Deployment-Centered Evaluation: Predicting Query-Level Rejection Risk in a Clinical LLM System
This paper presents a deployment-centered evaluation of an LLM system integrated into electronic health records at an academic medical center. By training a pre-response classifier that uses query content and deployment-specific context (e.g., provider type, department, language model), the model predicts the risk of user rejection with an AUROC of 0.719 over 4.5 months of prospective analysis. The findings demonstrate the feasibility of predicting user rejection using deployment context, enabling targeted guardrails and abstention strategies.
[2606.12702] Deployment-Centered Evaluation: Predicting Query-Level Rejection Risk in a Clinical LLM System
[Submitted on 10 Jun 2026]
Title:Deployment-Centered Evaluation: Predicting Query-Level Rejection Risk in a Clinical LLM System
View a PDF of the paper titled Deployment-Centered Evaluation: Predicting Query-Level Rejection Risk in a Clinical LLM System, by Alyssa Unell and 6 other authors
View PDF
Abstract:Large language models (LLMs) are increasingly integrated into clinical systems, making it essential to evaluate the real-world utility of these systems. However, static benchmarks tend to measure correctness rather than user acceptance, aggregate performance across queries, and require densely annotated datasets -- leading to major blind spots for evaluating clinical systems. In this work, we perform a deployment-centered evaluation of an LLM system embedded within electronic health records at an academic medical center, where user feedback is sparse but closely reflects the deployment conditions. Specifically, we train a pre-response classifier that estimates the risk that a future interaction will result in the user rejecting the LLM response, based on query content and deployment-specific context available before generation. We conduct a prospective analysis of our model over 4.5 months of user feedback, finding that our prediction model achieves an AUROC of 0.719. Further, we estimate the benefit of such predictions in two downstream use cases (guardrail triggering and abstention). Our key conceptual insight is that making use of deployment-specific context (i.e., the provider type, department name, language model used for response), as opposed to only query content, improves the ability to predict whether the user will reject the system output. Altogether, our empirical case study demonstrates the feasibility of predicting user rejection using deployment-specific context, opening the door to targeted guardrails.
Subjects:
Artificial Intelligence (cs.AI)
Cite as: arXiv:2606.12702 [cs.AI]
(or arXiv:2606.12702v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2606.12702
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Alyssa Unell [view email] [v1] Wed, 10 Jun 2026 21:44:20 UTC (640 KB)
Full-text links:
Access Paper:
View a PDF of the paper titled Deployment-Centered Evaluation: Predicting Query-Level Rejection Risk in a Clinical LLM System, by Alyssa Unell and 6 other authors
View PDF
TeX Source
view license
Current browse context:
cs.AI
new | recent | 2026-06
Change to browse by:
cs
References & Citations
NASA ADS
Google Scholar
Semantic Scholar
Loading...
Data provided by:
Bibliographic Tools
Bibliographic and Citation Tools
Bibliographic Explorer Toggle
Bibliographic Explorer (What is the Explorer?)
Connected Papers Toggle
Connected Papers (What is Connected Papers?)
Litmaps Toggle
Litmaps (What is Litmaps?)
scite.ai Toggle
scite Smart Citations (What are Smart Citations?)
Code, Data, Media
Code, Data and Media Associated with this Article
alphaXiv Toggle
alphaXiv (What is alphaXiv?)
Links to Code Toggle
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub Toggle
DagsHub (What is DagsHub?)
GotitPub Toggle
Gotit.pub (What is GotitPub?)
Huggingface Toggle
Hugging Face (What is Huggingface?)
ScienceCast Toggle
ScienceCast (What is ScienceCast?)
Demos
Demos
Replicate Toggle
Replicate (What is Replicate?)
Spaces Toggle
Hugging Face Spaces (What is Spaces?)
Spaces Toggle
TXYZ.AI (What is TXYZ.AI?)
Related Papers
Recommenders and Search Tools
Link to Influence Flower
Influence Flower (What are Influence Flowers?)
Core recommender toggle
CORE Recommender (What is CORE?)
Author
Venue
Institution
Topic
About arXivLabs
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)