Strategic Resilience in Healthcare: Auditing AI-Supported Balanced Scorecard Models

Purpose. This study explores the effective implementation of the Balanced Scorecard (BSC) within a digital transformation context to strengthen hospital resilience in times of crisis. By integrating artificial intelligence (AI) and big data analytics into the BSC framework, the research highlights how real-time, data-driven decision-making enhances strategic agility and performance monitoring in healthcare institutions. The findings suggest that this digitalized approach enables a more adaptive and accountable response to healthcare emergencies, aligning operational processes with strategic objectives under conditions of uncertainty. Design/Methodology/Approach. This study employs a qualitative case study approach centred on the Centro Hospitalar de Vila Nova de Gaia/Espinho (CHVNGE), a major public healthcare institution in Portugal. Data were collected through three complementary methods: (1) document analysis of institutional digital transformation policies and BSC implementation strategies; (2) semi-structured interviews with key hospital administrators to explore perceptions regarding the impact of digitalization on strategic crisis response; and (3) comparative performance assessment using internal metrics to evaluate operational efficiency and responsiveness before and after the digital integration of the BSC. Findings. Preliminary findings indicate that BSC digitalization significantly enhances organizational resilience, with its impact categorized into three key areas: Predictive decision-making → Leveraging predictive analytics to optimize patient flow management and resource allocation. Increased operational flexibility → Enabling real-time adjustments in financial and healthcare strategies. Enhanced strategic integration → Aligning crisis response strategies with long-term institutional objectives. Originality/Value. While traditional Balanced Scorecard (BSC) models have been widely applied across various disciplines to support crisis management, this study introduces a digitalized BSC framework, integrating artificial intelligence (AI) and big data analytics. This approach shifts crisis management from a reactive to a predictive and proactive model, enhancing strategic preparedness and operational efficiency. This study provides valuable theoretical contributions, expanding the existing body of knowledge on digital transformation in hospital management. However, as the research is based on a single case study, further comparative analyses across different hospital contexts and healthcare systems are needed to assess the scalability and generalizability of the proposed model. Practical Implications. The findings offer a scalable framework for BSC digitalization in hospital management, equipping healthcare managers and policymakers with more effective crisis response strategies. By leveraging AI-driven decision-making and real-time data analytics, hospitals can enhance resource allocation, operational agility, and resilience during crises. Social Implications. The digitalization of the BSC serves as a comprehensive performance management framework that strengthens inter-organizational healthcare systems. By ensuring efficiency, service continuity, and coordinated crisis response, this approach significantly contributes to public health preparedness and resilience, particularly during large-scale emergencies.