Journal of Thai-Chinese Social Science (JTCSS)

Practical Reflections on AI-Powered KPI Performance Improvement from the Perspective of Hospital Managers

KeMing Yu — 2026-02-06

In the context of high-quality development in public hospitals, Key Performance Indicators (KPI) have become an important management tool for hospital managers to enhance medical quality, operational efficiency, and service levels. However, traditional hospital KPI management exhibits significant shortcomings in data integration, dynamic monitoring, and decision support, struggling to meet the demands of refined management. This study aims to systematically analyze the mechanism through which Artificial Intelligence (AI) enhances hospital KPI performance from the perspective of hospital managers, construct an analytical framework for AI-empowered hospital KPI management, and explore its implementation pathways in management practice. Employing a normative research method, this paper summarizes the limitations of traditional hospital KPI management and synthesizes the empowering role of AI on KPI performance across aspects such as medical quality control, operational efficiency improvement, cost management, and patient experience optimization. The results indicate that by enhancing the real-time and prospective nature of data analysis, AI contributes to transforming hospital KPI management from outcome-oriented to process-oriented, and from experience-driven to data-driven. The study posits that systematically integrating AI into the hospital performance management system helps increase the scientific rigor and sustainability of managerial decision-making, providing crucial support for the high-quality development of hospitals. This research offers theoretical reference and managerial insights for hospital managers advancing the practice of AI-empowered performance management.

The Role of Artificial Intelligence in Alleviating Hospital Queuing Problems and Its Specific Manifestations

Yidong Chen — 2026-02-06

With the continuous growth in healthcare service demand, hospital queuing problems have become increasingly prominent and have emerged as a critical factor affecting both the operational efficiency of healthcare systems and patients’ care experiences. Traditional approaches that primarily rely on increasing human resources often fail to fundamentally alleviate queuing phenomena in complex healthcare systems. From the perspectives of service operations and patient flow management, this study explores the role of artificial intelligence (AI) in mitigating hospital queuing problems and examines its specific manifestations. Using a scenario analysis approach, this study takes the outpatient process of a general hospital as a representative research context and analyzes AI interventions from three dimensions: bottleneck identification, decision front-loading, and dynamic capacity scheduling. The findings indicate that by integrating multi-source healthcare data and conducting predictive analytics, AI can systematically alleviate queuing problems and reduce patient waiting times without relying solely on workforce expansion. The results provide managerial insights into the application of AI in hospital queuing management.

A Comparative Study of Hospital KPI Formulation Models Empowered by Artificial Intelligence

Huatian Lin — 2026-02-06

Hospital Key Performance Indicators (KPIs) are important management tools that connect strategic objectives with operational execution. Traditional hospital KPI formulation mainly relies on managerial experience and historical statistical data, which in practice suffers from problems such as static indicator structures, strong subjectivity, and slow response speed. With the widespread application of artificial intelligence technologies in healthcare management, intelligent KPI formulation methods based on big data analytics and machine learning models have gradually become a new development direction. This study takes traditional manual KPI formulation and AI-assisted KPI formulation as research objects. Through literature analysis and case comparison, the differences between the two models are systematically compared from the perspectives of formulation logic, data sources, dynamic adjustment capability, and management effectiveness. The results indicate that AI-driven KPI formulation models demonstrate significant advantages in accuracy, timeliness, and predictive capability, while still facing certain challenges in data governance and organizational adaptation.

From Experience to Intention: How Brand Experience Drives New Energy Vehicles (NEVs) Purchase Intention via Perceived Value and Brand Identification

Yilin Wang — 2026-02-06

This study examines how brand experience influences consumers’ purchase intention toward new energy vehicles (NEVs) in the context of Xiaomi Auto, and tests the mediating roles of perceived value and brand identification. Survey data were collected from 396 adult potential NEV consumers in Chengdu, China using a combined online–offline approach, and analyzed via confirmatory factor analysis and structural equation modeling with bootstrapping. Results indicate that brand experience significantly increases perceived value (β = 0.660, p < .001) and brand identification (β = 0.589, p < .001). Both perceived value (β = 0.223, p = .002) and brand identification (β = 0.195, p = .003) positively predict purchase intention, while brand experience also retains a significant direct effect on purchase intention (β = 0.358, p < .001). Bootstrapping confirms two significant indirect effects via perceived value (β = 0.147, 95% CI [0.056, 0.259]) and brand identification (β = 0.115, 95% CI [0.043, 0.203]), indicating partial mediation. The findings highlight that experience-centered branding shapes NEV purchase intention through both value-based evaluation and identity-based connection, offering actionable guidance for ecosystem brands designing multi-touchpoint experiences to convert consumer attention into purchase intention.