Integration in pharmaceuticals
AsscherAi helps pharmaceutical companies integrate AI systems with their batch record systems, reducing change control overhead and deviation investigation time, by providing a platform to query data in natural language and get answers in real time, thus streamlining the review process for quality assurance specialists.
A quality assurance specialist in a pharmaceutical company spends a significant amount of time reviewing batch records to ensure compliance with regulatory requirements. The introduction of AI systems integration in pharmaceuticals can potentially streamline this process, but it also poses unique challenges due to the strict documentation rules and validated processes that govern the industry.
Pharmaceuticals' specific challenges
The pharmaceutical industry is subject to stringent regulations, which can make integration with AI systems more complicated. Change control overhead, batch record review, and deviation investigation time are just a few examples of the challenges that can arise. For instance, when integrating with the batch record system, any changes to the AI system's configuration or algorithms must be thoroughly documented and approved, which can lead to significant delays. This change control overhead can be time-consuming and may require additional resources to ensure compliance.
Deviation investigation time is another area where pharmaceuticals' strict regulations can cause issues. If the AI system identifies a deviation in the batch record data, the investigation process must be thoroughly documented, and all relevant data must be retained for auditing purposes. This can be a labor-intensive process, requiring significant time and effort from quality assurance specialists.
Read paths, write paths and the need for care
When integrating AI systems with pharmaceutical systems, it is essential to consider both read paths and write paths. Read paths refer to the flow of data from the system of record to the AI system, while write paths refer to the flow of data from the AI system back to the system of record. While read paths are relatively straightforward, write paths require more care, as they can potentially introduce errors or inconsistencies into the system of record.
A shift supervisor checking the maintenance log, for example, may use the AI system to identify potential issues, but any changes to the log must be carefully validated to ensure accuracy and compliance. This requires a deep understanding of the system of record and the AI system's capabilities, as well as robust testing and validation procedures to ensure that the integration does not introduce errors.
Reconciling definitions that differ between systems
One of the significant challenges in integrating AI systems with pharmaceutical systems is reconciling definitions that differ between systems. For instance, the definition of a "batch" may vary between the AI system and the system of record, which can lead to inconsistencies and errors. To overcome this challenge, it is essential to carefully map the definitions and ensure that they are consistent across both systems.
This process can be time-consuming and requires a thorough understanding of both systems. However, it is crucial to ensure that the integration is accurate and reliable. By reconciling definitions and ensuring consistency, pharmaceutical companies can ensure that their AI systems provide accurate and reliable insights, which can inform decision-making and improve overall quality.
Latency budgets and precomputation
Latency budgets are another critical consideration when integrating AI systems with pharmaceutical systems. Latency refers to the delay between the time data is generated and the time it is available for analysis. In pharmaceuticals, latency can be a significant issue, as timely decision-making is critical to ensuring quality and compliance. To mitigate latency, pharmaceutical companies can use precomputation techniques, which involve computing results in advance and storing them for later use.
For example, a category manager comparing two suppliers may use precomputed results to inform their decision-making. By precomputing results, pharmaceutical companies can reduce latency and ensure that decision-makers have access to timely and accurate information. To learn more about how AsscherAi can help with latency budgets and precomputation, visit our website at www.asscher.ai.
Limitations and when to choose alternative approaches
While AI systems integration in pharmaceuticals can offer significant benefits, it is not always the best choice. In some cases, the complexity of the integration, combined with the strict regulations and validated processes, may make alternative approaches more suitable. For instance, if the integration requires significant changes to the system of record, it may be more practical to use alternative methods, such as manual data entry or custom scripting.
In other cases, the cost and resources required for integration may outweigh the benefits. If you are considering AI systems integration in pharmaceuticals and want to discuss your options, please contact us to learn more about how we can help. By carefully evaluating the challenges and limitations of AI systems integration, pharmaceutical companies can make informed decisions about the best approach for their specific needs.
Frequently asked questions
How can AI systems integration improve batch record review in pharmaceuticals?
AI systems integration can automate the review process, reducing time and effort required by quality assurance specialists to ensure compliance with regulatory requirements.
What are the challenges of integrating AI systems with pharmaceutical systems?
Challenges include change control overhead, deviation investigation time, and reconciling definitions that differ between systems, which can lead to inconsistencies and errors.
How can latency budgets and precomputation help in AI systems integration?
Latency budgets and precomputation can reduce latency, providing timely and accurate information to decision-makers, and enabling them to make informed decisions about quality and compliance.
When should pharmaceutical companies consider alternative approaches to AI systems integration?
Alternative approaches should be considered when the complexity of integration, combined with strict regulations and validated processes, outweighs the benefits, or when significant changes to the system of record are required.