Healthcare and artificial intelligence in digital transformation

Digital transformation processes continually contribute to every aspect of our lives, especially in the field of health and social care. Health services are integrated services. For this reason, the quality, cost and accessibility of the services offered in this sector are critical issues that require crucial development today. Technology and its improvements positively affect quality of life. In terms of health, new technologies and methods improve treatment, communication with patients, health protection and healthcare administration. Digital technologies can exist in various fields, such as portability, wearability, machine–to–machine communication, cloud computing and the internet of things (IoT). Today, the utilisation of these technologies in healthcare services ensures the digitalisation of processes.

As you read on, we will briefly discuss the importance of transitioning to new methods for diagnosis, treatment, rehabilitation and protection from diseases. We’ll look at how these can be achieved by healthcare professionals using techniques such as machine and deep learning (both are subsets of artificial intelligence). We’ll also look at how these methods can help healthcare organisations to increase convenience and efficiency.

Impact of artificial intelligence technology in health services

Artificial intelligence is a system designed to solve complex operations by adapting human thinking and consciousness structure to the machine, similar to the human brain structure. Artificial intelligence and its sub–branches are used in central and local health institutions with clinical processes, management processes and health documentation. Natural language processing (NLP) recognises the voices of doctors and nurses and transcribes their instructions into text. Capacity and medication management in hospitals are also an open area for improvement. By using machine learning methods, serious cost savings can be achieved by performing operations such as capacity prediction, determination of drug doses and optimisation of bed capacity.

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Artificial intelligence based solutions are encountered in mobile health with solutions such as voice response systems and in diagnoses.

IoT based monitoring solutions are gaining popularity in the area of chronic disease management. In the near future, such systems will store the data collected from patients in the cloud computing environment and use the captured information for analysis. Monitoring the regularity of indicators such as pulse and blood pressure, especially in heart patients, will enable early detection of heart attacks. The ability of artificial intelligence to learn and produce beneficial results is directly proportional to the quantity and quality of the data used.

The data collected with solutions such as the IoT, which we mentioned above, can be used to detect people’s health risks in advance, which shows us that artificial intelligence is well–positioned to revolutionise the health sector.

Given the impact of AI and machine learning on our wider world, it is crucial that AI is part of the curriculum for a range of domain experts. This is especially true for the medical profession, where the cost of a wrong decision can be fatal. Many nuances can be found in how an AI system is built. Understanding this process – and the choices involved – is vital for the appropriate use of automated systems. The data used to teach systems and the optimisation strategy deployed both have a profound impact on the ability of AI systems to solve a specific problem.

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AI has the potential to solve many of healthcare's biggest problems. Unfortunately, there are many different barriers. One of the biggest barriers is data. We can invent promising technologies and machine learning algorithms, but we need sufficient and well–represented data to realise the full potential of AI. The healthcare industry needs to digitise medical records, agree on standardisation of data infrastructure, protect privacy and handle consent of data from patients. Without these fundamental changes, it won’t be easy to realise AI’s purpose in helping human health.