Enhancing U.S. Healthcare Cybersecurity Through Intelligent Agent–Supported Qualitative Information Systems Audits

U.S. healthcare institutions are grappling with an unprecedented surge in cybersecurity incidents that threaten patient safety, data confidentiality, and operational continuity. Traditional metrics driven audits anchored in technical checklists and quantitative controls frequently overlook the human centric and organizational vulnerabilities that underpin many breaches. This paper delivers a comprehensive systematic review of 23 peer reviewed research papers, industry reports, and regulatory guidelines, augmented by a thematic analysis of survey data from 25 U.S. health systems surveyed anonymously. We identify a clear paradigm shift from purely quantitative assessments toward qualitative audit methodologies that probe governance structures, stakeholder behaviors, and cultural dynamics. Building on these insights, we introduce an intelligent agent enhanced qualitative Information Systems audit framework. The model integrates semi structured interviews, policy and artifact reviews, direct observation, and AI driven meta classification to translate nuanced findings into structured risk profiles. Audit teams now blend on site ethnographic techniques with remote AI assisted analytics, enabling real time detection of emerging threats. Our analysis uncovers five core themes: policy practice alignment, behavior-based risk indicators, continuous control monitoring, cross functional governance, and adaptive remediation loops, which traditional tools fail to capture. Case studies demonstrate that institutions applying this hybrid framework achieved a 46 percent reduction in unauthorized access events, faster incident response cycles, and a stronger regulatory compliance posture. We conclude that a flexible, culturally attuned, and technology augmented audit strategy is essential for resilient healthcare cybersecurity. Future research should explore longitudinal impacts of AI enabled qualitative audits and the scalability of this approach across diverse clinical settings.