Beyond the Numbers: Using AI to Support ACGME Survey Analysis

Courtney Dolan
1 hour ago
3 min read

ACGME Resident and Faculty Survey data provide valuable insight into the learning environment, program culture, and institutional performance. However, while the reports contain meaningful information, interpreting that data in a way that leads to actionable improvement can be time intensive, particularly during Annual Program Evaluation (APE) and annual reporting cycles.

As AI tools continue to evolve, many GME leaders are beginning to explore how these technologies can support administrative and analytical workflows. At Germane Solutions, we examined whether AI could help programs move beyond surface-level review and support more efficient, thoughtful analysis of ACGME survey results.

Our goal was not to replace human interpretation or institutional context, but to identify practical ways AI could assist GME teams in organizing information, identifying trends, and generating more focused questions for further review.

Exploring the Role of AI in Survey Analysis

Through a series of structured prompt tests using common ACGME survey report formats, we explored how AI could support early-stage analysis and streamline portions of the review process.

We found that AI was particularly effective at:

Summarizing key findings in a clear and organized format
Identifying recurring themes and potential areas for further review
Highlighting relationships across survey domains
Generating questions and hypotheses for deeper exploration

This was especially valuable when reviewing multiple programs or analyzing trends across survey years, where manual review can become repetitive and time consuming.

The Real Value: Asking Better Questions and Supporting Deeper Review

ACGME survey data rarely tell the full story on their own. Survey results require interpretation, context, and thoughtful follow-up to determine whether findings reflect broader trends, isolated concerns, or opportunities for improvement.

Used thoughtfully, AI can support this process by helping teams:

Frame patterns in a way that encourages further inquiry
Identify areas that may warrant closer attention
Improve consistency and efficiency in analysis workflows
Suggest avenues for deeper analysis rather than conclusions to accept at face value

In this way, AI serves as a support tool for GME leaders, helping reduce administrative burden while strengthening the analytical process.

The Importance of Prompting and Human Oversight

Throughout this process, it became clear that the usefulness of AI depends heavily on how it is guided. Effective prompts significantly influence the quality, structure, and relevance of the analysis produced.

At the same time, AI output should never replace direct review of survey data or institutional judgment. Local context, program culture, and operational realities remain essential to interpreting results accurately and determining appropriate next steps.

As part of this work, we refined a series of prompts designed specifically to support program-level and institutional-level survey review. These prompts were developed through practical testing and iterative refinement to better align with how GME leaders evaluate trends, identify opportunities, and prepare for discussions during APE, GMEC, and institutional review processes.

Practical Applications for GME Programs and Institutions

When used responsibly, AI can support several common GME workflows, including:

Annual Program Evaluation (APE) preparation
GMEC and institutional reporting
Longitudinal survey trend analysis
Standardized summary and documentation development

Even limited, targeted use can help programs improve efficiency, consistency, and clarity during the review process.

Downloadable AI Prompts for ACGME Survey Review

To support GME leaders exploring this approach, we are sharing downloadable prompt guides developed through our testing process. These resources are intended to serve as practical starting points that programs and institutions can adapt to their own workflows and review needs.

The prompts are not designed to replace expertise or decision-making. Rather, they are intended to support more efficient analysis and help teams focus time and attention where it matters most.

Ultimately, AI is meant to support, not replace, the expertise and judgment that define high-quality GME. As these tools continue to evolve, we see meaningful opportunities to help programs and institutions leverage AI responsibly to reduce burden, deepen insight, and strengthen the learning environment.

Download the prompt guides below to explore how AI can support your survey review process, and contact us to learn how Germane Solutions can help your institution leverage AI responsibly within GME operations.