The Emergence of Data in Life Sciences Processes

Creating lasting solutions is integral to many areas of life sciences, and data can inform these decisions and educate life sciences businesses on risks, too.

The evolution of cloud computing, alongside the increased use of AI and machine learning, means that analysis in life sciences has adapted considerably.

For example, insights into optimal procedures can be given by cloud-driven AI that can contribute towards lower costs, streamlined care, and more efficient business operations in the long term.

Additionally, the increased use of cloud computing assists in business efficiency by offering life sciences researchers a scalable, adaptable method for managing workload and resources as and when they are needed.

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The pharmaceutical drug discovery process can entail large amounts of vital data and evolving the development process relies on big data and AI.

Time and resources are two areas in which clinical trials have traditionally performed rather poorly due to their labour-intensive nature.

Evidence-based clinical research is also evolving as a result of statistical analysis, with DNA sequencing being a prime example of how analysing big data has progressed precision medicine.

As precision medicine advances continue, this will influence the way in which the drug development process occurs in the future, including the regulatory processes involved.

AI and machine learning can help to sift through this data more efficiently and provide the insights and patterns that can make use of the data most effectively.

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R&D processes can be notoriously time-consuming and often result in data not being used to its full potential.

For example, R&D professionals will traditionally have to examine a combination of patient data sets, clinical trial notes and documents that target key issues – these documents are complex, often have no clearly defined structure and can result in missing information or information overwhelm.

AI, analytics, and data science are tools that can be used to overcome the issues associated with R&D and help to identify patterns, increase clinical trial efficiency, optimise patient reporting and data collection, and enhance search and analysis efficiency in clinical trials.

Given that the R&D process is so long, AI is a valuable option due to its potential to reduce the costs and time associated with bringing new therapies to market and speed up regulatory processes.

The use of AI and data for improved R&D also links to personalised therapeutics/precision medicine as businesses – usually biopharmaceutical companies – can conduct more innovative research in relation to the effectiveness of specific medicines in relation to genetic variation.

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Predictive analytics tools and machine learning are part of the wider data solutions that improve the way in which patients are engaged across every stage of their treatment.

Personalised care for individuals requires data insights, which normally would have been reliant on making an additional (therefore unsustainable) workload.

In the UK, for example, the use of data-driven life sciences technologies is central to the Life Sciences Vision proposed by the UK government.

In this vision, the NHS has research embedded as a core element of effective patient care to deliver the goal of a digitally enabled and pro-innovation clinical research environment.

Data-driven life sciences companies can transform patient care and drug discovery across prevention, screening, diagnosis, treatment, response, and follow-up stages.

By using technology for the purposes of automation, real-time data can be used to provide better discovery insights and reduce the time it takes to get treatments to patients.

Precision Life, a platform taking a new approach to the analysis of complex disease biology through a range of data, uses population-scale data to understand what is driving disease within patient subgroups.

This information helps Precision Life to make better-informed decisions about effective interventions for individual patients, and this is all done by using insights generated on an individual level through data.

This is one example of many businesses harnessing the power of data for better patient outcomes – as data analytics and AI become more widespread in life sciences, more decisions will be made based on data.

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The emergence of data in the life sciences industry – catalysed by the pandemic – has the potential to truly change the way in which the industry operates and innovates.

Trends such as precision medicine have been increasing in prominence for some time and will continue to shape the industry as data improves and transforms the decision-making process.

The urgency necessary during the pandemic has only highlighted the need for more cost-and time-effective solutions, which data and AI can contribute to significantly, whether through the time to market for new therapies or automating processes to reduce the burden on healthcare professionals.

For such an innovative, fast-paced industry, data is less an intimidating or apprehensive part of the life sciences industry’s future, but instead, an opportunity to improve and adapt in ways that will meet the new technological demands of the world.

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To find out more about the future of the life sciences industry or for expert advice for all of your recruitment needs, get in touch with the Panda team today.