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What Does Digital Transformation Mean for the Future of Life Sciences?

26 April 2022

By Jay Freeman

What Does Digital Transformation Mean for the Future of Life Sciences?

From the pandemic to the acceleration of digital health, there are a number of factors that have considerably impacted the digital transformation of the life sciences industry.

For many life sciences businesses, this presents highly beneficial opportunities, so long as they are willing to keep up with the rapid transformation occurring to remain competitive.

Advanced technologies are changing processes, regulations, and the future of the industry at large, even though digital transformation is only just beginning. 

Which areas of digital transformation are having an impact on the industry, and what does it mean for the future of life sciences?

Changing business focus

The current digital landscape is dominated by several areas that cover digital and data for businesses globally.

Research conducted by Baker McKenzie found that the top three digital areas that respondents intend to pursue are:

  • (56%) Data analytics 

  • (55%) Cloud technology 

  • (55%) Monitoring 

The top three data areas that respondents intend to pursue are:

  • (53%) Data management

  • (44%) Data analytics and solutions

  • (32%) Data sharing

In Europe specifically, cloud technology and data analytics are key current digital growth strategies, with telehealth/remote monitoring, apps, and AI/automation predicted to be areas of focus in the next five to ten years as digitalisation catalyses more holistic approaches.

In the Americas, monitoring, apps, and business support platforms are the leading three areas of digital focus due to the rise of virtual patient management.

Data-sharing

Data is vital for the digital transformation of life sciences, as demonstrated during the pandemic.

Though cloud platforms, life sciences businesses have the opportunity to collaborate in a more efficient manner.

For example, in the biopharma sector, the exchange of data over cloud platforms could streamline the approval process for new drugs, from the application up to the approval process.

As data-sharing advances, the goal will be to drive more advanced analytics and insights by utilising newer technologies – AI and machine learning – to move away from descriptive data to predictive data, and eventually, prescriptive (what should be done) data.

This area of digital transformation will undoubtedly receive more attention as it comes to the forefront due to the issues that may arise around data privacy – compliance, privacy and security measures have to be adequately put in place for data-sharing to work effectively.

Additionally, complying with laws and regulations may be a significant hurdle for data-sharing, but it can be assumed that this will evolve alongside data-sharing processes.

Cloud processes

Cloud processes in life sciences have been utilised frequently for scalability, yet now organisations are leveraging cloud processes to assist with data-sharing, drive R&D, and catalyse automation, analytics, and machine learning.

According to McKinsey, in recent company reports and press releases, 16 of the top 20 pharmaceutical companies refer to cloud technology, including applications across research and early development, commercial, medical application, and the value chain.

The most significant benefits cited for the use of cloud technology are improvements in automation, data analytics, resilience, scalability, and cost optimisation.

Accessing data globally is another area that has been discussed frequently, particularly relating to the potential for earlier intervention and customised treatment for patients. 

60% of pharma executives have already made changes or have a plan in place to invest in cloud services to support their digital transformation efforts, with 42% of health services and pharma leaders sharing that improving the patient experience was their priority focus for investing in cloud technology.

Automation

A third of respondents in Deloitte’s Life Sciences Digital Innovation Survey reported using AI in day-to-day operations, and 31% have been part of a project that leveraged this technology.

Automating processes is a goal across the life sciences industry due to the repetitive nature of the work carried out across R&D, manufacturing, and supply chains.

The lengthy and high-cost nature of drug development processes are areas in which the use of AI can thrive, helping augment trial managers in handling global operations, risk predictions, and monitoring.

Many organisations will be most interested in the potential for automation to increase R&D productivity, lower overall costs of operations, improve quality of clinical trials, and improved patient experience.

In the future, much like other areas of digital transformation, AI and automation will be subject to increased discussions around regulations and compliance. 

Machine learning

Trial data has been an area that is labour-intensive due to the manual nature of inputting data sourced from multiple locations – thus increasing the potential for errors or inconsistencies – and this can delay trial progress.

Machine learning technology has the potential to identify errors or inconsistencies from multiple data sets, which can increase efficiency and accuracy in the long term.

The unique benefit of utilising machine learning technology is that they will continue to learn patterns in data and evolve with larger data sets.

Supply chain issues can also be assisted by the use of AI and machine learning, which can identify systemic issues in the manufacturing process and highlight production bottlenecks, or even predict completion times for corrective actions.

Given that globally, many companies experienced supply chain issues during the pandemic, technology that can help to streamline processes and help them to operate to the highest standard is a necessary solution.

In conclusion

Though many of the technologies accelerating the digital transformation of the life sciences industry are still in their early stages, the potential applications are significant and, in many cases, inevitable.

Many life sciences businesses are recognising the potential of certain technologies such as AI, cloud processes, and machine learning, each with their own contribution towards the digital innovation of the industry at large.

For many businesses, this technology is a competitive edge that will increase in effectiveness over time when adopted in the earlier stages – as the potential uses of these technologies are explored now, their use will only become more targeted and effective in the future.

As Deloitte says, companies should move from merely doing digital to being digital to amplify all areas of business.

To find out more about the digital trends impacting the life sciences industry, or for expert advice on securing top talent for your life sciences company, get in touch with the Panda team today. 


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