Enabling genomic sequencing in UK national healthcare provision
By 2030, the UK plans to have created the most advanced genomic healthcare system in the world, underpinned by the latest scientific advances, to deliver better health outcomes at lower cost. Part of this ambitious vision is creating the first national healthcare system in the world to offer whole genomic sequencing (WGS) as part of routine care.
Genomic sequencing is a scientific method for determining the entire genetic makeup of a specific organism or cell type. It can be used to understand how diseases such as cancer are formed and how to increase the efficacy of diagnostics and treatment. Genomics research, diagnostics, and testing creates vast volumes of invaluable data, which must be securely transferred and stored for analysis by multiple parties.
Doing this at scale is a monumental feat, so technology is key to creating a national healthcare system that uses WGS in routine care. Whether it’s leveraging the public cloud to support huge workloads at scale and low-cost, using AI and machine learning for rapid computational and predictive analytics, or powerful data fabrics to enable portability of data.
Investment in public cloud services
First, let’s deal with the need for investment in public cloud services, which has been a priority within public sector since 2013 with the introduction of Cloud-First. According to our own research of public section decision makers, 87% say their organisation will increase or maintain its cloud spend in the next 12 months.
The public cloud is a vital piece of the genomic sequencing equation. It provides much needed scalability when it comes to dealing with huge workloads of data as well as a cost-effective mechanism of hosting the precious data produced by the process.
Working in tandem with public cloud services, AI and machine learning will be the key to accelerating the time to insights of genomic sequencing, making breakthrough research and cutting-edge diagnostics faster.
Deploying AI
Across the public sector leaders have plans to deploy AI within their day-to-day operations – with 57% looking to do so in the next two years. In healthcare specifically, investment in AI is on the rise, but challenges around obtaining the right skills and supporting technologies to support massive undertakings such as genomic sequencing remain.
One such challenge identified by over two thirds of UK public sector leaders is that their data infrastructure is currently not fit-for-purpose to deploy AI effectively, responsibly, and ethically. To take advantage of not only AI, but also applications such as genomic sequencing that are supported by AI, organisations need the ability to store, manage, and analyse data across different teams and locations.
Allowing data to flow
To solve this problem, organisations are investing in powerful data fabrics that allow data to flow seamlessly and securely across multiple clouds, private storage, and Software as a Service (SaaS) applications. This is critical with genomic sequencing where data must be shared with specialist experts for analysis to diagnose conditions and contribute to medical research.
Furthermore, consider that we are dealing with highly personal and sensitive medical information – so while data sharing must be enabled, it must be secure and not undermine public trust in the process.
Across the public sector and technology industry, work is underway to make genomic sequencing a more scalable and cost-effective discipline, so that the government’s vision of incorporating it into the provision of routine national healthcare can be achieved.
Check out webinar on data’s role in driving the UK public sector forward:
Authors
Jack Watts
Jack Watts, EMEA Leader, Artificial Intelligence for NetApp, has been in the IT Industry for over 10 years with expertise in delivering x86 server solutions, is also considered a subject matter expert in Artificial Intelligence along with managing worldwide supplier & customer relationships and program delivery. Jack joined NetApp mid 2019 to increase the awareness of delivering a robust and optimised AI Platform to drive efficiencies in the Data Science Workflow and enable faster time to value. The importance of a competent Data Pipeline between Edge point devices, through to core on premise environments and hybrid multi cloud is absolute in necessity to deliver AI platforms to market.
