As a software vendor providing quality and regulatory solutions to the pharmaceutical industry, Scigeniq is keenly aware of artificial intelligence’s (AI) transformative potential. The impact goes far beyond ChatGPT and its cousins’ generative capabilities; the impact on quality systems is much more focused on data-driven capabilities.
We want to share how AI is shaping our roadmap for our software as a service (SaaS) product suite and how we see it enhancing quality systems and software applications in the pharma sector in the near future.
Laying the Foundation: Digitization and Data Collection
It seems obvious that exploiting AI’s capabilities begins with digitization, but it is worth highlighting the point.
Implementing software solutions for critical functions like Corrective Actions (CAPA), Deviations, and Change Control Management establishes a crucial foundation. These digital systems streamline and standardize processes, making data collection more comprehensive and consistent.
The data amassed from these processes, combined with information from instruments and manufacturing activities, provides a rich dataset that AI functions can analyze and act upon. This foundational step is essential for any organization looking to leverage AI effectively.
At Scigeniq, we envision the practical application of AI in three stages, each offering increasing levels of insight and utility.
Stage 1: Conversational Information Retrieval
The first stage includes deploying chatbot capabilities.
Most of us have experienced AI through chatbots, which allow us to ask questions and get answers in a conversational way based on the information available on the Web. Pharma companies can leverage this model by allowing them to ask questions about their own regulatory and quality documents.
With this capability, a team member, perhaps new to the company, would be able to ask the AI tool a question such as “What have we said previously in our regulatory filings about XYZ?” The tool would answer questions conversationally, explaining what has previously been said about that topic in the company’s stored documents. The prerequisite, of course, is having a library of documents available from which to build a response.
At Scigeniq, we are already working on incorporating these capabilities into our quality and regulatory solutions.
Stage 2: Identifying Patterns and Correlations
The next stage focuses on analyzing data to identify patterns and correlations. For example, AI can uncover that most deviations occur during specific time periods or are associated with particular processes.
This initial layer of insight enables investigators to narrow their focus when identifying causes and devising corrective actions. At Scigeniq, we are already implementing these capabilities into our products, allowing our clients to gain actionable insights from their data.
We can then move beyond identifying correlations to uncover actual causes. As we accumulate more data from various sources over extended periods, AI’s analytical capabilities will enable us to make more definitive statements about the underlying causes of deviations or issues.
This stage requires a robust dataset and sophisticated algorithms capable of distinguishing between mere correlation and actual causation. By understanding the root causes of problems, pharmaceutical companies can implement more effective corrective and preventive measures, improving overall quality and compliance.
Stage 3: Predictive Analytics
The third stage, which Scigeniq plans to implement in the next one to two years, involves predictive analytics. This advanced level of AI application requires a rich dataset and real-time digital feeds from instruments and processes. The goal is to predict potential issues before they occur, allowing for proactive interventions.
For instance, AI could forecast a likely deviation based on current trends and historical data, enabling preemptive actions to mitigate risks. This capability represents a significant leap forward in quality management, offering the potential to prevent issues rather than merely reacting to them.
Leveraging AI in Pharma Quality Systems
The potential impact of AI on quality systems in pharmaceutical companies is dramatic and evolving rapidly, but we have a clear vision of how to leverage AI in quality systems in the near term. From identifying patterns to uncovering root causes and ultimately predicting future issues, current AI capabilities can drive significant improvements in quality and compliance.
At Scigeniq, we are well down the path to integrating these advanced AI capabilities into our products. As we continue to innovate and develop our SaaS product suite, our plans and product roadmap will no doubt evolve because AI capabilities are evolving as fast as we can integrate them.
We know that the key to success for us—and our customers—is to stay focused on the real value of these capabilities and avoid succumbing to the hype of the newest cool technology. If you’re interested in seeing how we are implementing AI today, request a demo, and we’ll show you.