We all understand that entering the health system as a patient can be stressful. In the last 18 months, no matter what the condition, whether awaiting diagnosis, treatment, or a scheduled check-up the impact is acutely understood. Digital healthcare is at a pivotal point from the perspective of technological innovations facilitating improvements in disease treatment, improve individual wellbeing and personalised care. 

A new age of predictive care beckons in digital healthcare 

Over the coming years, the evolution of healthcare will centre around the reengineering of clinical care and operations around digital health and pervasive, real-time use of data and advanced analytics to achieve these goals. Healthcare systems worldwide will be expected to deliver diagnostics and care that is both predictive and proactive. As connected care and bioinformatics commentators including the World Economic Forum forecast, these innovations will be enabled by artificial intelligence (AI), machine learning (ML) and data-driven analytics.

In the very near future, the application of advanced analytics, including AI and ML, will greatly improve clinical decision-making and patient care outcomes. The analysis of patient health records alongside vast datasets that cover populations, conditions, countries, environmental factors, virology data and more will be leveraged to help manage myriad health conditions.

IT company Gartner stated: “Demands for care collaboration and coordination across the ecosystem are increasing the demand for real time data, insight and workflow optimisation and orchestration. This is resulting in foundational technologies, such as the real-time health systems (Hype Cycle for RTHS Technologies). The analyst predicts that in the coming years, healthcare will be characterised by a reengineering of clinical care and operations around digital health and pervasive, real-time use of data to achieve goals.”

Understandably, healthcare providers and medical teams alike are excited about the potential for AI-powered diagnostics and precision medicine. Primarily, this is because of what this means for improvements in patient care - especially when so many countries expect the continuation of care to be extended to meet the needs of a larger population of senior citizens in years to come. 

Pascal Holt, Director of Marketing at Iceotope

Data-driven patient healthcare

Clinical informatics, for example, uses data and a range of tools to support health professionals. These include data analysis, preventing hospital patients having accidents on wards, running systems for storing and sharing X-rays, as well as ultrasound and magnetic resonance imaging (MRI) scans. Within a few years, AI will be used to access data sources and reveal patterns in disease, aiding treatment and patient care programmes. 

AI-driven data analytics and resource-intensive task automation will enable public and private healthcare providers to increase the productivity and efficiency of care delivery, at the same time enhancing resource use, reducing waiting times and tackling employee burnout.

Transformational technologies such as Digital Health Platforms (DHP), will enable healthcare providers to quickly respond to external uncertainty as well as planned change. They can do this using cloud-first healthcare applications and tools that bring together Electronic Health Records (EHR), data connectivity and powerful analytics. In so doing, they can address strategic issues for providers, where monolithic EHR-centric application architecture fails to meet changing patient and clinical workforce demands. It is believed that DHP will reduce EHR total cost of ownership, release data for deeper insight and deliver improved clinical and cost outcomes.

There is no doubt that to facilitate change, strategic partnerships – ecosystems – must develop between healthcare providers, technology companies, data centre service providers and associated organisations, to drive this digital transformation. Many in healthcare already see the positive results of investment in AI as a powerful enabler of operational efficiency, which leads to better diagnosis, treatment, and outcomes. 

In addition to solving the challenges of integrating and provisioning healthcare systems, whilst offering a potentially faster route to shorter queues for treatment and less pressure on healthcare resources and personnel, ecosystems could also offer solution to clinical HR shortages. With Health Education England forecasting that they need to fill a skills gap of a staggering 672% to meet the anticipated requirement for a “digital workforce” in the coming decade, technology professionals in the IT channel could be of strategic importance in providing critical support.

Healthcare needs data, data needs infrastructure

Whether they’re excited or not by the prospect of better patient outcomes and relief from some of the pressures upon medical staff, it’s likely that very few in the profession have given much thought as to what technology-led developments could mean for the underlying critical IT infrastructure required to support this revolution.

Simply put, data demands will rely on higher density processor chips, especially high-density GPUs to provide the real-time grunt to ensure the swift delivery of processes like data capture, analysis and interpretation. The majority of PACS (Picture Archiving and Communications System) Administrators and IT departments have probably never seen density requirements like those demanded by today’s power-hungry chips, let alone have the capability to accommodate these requirements within their current IT and data centre infrastructure.

IT transformation, mobile devices, and the Internet of Things (IoT) are creating enormous volumes of data globally. IDC predicts that in 2025, 175 zettabytes (175 trillion gigabytes) of new data will be created around the world, while Gartner is forecasting that more than 75% of enterprise data will be generated and processed outside of the traditional data centre. The rising phenomenon of ‘data gravity’ is at the same time drawing the physical location of analytics, software applications and IT hardware towards the data source itself. This is creating a whole new set of challenges in healthcare, which must be overcome to support the patient anywhere from the doctor’s surgery to the emergency room, operating theatre and hospital ward right up to the bedside.

‘A year in the life of the NHS AI Lab’, 2020, illustrated that diagnostics had the most prevalent use of AI within the NHS. This marks the beginning of the use of deep learning (DL), ML and categorisation technology on enormous sets of medical images to create workflows and algorithms. While this will allow for faster and more accurate outputs at the point-of-care, it also means that an increasing amount of processing also needs to be done at the healthcare edge, giving rise to additional challenges such as available power, space, acoustics, as well as physical and data security. 

Digital healthcare at the server level and sustainability targets

Currently the healthcare sector faces its own sustainability challenges, producing the equivalent of 4.4% of global net emissions. At the same time, the data centre sector has recently come under the spotlight for its rising demands upon power grids all over the world. Put together, these add up to greater IT infrastructure challenges. Healthcare leaders are set to prioritise sustainable initiatives, with projected cost savings as an additional driver, which many believe go hand-in-hand with technology advancements.

Since the data centre industry provides services and infrastructure to support the digital transformation of almost every sector, it is also set to inherit a substantial proportion of their sustainability challenges. Data centres exist to process and store data as efficiently as possible to enhance the benefit for customers and owners. For the provider of data centre services, its highest operating expense is its electricity bill – the cost to power and cool IT equipment and its supporting environment. Depending on the source of its grid supply, it’s also a key source of emissions. 

In the coming years, the exponential upsurge in data processing necessary to extract patient insights from large datasets will continuously drive the requirement for higher power compute densities. CPU power consumption is on the rise, with Thermal Design Power (TDP) mapped to reach 400+ watts - resulting in hotter chips and higher rack densities. Increasing use of high-power GPUs alongside the CPU to accelerate computational workloads is also resulting in much higher power consumption and is driving the need for a fundamental review of thermal management in the data centre and at the edge.

Currently, the predominant way to remove heat from IT server equipment is by inefficient cool air drawn through the chassis, using numerous internal electrical fans to satisfy the higher density processors within the servers. Even the most efficient air-cooling systems cannot cope with the requirement of CPUs with mapped TDP of 400+ watts. Simply blowing more cool air at the problem is neither practical, efficient, nor sustainable. Efficiency of compute also requires the collaboration between servers, data centres, interconnectivity, and the customer to understand how best to move, process and store data. HPC and supercomputer level computations require specific layouts that increase the need for direct to component cooling.

The industry standard measurement of data centre efficiency - PUE - has plateaued over the past 5 years, and the use of air cooling has become a roadblock to the use of additional high density ITE and the need to drive more efficiency throughout the data centre industry. A step change in cooling technology is needed and today, liquid cooling alone is the only system that can take up the mantle to enable digital transformation inside and outside the data centre. In doing so, it can provision the transformation needed for high-quality, personalised healthcare delivery with efficiency, sustainability and cost-effectiveness built-in.