Enhancing healthcare resilience through AI-driven performance management systems.

Area of Science:

• Health Informatics
• Organizational Theory
• Artificial Intelligence

Background:

• Healthcare organizations face systemic challenges including environmental, demographic, and financial pressures.
• Innovative performance management is crucial for detecting dysfunctions and enabling strategy reversals.
• Developing organizational resilience is essential for navigating complex healthcare landscapes.

Purpose of the Study:

• To explore the application of artificial intelligence (AI) in enhancing the resilience of healthcare organizations.
• To introduce a framework for integrating AI into performance management for improved organizational adaptability.
• To provide a structured approach for proactive decision-making and strategy adjustments in healthcare.

Main Methods:

• The study employed the Design Science Research Methodology (DSRM).
• Developed the Resilient AI Performance Optimization Framework (RAIPOF).
• Validated the framework through an illustrative application.

Main Results:

• The RAIPOF integrates AI-driven tools into organizational processes for healthcare resilience.
• AI-enhanced smart key performance indicators (KPIs) – descriptive, predictive, and prescriptive – are embedded into performance systems.
• The framework aligns strategic planning with real-time operational data and resilience indicators.

Conclusions:

• RAIPOF provides healthcare managers with a structured approach for anticipatory decision-making and resource allocation.
• It facilitates continuous improvement in volatile and high-risk healthcare settings.
• The study positions AI as a strategic enabler of adaptive governance, advancing organizational resilience literature.