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

TL;DR

This paper shows how AI can quickly analyze healthcare interviews, uncovering insights faster and more effectively than traditional methods.

Contribution

The study demonstrates that freely available AI tools can identify theoretical connections missed by manual analysis in healthcare qualitative data.

Findings

LLM AI analysis identified ten primary factors and twenty-four subtopics from healthcare interviews.

AI uncovered a theoretical link to Donabedian’s quality model that manual analysis missed.

AI analysis reduced analysis time from weeks to nearly instantaneous.

Abstract

What are the main findings?
Freely available large language models (LLM) rapidly analyzed textual healthcare data yielding a 10-factor, 24-subtopic structure consistent with traditional manual LSA results.AI systems identified theoretical linkages with the Donabedian model of healthcare quality that traditional manual analysis had missed.

Freely available large language models (LLM) rapidly analyzed textual healthcare data yielding a 10-factor, 24-subtopic structure consistent with traditional manual LSA results.

AI systems identified theoretical linkages with the Donabedian model of healthcare quality that traditional manual analysis had missed.

What are the implications of the main findings?
LLM-based analysis can reduce the time, expertise, and labor required to extract meaningful insights from large qualitative datasets in healthcare settings and potentially uncover deeper insights than traditional methods.Freely available AI tools increase the accessibility of text-based analytics, enabling healthcare organizations to extract meaningful operational and patient quality insights from existing qualitative data.

LLM-based analysis can reduce the time, expertise, and labor required to extract meaningful insights from large qualitative datasets in healthcare settings and potentially uncover deeper insights than traditional methods.

Freely available AI tools increase the accessibility of text-based analytics, enabling healthcare organizations to extract meaningful operational and patient quality insights from existing qualitative data.

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.

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