19.

An efficient escalation process is vital for detecting critical errors and implementing necessary corrective actions. However, the sheer volume of records, the complexity of information, and the coordination required across multiple teams make this process heavy and prone to delays. These inefficiencies can have severe repercussions, including compromised product quality, delayed responses to critical issues, and potential adverse effects on both the company’s reputation and the health of its customers. A streamlined and effective approach is urgently needed to mitigate these risks and ensure timely resolution of quality issues.

Solution

To reduce the cycle time of the escalation process, Algorhythm Group made use of Artificial Intelligence (AI), to learn from large amount of data, and to guide the parties involved to take the correct decision.

Our AI model used state-of-the-art Natural Language Processing (NLP) algorithm to predict in the early stage of the process if a QI would become an escalation.

The overall solution was integrated within a fully automated flow that ensures for faster processing.

Impact

The solution allowed for an original cycle time of 3-4 weeks to be reduced to a time of less than 1 week.

Thanks to the intelligent automation flow, the escalation process has become more effective, with faster detection and processing of adverse events. A fully automated flow allow for easier maintenance and a reduce in time of human effort.

Intelligent automation to reduce process cycle time & increase consistent decision-making

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