Disruptive innovation has made its way through most industries as a driving force for advancement and change.
But, the scientific research sector, whether public or private, has remained largely untouched by the benefits of disruptive innovation. The result? Two of life science’s biggest hurdles – astoundingly high lack of experimental reproducibility, and the fact that it continues to take 10+ years and over $2 billion to develop a life saving drug in the year 2016 – are still ongoing challenges.
These statistics persist because the scientific process requires rigor and validation, making scientists reluctant to modify their work in any way that might compromise the results. Given what is at stake – cures for diseases, new therapies, and more — this hesitancy is completely understandable. It is not an aversion to new technologies or the idea of innovation. After all, scientific research is all about innovation and breakthroughs, supported by reproducibility that enables the proliferation and deployment of those breakthrough solutions.
The truth that every researcher knows is that there is no single variable that contributes to irreproducibility across all scientific disciplines. The key is to measure everything, including ambient temperature, light, humidity, oxygen and CO2 – not just the variables that have historically been tracked – in order to identify sources of variability and determine whether they are relevant or not. With that critical insight, researchers can begin to understand how those factors affect reproducibility, and understand why expected and actual results differ. But, measuring everything has historically been beyond the scope of even the most committed researcher. And the process was time-intensive and incredibly disruptive, usually occurring only when something went inexplicably wrong.
At Elemental Machines, we are focused on disrupting experimental research – in a good way. Our goal is to enhance the research process without disrupting the scientist’s work. We are doing this by applying technology that has worked so well in other sectors to the scientific process. Intelligent, Wi-Fi-enabled sensors passively collect real-time data without disturbing the scientific workflow in any way. Cloud computing, real-time processing, and advanced analytics identify meaningful variability and offer new insights into the scientific process.
By innovating in the research process, we are not changing human behavior in the lab. Instead, we are augmenting it with technology-based tools that seamlessly give scientists new levels of insight and confidence. The result? Improved efficiency and reproducibility, enabling researchers to reduce the cost and compress the timelines of their work. Simply put, we accelerate science through disruptive innovation in the lab, not by disrupting the scientist.