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Healthcare companies have been storing data for numerous reasons. Many pharmaceutical companies are acknowledging the fact that big data analytics can be an asset in reducing costs in healthcare research, development, and manufacturing.
Healthcare companies are experiencing rapid growth in the use of Artificial Intelligence (AI) tools as the market for AI in drug development has ballooned up to $ 700 million. The biggest reason for the upsurge in demand for intelligent tolls is the efficient utilization of data. Healthcare manufacturing companies have started integrating the Internet of Things (IoT) data from the factory floor to process development and optimization. However, the data from the smart devices can also assist in the entire process from research to manufacturing to sales and beyond.
AI and big data can also improve the efficiency and accuracy of drug trials and patient monitoring systems with a better determination of patient outcomes. Many AI tools and machine learning algorithms can provide better insights into the manufacturing processes to enhance decision making in the pharmaceutical industry.
The step-by-step approach of batch processing has become outdated, and healthcare companies have started implementing continuous manufacturing to monitor factors like temperature and moisture change. Continuous manufacturing processes help in the collection of real-time feedback, which reduces the variability in the final product and patient outcomes through Process Analytical technology (PAT). PAT is an efficient and cost-effective method especially in the synthesis of biological proteins, which can cost millions of dollars per batch. The application of AI tools for performance and PAT for feedback response can help to improve manufacturing processes immensely through automated machine learning processes.
Big data analytics and AI can also improve the research and development processes by reducing time and cost. These technologies can be ideal in designing better clinical trials and predict outcomes with the analysis of previous data. AI and big data analytics tools can also provide statistical conclusions about a particular drug through various algorithms and processes.