Digitization is happening faster than ever. The term New Work is used everywhere. The same applies to the life science industry. Workplaces and working conditions are being redesigned and rethought. According to a recent Deloitte study with more than 12,000 surveyed experts in over 90 countries, one-third of all executives say that digital transformation has made a significant push.
This push is visible in many processes and workflows. In the life science industry, digitization is also influencing the distribution of roles between the pharmaceutical industry and medical technology. The influence extends to interaction with the end customer. Experts see challenges in this, but also opportunities for the future.
Opportunities of digital transformation
With the digital transformation, new innovations are constantly appearing. Old technologies are improved or new ones are added. Terms such as machine or deep learning are appearing more and more frequently. Artificial intelligence is no longer just a science fiction idea. The McKinsey Global Institute examined logistical processes in the life science sector and came to the conclusion that 80 percent alone can be automated with the help of machine learning. But what does that mean?
Machine and Deep Learning
Machine Learning (ML) is a subfield of artificial intelligence(AI). Algorithms recognize patterns and regularities in data sets and develop solutions from them. Deep Learning (DL) is a method of information processing and a subarea of Machine Learning.
The decisive difference lies in whether or how humans intervene in the learning process: In machine learning, humans intervene in the analysis of the data and the actual decision-making process. In contrast, deep learning models are able to learn on their own. This happens by the systems repeatedly linking what they have learned with new content. In this way, they learn again. Humans do not intervene in this learning process; the analysis is left to the machine.
The potential of Machine and Deep Learning
Machine and Deep Learning bring enormous application possibilities for the life sciences. They hold the potential to revolutionize the way businesses operate. But not only that. Machine and Deep Learning can be used to manufacture, distribute or research products. Ergo, it can be used to create entirely new products that could not be manufactured in the past.
This is just a small part of what artificial intelligence (AI) can achieve in life science. It gets particularly interesting when AI helps analyze huge data sets from clinical trials, health records and genetic profiles. The first use of AI technologies in healthcare showed a cost reduction of an incredible 50 percent. At the same time, AI improved patient outcomes by more than 50 percent.
The Challenge
But in order to benefit from digital transformation, companies need to get to grips with the issue. This includes familiarizing themselves with cloud work, dealing with data storage, and acquiring knowledge about IT security. This in turn costs resources. Companies should consult appropriate experts and integrate them into the company on a project basis. The reason for this is the resulting consequences. According to forecasts, the healthcare system will use technology so extensively in clinical medicine that life science companies will have to ensure that aspects such as patient safety, data protection, and privacy are guaranteed.
Conclusion
Digital transformation is a challenge that all industries must master. Companies can meet this challenge on three levels: normative, strategic, and operational. Experts assume that only then companies can transform themselves holistically. At the same time, it is possible for companies to exploit the potential of digital technologies more easily for themselves and to stay one step ahead of the rapid pace of digitization so that they have an important competitive advantage over other companies.
