Elevating Patient Care: The Integration of Artificial Intelligence in Quality Assurance Practices

Abstract

Purpose: This article explores the integration of Artificial Intelligence (AI) in quality assurance practices within healthcare settings, aiming to significantly enhance patient care. By improving the accuracy and efficiency of quality assurance processes, AI can reduce human error and increase compliance with healthcare standards.

Methodology: The study employs a mixed-methods approach, combining quantitative data from healthcare facilities utilizing AI systems with qualitative interviews with healthcare professionals. Surveys assess the impact of AI on quality assurance metrics, while interviews provide insights into the perceptions and experiences of staff regarding AI implementation.

Findings: The results indicate that AI integration significantly enhances the speed and accuracy of quality assurance tasks, leading to a reduction in human error and an increase in compliance with healthcare standards. More importantly, healthcare professionals reported improved patient outcomes and greater workflow satisfaction, which are attributed to AI's capability to handle routine checks and data analysis.

Unique Contribution to Theory, Policy and Practice: This article contributes to the theoretical understanding of AI's role in healthcare quality assurance by establishing a framework that links AI utilization with improved patient care outcomes. It informs policymakers about the efficacy of AI technologies, advocating for policies that encourage their adoption in healthcare settings. Practitioners gain insights into best practices for implementing AI solutions that support quality assurance, ultimately fostering a safer and more efficient healthcare environment.