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The utilization of artificial intelligence (AI) in the solution of many problems encountered in healthcare in recent years is rapidly becoming widespread. Understanding of the use and importance of efficiency, security and accessible healthcare to everyone and providing value-based services for healthcare decision-makers is essential. The special uses of machine learning, natural language processing and smart voice assistants, which have developed as sub-branches of AI, for healthcare services, the contributions of these techniques to the digital transformation of healthcare services and how all these will help decision-making processes in healthcare services, will be discussed in this chapter. And also, FDA-approved algorithms that are a kind of AI tool will be explained.

The rapid increase in analytics is playing an essential role in enlarging various practices related to the health sector. Big Data Analytics (BDA) provides multiple tools to store, maintain, and analyze large sets of data provided by different systems of health. It is essential to manage and analyze these data to get meaningful information. Pharmaceutical companies are accumulating their data in the medical databases, whereas the payers are digitalizing the records of patients. Biomedical research generates a significant amount of data. There has been a continuous improvement in the health sector for past decades. They have become more advanced by recording the patient’s data on the Internet of Things devices, Electronic Health Records efficiently. BD is undoubtedly going to enhance the productivity and performance of organizations in various fields. Still, there are several challenges associated with BD, such as storing, capturing, and analyzing data, and their subsequent application to a practical health sector.

This chapter presents a framework for an action research based intervention to develop and transform sustainable healthcare in a regional context. The framework is illustrated by the case of the Regional Cancer Centre (RCC) West in western Sweden.

The framework draws upon and develops Pettigrew’s context–content–process model of strategic change and applies it to the unfolding narrative of the change effort. The empirical focus is the activities of a learning platform consisting of the RCC leadership, senior cancer physicians designated as process owners and an action research team. Data were collected from documents, observations of the learning platform, notes from meetings and interviews. Outcome data were obtained via the self-reporting of the physicians.

The learning platform established the capability for wide ranging development and quality improvement on the 23 cancer pathways as well as some support activities around principles of patient-centred care. A clear result is greater inter-organisational collaboration between care professionals as well as the introduction of new medicines, clinical methods, joint learning activities and new forms of measurement and monitoring of care practices. All of the improved measures are sustained.

Whilst there is no shortage of rhetoric on patient-centred care, the reality is that in complex healthcare systems solutions such as process-oriented approaches often fail. This case presents a model and an approach that eschews clear visions for change and instead places an emphasis on dialogue, participation, professional autonomy and collaborative communities as means for achieving the patient-centred ideal. The case also shows the value of seeing sustainable health systems as being grounded on practitioner–scholar collaboration that combines practical knowing with scientific knowledge.