How Pharma Companies Can Use Artificial Intelligence (ai) And Data To Their Advantage?

By Author: management consuting
Total Articles: 2
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Life sciences firms and pharmaceutical firms understand the potential transformative capabilities of artificial intelligence (AI), and generative AI in particular. Generative AI can be used for better design of clinical trials and enhancing the accuracy and efficiency of their drug discovery. However, the complicated nature of data continues to trouble many pharma companies. So, the pharma companies need to shift focus from managing data to strategically using it for the growth of their business. Companies can combine untapped unstructured data with the old structured data, and through generative AI can generate new insights and enhance the data findability. But, to scale AI applications beyond the simplest productivity applications, it will need a dedicated data strategy.

Harnessing clinical data beyond regulatory submissions
The ability to use clinical trial data to do more than regulatory submission is one of the most important ways that pharma companies can use AI and data to their advantage. Clinical data have generally been closely associated with compliance and approval processes, and this has limited ...
... their potential further. Nevertheless, using generative AI for marketing and solutions, businesses can open up new horizons, such as optimization of future trials, finding new therapeutic uses, and better data design.

An emphasis on enabling clinical data to be FAIR (findable, accessible, interoperable, and reusable) has a strong potential value. These data principles can help companies overcome data silos and make data more accessible between teams. Generative AI can help optimize the data structure through automatic tagging and description of the data, which is easier to locate and reuse for other research and development purposes. Moreover, AI can combine multimodal information, such as the tumour biopsy findings, CT, and electronic health records (EHRs), to give further information on the progression and efficacy of the disease treatment.

Triangulating commercial data for healthcare ecosystem engagement
The pharma companies can use the power of AI and commercial data to advance their strategies and customer interaction in the changing healthcare ecosystem. The commercial data activity is generally geared towards data gathering in structured form such as demographics or simple customer measurements. Although such information is important, it does not always provide an in-depth understanding of what customers need and want. However, generative AI can be used to extract more insights, as it can analyze unstructured sources of data and allow pharma companies to understand their customers fully.
AI can play a pivotal role in triangulating data for pharmaceutical companies. They can gather health details and treatment history directly from patients, link and integrate data from various first, second and third-party sources and enrich the data by identifying patient risks or understanding the role of healthcare providers in a patient's treatment journey. This deeper understanding of customers enables companies to create context-driven, individualized healthcare digital solution and strategies that consider not just transactional needs but also the broader challenges and preferences of the customer.

Optimizing manufacturing process
AI and data can be used by pharmaceutical companies to make their manufacturing processes more predictive rather than reactive. Currently, companies identify most manufacturing issues only after they occur, resulting in costly breakdowns and delays. With the help of AI and data, businesses can anticipate and prevent issues even before they happen to save their time and maximize their production.

Digital twins, which are virtual copies of manufacturing processes, are one of the means of achieving it. These models replicate the real-world situations, which enable the companies to manage, evaluate, control and streamline their activities. The precision of such imitations can be increased by Gen AI, which will examine data between the devices, RFID tags and sensors in real time within manufacturing plants and warehouses. AI will be able to track how raw materials will be used, how equipment is functioning and the adherence to the working process to maintain the quality, efficiency and compliance.

Generative AI has the potential to transform the pharmaceutical industry by unlocking the full potential of data. Pharma companies must stop treating data just as a regulatory requirement or support tool. Instead, they should build a strong data strategy, integrating structured and unstructured data. By effectively using AI in clinical trials, commercial strategies, and manufacturing, pharma companies can improve efficiency, accelerate drug development, and optimize operational processes.