It’s no secret that healthcare has some major problems to solve. From widespread killers like heart disease and cancer, to lifestyle diseases associated with obesity, longer life expectancies and lifestyle concerns, the medical sciences are facing some significant challenges in the years ahead.
For example, in 2017 alone, Alzheimer’s and other dementias will cost families and taxpayers $259 billion in lost economic activity and healthcare costs, and these numbers could rise as high as $1.1 trillion by 2050.
What’s more, the National Alliance on Mental Illness has reported that an estimated 43.8 million U.S. adults has a mental illness, representing as much as 18% of the population.
And these concerns extend to the next generations as well. According to CDC estimates, about 1 in 68 children aged eight and under has been identified as having autism spectrum disorder.
There is a lot of work for healthcare researchers to do to address these and other concerns. But it isn’t going to be easy.
Part of the problem is that the scientific community is not set up to support large, multi-specialty projects. Modern scientific research involves massive data and computational complexity, and under the current system it is very difficult and costly for researchers to reproduce their peers’ innovations, let alone offer their own advancements. Researchers are siloed in specialties, in institutions, in countries, leaving those working on complementary projects isolated from each other and the scientific community as a whole. This is holding back progress on a long list of needed technologies.
How can we accelerate science to enable researchers to solve the largest healthcare challenges in the world?
Solving today’s biggest medical problems will require more computational horsepower and more organized infrastructures that are better suited for collaboration than the siloed systems that researchers are using today. A cloud-scale research platform is needed to foster collaboration, speed up discovery, maximize funding potential, ensure reproducible results and unlock the potential of disruptive innovations.
A startup out of Minneapolis called Flywheel is working to make this a reality, creating a connected, cloud-based research community where scientists can collaborate and share reproducible science. The idea is to create a cloud-based, SaaS-enabled workspace where researchers all over the world, from many different institutions and specialties, can work together to bring new treatments to market faster.
This includes data management tools to allow researchers to capture, organize and search huge volumes of data from virtually any source; cloud-scale computing that enables them to tap powerful hardware that can dramatically speed up their analyses; as well as shared data algorithms that facilitate collaboration and data reproducibility.
It’s about advancing science and medicine through seamless collaboration and reproducibility, and about making research deeper, more cost efficient and expediting time to market for the biggest medical innovations.
More than 70% of scientific researchers say they have tried and failed to reproduce another scientist’s experiments during their career, and more than half have failed to reproduce their own experiments. This is frustrating for researchers, who need to be able to reproduce results in order to push new innovations forward, but it can also be costly. Even a delay of one year in bringing a new drug to market can costs researchers more than $1 billion in lost opportunity.
But that’s just part of the plan to accelerate the speed of innovation in the sciences.
Flywheel is also building an archive, a repository of scientific developments, methods, tools and more so that future researchers will be able to build on those discoveries and push the envelope even further, without having to go back and redo the work that’s being done now.
Think about it like this. As a SaaS platform, Flywheel’s reach is effectively limitless. As its user base grows, it will begin to build an unparalleled and valuable data asset and algorithm warehouse that’s unlike anything else in the world today. This will consist of community-generated content, created by researchers, that will on the flipside be available to anyone who wants to use it in their own, later, research projects. In the future, Flywheel will have opportunities to act as a channel for algorithms that require a license when used commercially, effectively offering an “App Store” for scientific algorithms. Flywheel will take a percentage of the associated license/subscription fees for this data.
When SaaS platforms scale, the goal is always to do it exponentially, as there is effectively no extra cost to the provider to add new users to their rolls. What Flywheel is doing with its collaboration platform is bringing this attitude to academia and the pharma/biotech industries.
What’s more, this technology will accelerate advancements and innovations in a wide range of fields, including biotech, pharmaceuticals, health care, agriculture and more. It effectively greases the wheels for science to be done quickly and accurately, at scale.