The transition from a delivery model based on volume to a model based on value is underpinning an unprecedented rate of change in healthcare. Our research identifies academic medical centers that have successfully moved their organizations from a volume-based care delivery model to a model that is based on value, achieving top decile performance in the transition. We asked senior leaders about their key imperatives in achieving system-wide healthcare transformations. Major teaching hospitals were sampled using data envelopment analysis to identify hospitals that produced the best value-based purchasing process of care, Hospital Consumer Assessment of Healthcare Providers and Systems scores, and mortality scores with the most efficient utilization of human and financial organizational resources. On-site interviews were conducted with the CEOs of the top-performing hospitals. Content analysis of the transcribed interviews revealed 10 clear imperatives that our sample of CEOs said were essential.

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 most efficiently. Interview transcripts were analyzed using a mix of traditional text mining in LSA and Gemini 2.5. The capability of four freely available AI platforms—Gemini 2.5, Scholar AI 5.1, Copilot’s Chat, and Claude’s Sonnet 4.5—was also reviewed. Results: LLM AI analysis identified ten primary factors, comprising twenty-four subtopics, that characterized successful hospital performance. Notably, AI analysis identified a theoretical connection that manual analysis had missed, revealing how the identified framework aligned with Donabedian’s seminal structure, process, outcomes quality model. The AI analysis reduced the required time from weeks to nearly instantaneous. Conclusions: LLM AI tools offer a transformative approach to unlocking insight from the analysis of qualitative textual data in healthcare settings. These tools can provide rapid insight that is accessible to personnel with minimal text-mining expertise and offer a practical solution for healthcare organizations to unlock insight hidden in the vast amounts of textual data they hold.