Enhancing healthcare resilience through AI-driven performance management systems

Abstract

PURPOSE: In a world marked by systemic challenges, innovative performance management approaches are needed to enhance the detection of organizational dysfunctions and provide proactive support for undertaking timely strategy reversals. This paper explores how artificial intelligence (AI) can support the development of resilience in healthcare organizations facing increasing environmental, demographic and financial challenges. DESIGN/METHODOLOGY/APPROACH: The study adopts the Design Science Research Methodology (DSRM) to develop the Resilient AI Performance Optimization Framework (RAIPOF). An illustrative application supports the framework's validation. FINDINGS: The RAIPOF offers new insights into the use of AI in advancing healthcare resilience by providing a structured approach to integrating AI-driven tools with organizational processes. The framework embeds AI-enhanced smart key performance indicators (KPIs) - descriptive, predictive and prescriptive - into performance systems to align strategic planning with real-time operational data and resilience indicators. PRACTICAL IMPLICATIONS: RAIPOF equips healthcare managers with a structured approach for anticipatory decision-making, proactive resource allocation and continuous improvement, particularly in volatile and high-risk settings. ORIGINALITY/VALUE: This study advances the literature on organizational resilience by positioning AI not merely as a technological enhancement but as a strategic enabler of adaptive governance. By bridging the gap between operational metrics and strategic foresight, the framework challenges traditional static and siloed approaches to performance management and contributes to the interdisciplinary integration of AI, health informatics and organizational theory.

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