The combination of economic crises, a growing shortage of public resources, socio-economic disparities, and demographic shifts has significantly amplified the strain on healthcare systems globally. Rising healthcare demand and the goal of universal coverage highlight the need for improved resource allocation, infrastructure, and workforce planning. AI offers significant opportunities for enhancing efficiency and service quality, yet its integration is limited by regulatory, organizational, ethical, and environmental challenges. Although the literature broadly emphasizes AI’s potential, empirical evidence on its actual impact on healthcare performance remains scarce.
Based on these premises, this study proposes a comprehensive set of key performance indicators (KPIs) to assess AI’s tangible effects. The proposed set of KPIs provides a practical and adaptable toolbox for healthcare decision-makers to evaluate AI-driven changes and monitor their implementation.
This paper employs a Causal Loop Diagram (CLD) approach to explore how AI influences healthcare systems, focusing on two key areas: (i) optimizing resource allocation to improve efficiency and (ii) enhanced personalized care to elevate service quality. CLDs provide a structured and dynamic framework for understanding the complex interdependencies shaping healthcare organizations. The CLD approach serves as a foundation for developing KPIs to guide managers in evaluating AI integration. By combining systems thinking with the development of KPIs, this research provides a novel methodological framework for assessing the tangible impact of AI technologies in healthcare organizations. The resulting set of KPIs measures and evaluates the effect of AI integration within healthcare organizations. It is intended to guide future research regarding AI applications to healthcare management, as well as help managers assess the impact of AI implementation in healthcare settings. This research addresses the lack of empirical validation regarding the impact of AI in healthcare organizations by providing a set of indicators for evidence-based decision-making. Thus, it contributes to both academic research and practical applications of AI in healthcare management.