AI-Enhanced Qualitative Analysis in Healthcare: Unlocking Insight from Interviews of Leadership at Top-Performing Academic Medical Centers.

ABSTRACT: Background/Objectives: Vast amounts of textual data are generated by healthcare organizations every year. Traditional content analysis is time-intensive, error-prone, and potentially biased. This study demonstrates how freely available large language model (LLM) artificial intelligence (AI) tools can efficiently and effectively analyze qualitative healthcare data and uncover insights missed by traditional manual analysis. Interview data from chief nursing officers (CNOs) at top-performing academic medical centers were analyzed to identify factors contributing to their operational and patient quality success. Methods: Semi-structured interviews were conducted with CNOs from top-performing academic medical centers that achieved top-decile quality measures while using resources

SUBMITTER: Ashton T 

PROVIDER: S-EPMC12841440 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature