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Artificial intelligence (AI) applications are increasingly used for day-to-day operations in healthcare. Each has a relatively limited scope or task, and several find application in managerial and organizational processes. More and more, AI and machine learning (ML) devices have received US FDA approval in the last decade. This chapter covers the main AI applications in healthcare, with a focus on organizational AI solutions (administrative AI), the main AI developers, their investment and real-world data and case studies in healthcare and other sectors. AI can be applied in resource management and procurement, resource allocation, clinical case management, staff work shift scheduling and handling of emergencies. AI applications are becoming ubiquitous in hospital (e.g. emergency room and operating theatre) and outpatient settings (e.g. ambulatory care and dentistry clinics). Their implementation is expected to bring direct benefits for patient care and satisfaction. This chapter gives a broad definition of AI in healthcare settings, with a focus on administrative applications and their use in case study data.

Common challenges for healthcare systems worldwide are population ageing, rising therapy spending and reduced economic resources. In response, AI can play a crucial role in facilitating managerial and economic objectives within a holistic vision of care and improve the experience of patients and professionals. AI may change the delivery of services and the demand for them as well. This raises questions of how to balance the supply and demand sides of healthcare services, how to leverage competitive positioning and how to differentiate strategies specific to the public and to the private sector.

Implementing artificial intelligence (AI) in healthcare organizations involves the entire organization. This groundbreaking technology is becoming central to achieve the goals of the new healthcare through the ongoing commitment to sustainability despite the severe lack of resources. Decision-makers in healthcare need knowledge and skills to prepare for the changes in many professional activities in the years ahead. Furthermore, chief medical officers and clinical leaders need to act on the opportunities that AI can bring, starting from its integration into the reality of healthcare settings while working with those responsible for managing and implementing AI in compliance with current legislation in Europe and the United States. Finally, stakeholders need to know how to leverage AI capabilities and how to recognize its limitations and its opportunities in administrative applications (admin AI) to optimize day-to-day operations and clinical applications (non-admin AI). In this view, clinical leaders and health care decision-makers may appreciate AI as a new way to provide sustainable social and healthcare services.

The complex challenges facing the healthcare sector call for a revision of the ways it can provide high-quality services with economic sustainability. Revision can proceed along different pathways. Among the new paradigms of healthcare is the shift from a silo approach by hospitals towards an integrated, multidisciplinary approach. This entails restructuring hospitals in disease centres and exploring how AI can aid in the integration of hospital services and community care. Reorganization is vital to the development of patient-centred healthcare and the holistic approach. To achieve these goals, healthcare and policy decision-makers need to consider both the administrative and the clinical aspects of everyday issues. AI can play a key role in helping balance this duality. The overarching objective is to create interdisciplinary therapeutic and diagnostic pathways within care networks shared between the hospital and the community. This involves the analysis of huge amounts of data and interdisciplinary knowledge beyond the grasp of an individual. Therefore, knowing how AI can help in the development and reorganization of community healthcare is essential for clinical leaders to take advantage of this enormous opportunity in larger settings.

Several primary challenges can be identified in the strategic application of AI for organizational optimization within the operational framework of diverse healthcare settings. To develop future clinical leaders, essential skills and competencies need to be cultivated within teams through training and support. The overarching aims are to foster skills for health management and leadership, as well as promote organizational behaviour for change and health system facilitators (i.e. payment systems and health information systems) to incentivize adoption. This chapter provides readers with a comprehensive roadmap for the implementation of AI in healthcare settings.

This paper aims to conduct a tertiary review to analyse the state of the art of literature reviews on knowledge management (KM) published in academic journals and provide an overview of their evolution. From 2000 to 2022, about 500 reviews have been published in the KM field, with most systematic studies compared to bibliometric or meta-analytic studies, and an absence of previous tertiary studies. Therefore, given the lack of previous tertiary research, this paper provides a complete picture of the evolution of review topics in the past and presents implications for both researchers and practitioners.

A classification scheme was defined to cluster and evaluate the literature reviews, both in terms of methodological approach and content. Regarding the content, the various secondary papers were classified according to the purpose of the research (state of the art, taxonomy, research agenda and research framework), the unit of analysis (small and medium enterprise, large company, start-up and university), the KM models adopted and the thematic areas addressed. Furthermore, a tertiary review methodology was identified integrating two main approaches: a bibliometric approach for cluster identification and a systematic approach for the discussion.

Two categories of contributions emerge from the results: those concerning research topics that have found a continuous interest over time and those that have not yet found a constant research interest. This latter aspect is relevant to help researchers conduct future literature analysis in KM research to bridge existing research gaps.

This paper provides a unique compendium of search directions to offer a comprehensive overview of the scientific debate about KM. This overview can also be used as a managerial panacea to identify best KM practice guidelines from existing reviews.

This is a unique attempt to conduct a tertiary study on KM for more than two decades by providing insights into the structural body of knowledge through academic progress in the subject of KM. Thus, this study expands the field of KM and provides original approaches for research in the field.