Improving the Quality Management Process of Specialists' Consultations in the Healthcare Sector

Abstract

The relevance and challenges of ensuring effective quality management in specialist-to-specialist business medical consultations are particularly evident in the healthcare sector. There is currently a significant gap in the standardized decision-making logic, structured communication processes, and comprehensive digital support tools. This study aims to provide recommendations for improving the quality management process of specialist-to-specialist business consultations by integrating principles 
of digital management and artificial intelligence (AI). To achieve this aim, the study sets out three key objectives: to analyze scientific literature regarding the principles of quality management and the potential applications of AI in the healthcare sector; to examine the existing structure of business consultation processes in an organization, highlighting critical aspects of quality management through expert evaluation, and assessing the opportunities and impact of integrating AI tools; and to propose an updated model for managing the quality of business consultations based on the study's findings. The study employs methods such as scientific literature analysis, expert evaluations, and in-depth interviews. The findings led to the formulation of an updated consultation process model integrating AI tools into four primary areas: complexity classification, treatment plan formation, corrections analysis, and feedback processing. One significant change proposed is eliminating the two-tier decisionmaking approach, where a consultant independently assesses their competence. Instead, an automated system employing an AI classifier is introduced, assigning consultation cases based on complexity level and consultant availability. This approach not only enhances the consistency of decision-making but also improves the efficiency of consultations and the management of corrections throughout the entire 
process.