A MULTIDIMENSIONAL FRAMEWORK FOR EVALUATING ARTIFICIAL INTELLIGENCE IN HEALTHCARE GOVERNANCE

Abstract

Artificial intelligence (AI) is increasingly used to support healthcare governance by improving planning, resource allocation, policy development, and organizational decision-making. However, existing approaches to AI evaluation remain largely focused on technical performance and clinical effectiveness, providing limited guidance for assessing AI systems used in healthcare management and public administration. This study aims to develop a multidimensional framework for evaluating the effectiveness of AI in healthcare governance. The study is based on a narrative review and synthesis of contemporary scientific literature, international guidelines, health technology assessment frameworks, and AI governance recommendations. The proposed framework integrates six complementary evaluation dimensions: technical, clinical, organizational, economic, governance, and ethical. For each dimension, key evaluation objectives and recommended indicators are identified based on evidence from international methodological frameworks and recent research. The framework is intended to support healthcare managers, policymakers, researchers, and healthcare organizations in conducting comprehensive assessments of AI systems throughout their lifecycle. Unlike existing approaches that typically evaluate individual aspects of AI implementation, the proposed framework combines multiple evaluation perspectives into a unified model specifically tailored to healthcare governance. The findings contribute to the development of standardized approaches for evaluating AI-enabled decision support systems and may facilitate more evidence-informed adoption of AI technologies in healthcare management.