Comment: How digitisation and AI can help create a more-efficient and responsive healthcare system

Barley Laing, UK managing director at Melissa, reveals how digitisation and AI can save the health service money, drive efficiency, and improve patient outcomes

AI and other transformational technologies are particularly suited to the healthcare industry

The digitisation of patient records is the first step to a more-efficient health service – one the Government recognises with its ambition to make the service paper free at point of care by 2020.

Yet recent research reveals paper records are still predominantly used, with just 12% of trusts on track to meet this target.

The real value comes in using semantic technologies and informatics to unlock hidden insight from the patient and wider health-related data from the digitisation process

Unfortunately, this pinch point with patient information has a negative impact on efficiency in the health service.

It’s digitisation that will relieve the administration bottleneck, enabling precious budgets to be spent elsewhere and allowing health professionals to access more-accurate records efficiently and reliably.

While digitisation begins with the scanning of patient records – a task that can be undertaken quickly and cheaply – it is only the first small step on the important journey to delivering insight on the digitised data.

This insight can help improve patient health, save lives, and inform the targeting of health spend, ultimately driving efficiency.

AI delivers efficiency

Barley Laing

Informatics and semantic technology, a form of artificial intelligence (AI), are particularly suitable for the health sector.

By applying some of these AI principles, organisations can start to realise benefits from their digitisation efforts, and enable the entire health service – trusts, physicians and their patients – to derive value from the process as well.

The AI source should not only effectively read handwriting and understand character sets and the barcoding system in the scanned patient records; it should go much further.

Informatics and semantic technology, a form of artificial intelligence (AI), are particularly suitable for the health sector

This is why informatics that combines AI, computer, and cognitive science are starting to play an important role in delivering a new level of intelligence; and the reason semantic technology, which often works successfully with informatics, is set to have the greatest impact in the industry.

Semantic technology

Also called ‘semtech’ or the semantic web; semantic technology is an extension of the current web that has been defined over the past decade by the World Wide Web Consortium (W3C) in collaboration with Stanford University, MIT and others.

Semantic technology stands out in its design which enables universal data interoperability; while, HTML, whose specifications are also managed by W3C, is purely designed to allow universal access to documents on the web.

Along with global data interoperability, semantic technology delivers a form of AI that associates words with meanings and recognises relationships between them.

This enables health practitioners to deliver additional indepth understanding of their patients by making connections from data in their records in real time.

This helps in quickly identifying possible patients risks in areas like heart disease and cancer, through to organ failure; enabling intervention with preventative measures at an early stage in order to improve patient outcomes and save lives.

It’s digitisation that will relieve the administration bottleneck, enabling precious budgets to be spent elsewhere and allowing health professionals to access more-accurate records efficiently and reliably

Also, the ability to integrate data from virtually any source, allows semantic technology to spot trends, such as an increase in the types of cancer in a certain geographic area and by demographics, with the potential to predict its possible future growth.

This pattern recognition delivered by semantic technology makes preventative intervention possible through the targeted deployment of health resources.

Accurate data

Data quality can typically be poor in the health sector because of its complexity. Unfortunately, incorrect data will mean the insight provided via AI will not be as good as it should be.

To deliver accurate insight using AI, the health service needs to invest in preventative data cleansing to ensure data quality. It must be the first move before taking the plunge with AI.

Health professionals require access to an easy-to-use interface that enables them to visually explore the data and carry out data analytics, making the best use of the opportunity semantic technologies and informatics provide.

It must be one that doesn’t require knowledge of coding and empowers organisations to uncover insight securely.

Also, any service sourced should have built-in knowledge of drugs, genes and diseases; one that recognises and can validate drug names, variants, dosages and spellings to prevent any confusion and reduce the likelihood of errors in what is a complex sector.

While it’s time to digitise in healthcare, health professionals must recognise that this is only the first step.

To deliver accurate insight using AI, the health service needs to invest in preventative data cleansing to ensure data quality

The real value comes in using semantic technologies and informatics to unlock hidden insight from the patient and wider health-related data from the digitisation process.

Only then is it possible to drive efficiency in improving patient health, saving lives, and informing the targeting of budgets – saving health professionals large amounts of time and public money in the process.

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