Challenges to the digitalisation of healthcare

Challenges to the digitalisation of healthcare

Whilst digitalisation is seen as imperative for health systems worldwide, there are undoubtedly some challenges that slow and limit its implementation. Though such challenges are various and many, they may be attributed to one of the four following categories.


The introduction of individual digital processes such as these can in fact produce inefficiencies, which can limit the number of patients who can be seen or the time spent with patients, ultimately worsening care outcomes. Indeed, traditional hospital infrastructure was not designed to meet the demands of digital business.

The ability to provide innovative services and better patient outcomes is underpinned by a strong foundation: the digital infrastructure.

Many healthcare providers still use paper-based processes in the delivery of core services, wasting time and money on unnecessary administration.

Some providers have shifted to scanning paper, even employing digital signatures, but have not altered their business processes or reformed the underlying infrastructure – they have digitised, not digitalised.


There is still a popular, misguided perception among some healthcare professionals that digital infrastructure does not impact patient care.

As a result, digital infrastructure can receive low priority in procurement programmes, presenting a barrier to its adoption.

Research demonstrates a shift in this perception towards embracing digitalisation and going digitally-native is occurring. Often generation-based, this misconception is fortunately phasing out.


Many hospitals and healthcare institutions lack the network connectivity necessary to drive technological clinical innovation. A lack of sufficient network coverage is the single most prevalent roadblock to the deployment of interconnected medical devices and other smarts. Poor network infrastructure may also result in increased operational costs. Expansion of bandwidth does not help, as it drives up the long-term costs but does not solve the underlying issues.

To support new AI algorithms and IoMT devices, an adequate supply of underlying computing resources is required for the training of these complicated algorithms and to ensure permanent reliable data feeds to and from the IoMT devices. Due to the sheer size of training data as well as the nature of deep learning models, a powerful deep learning training rig can drive down the total cost of AI product ownership. 


The increasing availability of sensors and medical devices means that often healthcare providers install multiple sensors before figuring out how the data will be used.

Data minimalism is an approach to research and data collection whereby only the information that is needed is collected, and in turn only the necessary hardware is employed. Data minimalism brings direct profits in reduced costs of storing data. It is also a socially and environmentally responsible practice, as reduction in CPU costs results in reductions of unnecessary CO2 transmissions.

Data minimalism brings higher and better system quality, as it focuses only on the data that provides value. It contributes to stability, as the less data collected, the fewer associated risks.

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