Dec 05, 2022

What do you mean by Digital Transformation in Life Sciences?

  • By EVA JOHNSON,
  • 210 Views
Digital Transformation in Life Sciences

Life sciences are only now beginning to embrace digital transformation as an industry actively altering itself in the quest to reestablish long-term sustainability. They are pursuing a more holistic strategy that embraces a broader organizational approach to change rather than the incremental focus on near-term technological innovation, operational efficiency, and the externalization of non core competencies prevalent in Digital Transformation in Life Sciences efforts today.

 

Digital transformation can fundamentally transform the organization, challenging the traditional leadership approach and incorporating technological advances (both near- and long-term), more effective information usage, evolving social norms, and an increasingly collaborative mindset on the next step in operational excellence. Keep reading the blog post to understand the impact of Digital Transformation in Life Sciences in detail!

 

Do You Know?

 

  • According to a Forbes report by Forbes 94 percent of life sciences, executives who are familiar with cognitive computing believe it will disrupt the business.
  • 96 percent of CEOs in the biological sciences are familiar with computational thinking and plan to invest in cognitive capacities.
  • 87 percent of life sciences executives familiar with cognitive computing feel it will play a vital role in their company’s future.

A Framework To Help You Navigate Digital Transformation In Life Sciences

 

Digital transformation provides opportunities for biopharma and MedTech companies to execute efficiently, engage effectively, and invent new goods and services. Here it works like this:

 

●    Execute Efficiently

Develop a digital culture that promotes new ways of thinking and capabilities by digitizing and rationalizing operations to create efficiencies and cost savings.

●    Engage Effectively

Drive a new enterprise model that creates and delivers targeted interactions that meet the demands of customers/patients/employees and foster reliable connections.

●    Innovate Efficiently

Using data and innovative platforms, catalyze the development of goods, services, and new business models to deliver value for customers.

Digital Transformation in Life Sciences

Source: Deloitte

Creating and implementing a digital strategy is a challenging endeavor. Furthermore, there is a propensity to manage digital initiatives in the form of projects or limit them to activities inside a specific division or function. Life sciences firms that want to avoid this trend must create a digital DNA that aligns their digital activities, people, culture, and structure with the organization’s overall goals. That goal necessitates a shift in how people view, think, and act throughout the company.

 

i). Digital Transformation and Covid -19

 

Globally, digital transformation is changing the DNA of the life sciences sector. While the COVID-19 pandemic caused significant disruption in healthcare and pharmaceutical, it also accelerated the pace of innovation and brought about a paradigm change in the life sciences ecosystem. Historically, industry leaders were hesitant to accept best practices from outside the sector; nevertheless, the pandemic’s urgency forced them to break through the barrier and achieve operational efficiency.

 

The life sciences industry has advanced the concept of digital transformation by rethinking its entire value chain, from simulations and modeling to ELN, regulatory compliance, EDC, medication safety, and clinical trials, to business applications (such as CRM and ERP) and general industrial processes. When innovation is the norm and digitization is critical to company continuity, life sciences executives have risen to the occasion and capitalized on the digital transformation wave to enable development.

 

ii). Digital Revolution Has Just Began

According to Statista spending on digital transformation (DX) is expected to reach 1.8 trillion US dollars by 2022. Global digital transformation spending is expected to exceed 2.8 trillion US dollars by 2025.


Leading life sciences organizations have recognized that the digital transformation in Life Sciences gives enormous opportunity to transition from being solely product makers to also being service providers and health care providers.

 

They are frantically hiring senior leaders from leading digital organizations or industries with high levels of digital adoption. They are also engaging with established and startup digital companies and see the value in participating in larger health ecosystems.

 

Life sciences businesses were in an excellent position at the onset of the digital revolution, bolstered by solid profit margins that appeared to be immune to the winds of change.

 

Things have changed since then. It is now time for them to acquire digital capabilities, make the necessary investments to harness the quantity of data, and link to the rapidly expanding health care digital ecosystems that promise to improve care quality drastically.

Digital Revolution

Source: Statista

 

Examples of Digital Transformation in Life Sciences

 

Creative examples that set a high bar for the use of AI, machine learning, and other digital technologies in research and development that helped in Digital Transformation in Life Sciences

 

i). Using artificial intelligence to accelerate drug discovery

 

The medication discovery process is time-consuming and expensive, with less than 12% of products approved for patient usage. Early adoption at the discovery stage has shown that, when used correctly, AI can assist improve product timelines and lower the financial capital required in the R&D phase.

 

Sumitomo Dainippon Pharma stated in early 2020 that DSP-1181, its therapeutic candidate for the treatment of obsessive-compulsive disorder, had entered Phase I testing.

 

It is recorded that the medication’s compound was identified within 12 months of its discovery launch by leveraging UK business Exscientia’s AI drug discovery platform, a significant improvement over the many years it can take using traditional R&D procedures.

 

This venture exemplifies how advanced AI technology combined with a profound understanding of a therapeutic area can provide spectacular results. Such collaborations between digital leaders and R&D behemoths will become more common as we delve deeper into the potential of AI and machine learning.

 

ii). Using machine learning to integrate and compile trial data

 

Traditionally, assembling trial data for reports or case studies required human data input from several sites. As a result, discrepancies and errors can occur, requiring documents to be reread or resubmitted, slowing trial progress, or delaying startup operations.

 

One of the most common applications of AI in a life science environment is data analysis and processing.

 

Pfizer organized a hackathon in 2019 to evaluate the machine learning technology of four possible partner firms, with the sole objective of identifying flaws and anomalies in datasets from 30 clinical studies.

 

Pfizer revealed the known faults in the data to the companies so that they could “teach their tools” the patterns in such errors, and the findings from each business were used as an indicator for pursuing a relationship.

 

Machine learning technology can use data from many sources to populate standardized forms, documents, and trial artefacts that aid in the fast startup of clinical trials or the advancement of ongoing research initiatives.

 

Machine Learning To Integrate and compile Trial Data

 

iii). Deep learning is being used to anticipate patient responses

 

There is a lot of ambiguity when it comes to how a product will work when prescribed to a real-world population. On the other hand, deep learning algorithms show promise for predicting how patients would respond based on existing data and known demographic characteristics.

 

In 2019, a study was undertaken to predict the clinical response to therapeutic drugs used in cancer therapies. Deep neural networks were trained to predict likely medication responses on numerous test groups of patient cohorts using a database of 1,001 cancer cell lines.

 

Machine and deep learning technology will likely be increasingly used to reduce ambiguity, establish early efficacy, and inform essential decisions in developing novel medications.

 

Anticipate Patient Responses

 

iv). Reimagining laboratory surroundings through the use of automation

 

Automation has a long history of decreasing human input in jobs, reducing the time required to finish tedious tasks and the potential for human error. Automation technology can help boost productivity, accuracy, and quality compliance in R&D settings.

 

Arctoris, an Oxford-based technology startup, uses automation and robotics to reduce the amount of manual labor required to execute laboratory research. Researchers can use their technology to access a platform to operate their laboratory studies remotely utilizing robotics, which means that during the pandemic’s peak, folks typically lab-based can still do investigations from home.

 

Experiments conducted in this manner also allow for a higher level of protocol adherence and consistency; a standard protocol may suggest something like mixing the sample,’ which would naturally result in somewhat different actions when performed by a human. In this capacity, using robotics can ensure that the same movement is made each time the protocol is followed.

 

v). Virtual reality treatment for patients

 

Telling individuals ten years ago that you could relieve their pain with a device comparable to a video game would have elicited blank eyes. However, in 2018, Virtual Reality (VR) is the crowning achievement of digital transformation in healthcare. Its numerous applications are radically altering the way patients are treated.

 

Virtual reality offers a safer and more efficient alternative to medicines. Virtual reality technology is being utilized to treat everything from anxiety to post-traumatic stress disorder and stroke.

 

VR is a strong communication medium that can help you better understand your clients’ demands and virtually engage them with your products or services.

 

Ending Up!

 

However, the Digital Transformation in Life Sciences is just getting started. Pharmaceutical and medical device firms have numerous potential in the still-developing digital health ecosystem.

 

Based on our work with dozens of life sciences companies and our recent research on digital strategies in this and ten other industries, we believe that the most successful life sciences firms will be those that collaborate effectively with their ecosystem partners to launch breakthrough therapies and devices, as well as digitally monitor and improve the health of patients who use them.

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