Harnessing Digital Transformation to Drive Innovation in Personalized Medicine

The technologies that underpin health care have been evolving rapidly in recent years, helping to drive digital transformation in the life sciences. In 2019, approximately 86% of life sciences companies reported experimenting with new technologies, including artificial intelligence (AI), to drive innovation and better patient outcomes, and 84% of health care organizations had adopted at least one cloud-based digital health solution using clinical data.^1,2

In 2020, the COVID-19 pandemic brought to light the importance and acceleration of digitization. In fact, according to a McKinsey survey of global executives, the pandemic has accelerated the digitization of customer and supply chain interactions and of internal operations by three to four years.³ And according to Accenture, approximately 73% of organizations across industries reported piloting or adopting AI in one or more business units in mid-2020.⁴

Especially in precision medicine, or personalized medicine – environments where harnessing new technologies can help quickly and affordably create and deliver complex, patient-specific pharmaceutical or medical devices – embracing digitization is “the key to enable and operationalize both standardization and personalization in health care,” says The Journal of Precision Medicine.⁵

4 Practical Ways to Apply Digitization

As companies in precision medicine look to embrace a modern approach to technology, there are some practical applications to consider in their pursuit of such efforts:

• Small Automation. Take a “small automation” approach to digital transformation, starting small and smart. Companies can do this by quickly implementing adaptable technologies that fill existing gaps left by their current systems.⁶ Rather than initiate a massive, organization-wide digital transformation effort, focus on piloting small, smart technological changes that make an impact in specific areas of improvement. This approach is easier to implement, more flexible, and less expensive than large-scale IT initiatives.
• Interconnectivity. Digitally connecting systems, people, and data across critical areas – including R&D, manufacturing, supplier management, and patient care – enables organizations to drive smarter, more agile operations. For instance, for precision medicine manufacturers producing a unique product as a batch of one, a cloud-based platform can deliver applications that connect enterprise quality, shop floor production, and supplier networks relevant to the product, its batch, and its end user.
• AI/Machine Learning. AI and machine learning can help process massive amounts of data to quickly identify new compounds, treatments, and clinical trial participants; predict a new therapy’s efficacy and safety; and reduce the reoccurrence of quality errors and the time it takes to complete an investigation. AI has become “the most powerful and pervasive emerging technology across healthcare and life sciences,” according to Gartner, and precision medicine is considered a very promising area for machine learning applications.⁷
• Cloud Technologies. The computing power, storage capacity, and networking speed of the cloud are needed to support AI and machine learning. The cloud provides a scalable environment that simplifies data management and access. Integrated cloud technologies can help organizations harmonize different platforms, break down silos, and streamline the capture, storage, and access of digital health care data. Cloud computing can expedite experimentation, reduce drug discovery costs, and drive better forms of clinical care.

As medical technology and pharmaceutical organizations have re-evaluated and adjusted their processes to help address the global health crisis — and to prepare for the new normal — the future of personalized medicine will likely see organizations accelerating digital transformation efforts to enable innovative, mass-customized products and more precise treatments in health care.

Resources

1. “Post-Digital Technologies for Next-Generation Care,” Geoff Schmidt, Gro Blindheim, Jonathan Burr, Theo Forbath, Andy Greenberg and Sowmya Srinivasan, Accenture, September 2019.
2. “Untangling Clinical Data Integration,” Andrea Brückner, Martin F. Brunner, Karl Kugler, Kyle Janak and Josef El-Rayes, Accenture, July 2019.
3. “How COVID-19 has pushed companies over the technology tipping point—and transformed business forever,” Laura LaBerge, Clayton O’Toole, Jeremy Schneider and Kate Smaje, McKinsey & Company, October 2020.
4. “COVID-19: Post-Coronavirus Technology Trends,” Accenture, June 2020.
5. “Personalization and Standardization: Can We Have it All?” Luis Lasalvia and Reto Merges, The Journal of Precision Medicine, June 2020.
6. “How to Embark on a Digital Transformation,” Sara Bresee, Manufacturing Business Technology, July 2020.
7. “Predicts 2020: Life Science CIOs Must Digitalize for Business Growth,” Andrew Stevens, Jeff Smith, Michael Shanler and Animesh Gandhi, Gartner Research, December 2019.