Accenture’s recent report titled, Digital Transformation in the Lab: Bridging Analog Islands in a Digital Ocean, examined the current state of transformation within life sciences labs. Researchers spoke with 128 leaders in the life sciences industry, with 69% of respondents at Director-level or higher and 55% executive or R&D management, to get a closer look at the maturity of their digital strategies, the barriers to adoption of digital and pathways for implementation.
As a professional with over 25 years working with pharma companies and most recently with Federal agencies organizations, I wanted to view the findings through the lens of my government work with Accenture’s Scientific Informatics Services group. Many of the insights in the report are aligned with governmental perspectives, however there are differences in what is both driving and slowing transformation between the private sector and the governmental setting.
Transformation in the lab
Within both governmental and non-governmental settings, there is a disparity between what life looks like outside the lab and inside. Largely speaking, most scientists live in a world that is digitally connected – one that is smart and highly intuitive when it comes to facilitating everything from online purchases to health monitoring or digital messaging. Yet inside so many research labs, it is different. Inside our Federal government, there is awe-inspiring science being conducted, yet much of it is conducted in unconnected and siloed systems, often using analog paper processes or computer systems using command line interfaces. Imagine being on the cutting edge of science and needing to analyze research results stored on external hard drives using spreadsheets or transforming complex datasets using a Linux shell script.
Whether in labs engaged in research, or in labs generating and analyzing clinical data, the volume, velocity, and variety of data being generated by scientists is overwhelming the systems required to support it. Up to 70% of experimentation is not reproducible, often due to the inability to find the original research data, or because the experimental conditions (metadata) are inconsistently or inadequately documented.
of experimentation is not reproducible
Processes that have been practiced for decades are rapidly becoming redundant, yet many remain in place. In fact, of the 128 industry leaders surveyed, 40% had not embarked on applying digital to their research and quality labs, and 37% more seemed to be stuck in pilot mode. It’s like our scientists are stepping back in time every time they get to work. All this while more and more data continue to be collected.
of industry leaders surveyed had not embarked on applying digital to their research and quality labs
of industry leaders surveyed seemed to be stuck in pilot mode
In the governmental context, this slow transformation is amplified by factors that don’t affect non-governmental companies as heavily. Unlike biopharmaceutical enterprises that are driven by markets and returns on investment to adopt digital, the motivations for transformation in a governmental setting are functionally based and not predicated on potential job or revenue loss. Many times, research funded by the federal government is conducted by small research groups that do not have the organizational incentives to share data effectively and increase research efficiency. For governmental organizations, transformation is often more dependent on the desire of those working in the sector to want the change and the status quo often wins in these cases.
All scientists should be excited about the potential of digital transformation. Across science-driven organizations, the convergence of science and technology has helped create more novel results, faster than ever before. By embracing the potential of New Science—therapies and medicines that combine the best in science and digital health technology—innovations can offer better patient outcomes, improve patient engagement, and generate new growth to combat the disruption that is impacting many of today’s companies. Accenture research predicts New Science therapies will represent 54% of sales between 2017 and 2022, up from 47% between 2012 and 2017.
From a governmental perspective, it could be argued that transformation is requisite, as the benefit to the public will be large for all the same reasons as above. With this in mind, one of the key pathways to change, in my view, is adopting the FAIR principles. The acronym stands for Findable, Accessible, Interoperable and Reproducible, and is widely accepted to be something we should all be striving to achieve, whether in a governmental or non-governmental context.
Metadata and data should be easy to find for both humans and computers.
Once the user finds the required data, she/he needs to know how it can be accessed and applied with appropriate security and authorization controls.
The data needs to be integrated with other existing data and operate with applications or workflows for analysis, storage, and processing.
Metadata and data should be well-described so that they can be replicated and/or combined in different settings.
FAIR was created to outline standard practices for data collection, storage and use. This kind of standardization requires buy-in from the organizations at play. As the motivations are somewhat different for government, this will take strong leadership and clearly mapped out processes for transformation, to the benefit of all.
Accenture Scientific Informatics Services works with many large-scale and crucial health and medical organizations that do everything from health testing of passengers and livestock entering the US to testing contraband and workplace safety. The ability to converge all the data gathered from these tests with other medical research and lab data will deliver insights that right now are unimaginable.
Couple this with the non-health research and testing that our Federal government conducts to find renewable energy sources and to ensure the safety and security of our citizens, we can see the value of unleashing the scientific data.
But how do we transform?
Accenture’s Digital Transformation in the Lab report outlined three key stages for transformation, that I believe, combined with adoption of FAIR standards, will help us unlock the value of data and drive innovation and insight.
Phase #1 - Foundational:
Do the basics brilliantly. Priorities here include simplifying the application landscape, deploying automation, connecting all instruments, and capturing data digitally.
Phase #2 - Transformational:
The modern digital lab is moving away from isolated and monolithic systems toward a cloud-based, multi-vendor platform approach that delivers “connected capabilities” formerly provided by one or more of these traditional systems.
Phase #3 – Aspirational:
Allowing for new scientific models to be realized by leveraging a redefined lab environment powered by an end-to-end data supply chain, deeper adoption of AI, whole lab automation, and implementation of in silico methods and simulations.
Through a thoughtful approach that incorporates the FAIR standards and is driven by leadership at the top level, both governmental and non-governmental organizations can truly step into the Digital Lab of the Future – a brave new world of data-driven innovation and insights that will open doors to discovery.