Search results

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.

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.

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.

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.

Our study investigates the role of the Human Resources Management (HRM) system strength in supporting Italian healthcare managers during times of uncertainty and change. The perceived HRM system strength and its relationship with managers’ taking charge behaviors, perceived procedural constraints, and work engagement were examined.

Two surveys were conducted to gather empirical data from a pooled sample of 121 healthcare managers located in hospitals across Italy. We use regression analysis to test our hypotheses.

The data show that strong HRM systems facilitate managers taking charge behaviors and work engagement. Additionally, the findings highlight the mitigating effect of a strong HRM system on procedural constraints, such as red tape, in public healthcare organizations.

Emphasizing the positive outcomes associated with strong HRM systems, the findings suggest that public health organizations should make efforts to put in place robust HR practices to bolster engagement and proactive behaviors among healthcare managers in times of uncertainty and change.

Analyzing a unique data set, the study extends the understanding of HRM system strength in the public sector, specifically in post-pandemic healthcare organizations. Overall, the study contributes to the growing literature on HRM system strength by offering novel insights into its nomological network.