arxiv: 2605.06191 · v1 · submitted 2026-05-07 · 💻 cs.AI

Recognition: unknown

Systematic Evaluation of Large Language Models for Post-Discharge Clinical Action Extraction

Shivali Dalmia , Ananya Mantravadi , Prasanna Desikan

Authors on Pith no claims yet

Pith reviewed 2026-05-08 10:12 UTC · model grok-4.3

classification 💻 cs.AI

keywords clinicalactionextractionllmsmodelsannotationmodelreasoning

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The pith

Large language models match supervised models at detecting actionable tasks in discharge notes but fall short on detailed classification.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper systematically tests zero-shot and few-shot large language models on extracting post-discharge clinical actions from narrative hospital notes in the CLIP dataset. It finds that these models perform as well as or better than task-specific supervised BERT models when simply deciding whether a span is actionable, all without any fine-tuning and while respecting data privacy. Supervised models maintain an edge when assigning those actions to specific fine-grained categories. The evaluation also highlights that many model errors trace back to inconsistencies in how the original dataset was labeled rather than to failures in clinical reasoning. This work matters because accurate extraction of actionable items can support safer transitions of care, yet current benchmarks may not fully reveal whether models truly understand clinical implications.

Core claim

Contemporary LLMs achieve performance comparable to or exceeding supervised models on binary actionability detection from discharge notes, while supervised baselines retain a meaningful advantage on fine-grained multi-label category classification, despite no task-specific fine-tuning and under strict data-privacy constraints. A two-stage extraction framework decomposes narrative notes into explicitly actionable tasks through staged prompting. Qualitative analysis shows that failures often arise from misalignment with dataset annotation conventions, especially for implicit actions and rigid labeling rules, suggesting that labels without rationales prevent distinguishing reasoning failures in

What carries the argument

The two-stage prompting strategy that breaks narrative discharge notes into fine-grained, explicitly actionable clinical tasks for systematic comparison against supervised baselines.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

Creating rationale-annotated datasets could allow clearer tests of whether LLMs possess genuine clinical understanding beyond pattern matching.
Integrating LLMs into clinical workflows for post-discharge planning might be feasible sooner for initial screening than for precise categorization.
Similar evaluation approaches could apply to other safety-critical domains where narrative text needs decomposition into actions.
Future benchmarks should measure not just match to labels but alignment with expert clinical judgment on actionability.

Load-bearing premise

The CLIP dataset's existing annotations accurately capture what counts as clinically actionable, so mismatches with model outputs indicate model limitations instead of differences in labeling conventions.

What would settle it

Re-annotating a subset of the CLIP dataset with explicit rationales for each actionability decision and re-running the models to see if the gap between LLMs and supervised models narrows or disappears.

Figures

Figures reproduced from arXiv: 2605.06191 by Ananya Mantravadi, Prasanna Desikan, Shivali Dalmia.

**Figure 1.** Figure 1: High-level workflow of the proposed LLM-driven annotation framework. Discharge notes are converted to sentence-level instances, processed in view at source ↗

**Figure 2.** Figure 2: Iterative prompt refinement: Each iteration introduces targeted changes by error driven analysis. One representative example is shown per refinement view at source ↗

read the original abstract

The work in this paper evaluates zero-shot and few-shot large language models (LLMs) for safety-critical clinical action extraction using the CLIP discharge-note dataset, with particular emphasis on transitions of care and post-discharge patient safety. To manage the complexity of clinical documentation, we introduce a two-stage extraction framework that decomposes discharge notes, that are written in narrative form, into fine-grained, explicitly actionable clinical tasks through a staged prompting strategy. Our contributions include a systematic assessment of generative LLMs for clinical action extraction, a detailed comparison between general-purpose LLMs and task-specific supervised BERT-based models, and an analysis of annotation inconsistencies across different action categories. We show that contemporary LLMs achieve performance comparable to or exceeding supervised models on binary actionability detection, while supervised baselines retain a meaningful advantage on fine-grained multi-label category classification, despite the absence of task-specific fine-tuning and under strict data-privacy constraints. Qualitative error analysis reveals that many failures stem from misalignment between model reasoning and dataset annotation conventions, particularly in cases involving implicit clinical actions and rigid structural labeling rules. These results indicate that reported performance reflects model limitations due to lack of clinical reasoning, that is not captured by plain annotations. Labels without rationales make it impossible to distinguish clinical reasoning failures from annotation convention mismatches. Advancing clinical NLP requires reasoning-annotated datasets that document why specific spans are actionable, not merely which spans were labeled, enabling proper evaluation of model clinical understanding.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

LLMs match supervised baselines on binary actionability detection in discharge notes but the annotation mismatch analysis is the clearer takeaway.

read the letter

The paper shows that zero- and few-shot LLMs reach performance comparable to or better than fine-tuned BERT on spotting whether a span in a discharge note is actionable, while the supervised models keep an edge on assigning the fine-grained categories. They do this with a two-stage prompting setup that first breaks narrative notes into explicit tasks, all under data-privacy constraints that rule out fine-tuning on the target data. That setup is straightforward and practical for hospital settings where notes cannot leave the premises. The error analysis is the part that lands best: they document how many mismatches trace back to the CLIP dataset's labeling rules rather than model reasoning failures, especially around implicit actions and rigid structural conventions. They correctly note that plain labels without rationales make it impossible to separate those two sources of error. This observation is not new in clinical NLP but is applied here with concrete examples from their runs. The work is incremental on the LLM evaluation side and does not introduce new models or large new datasets. What it does well is run a head-to-head comparison with clear privacy constraints and then use the results to argue for better-annotated data rather than claiming the numbers prove LLM superiority. The soft spot is that the abstract and available details leave the exact metrics, prompt choices, and split handling opaque, so it is difficult to judge how sensitive the binary-versus-multi-label gap is to those decisions. The annotation critique is useful but also undercuts clean attribution of the performance numbers until rationale-augmented labels exist. No load-bearing contradictions appear in what is shown. This is for clinical NLP researchers focused on extraction from notes or on care-transition tools. It is worth bringing to a reading group for the dataset discussion. It deserves peer review because the privacy setup and the call for reasoning annotations are concrete enough to evaluate and build on.

Referee Report

2 major / 2 minor

Summary. The manuscript evaluates zero-shot and few-shot large language models for post-discharge clinical action extraction on the CLIP discharge-note dataset. It introduces a two-stage prompting framework to decompose narrative notes into fine-grained actionable tasks, then compares LLMs against supervised BERT baselines on binary actionability detection (where LLMs are comparable or superior) and multi-label category classification (where supervised models retain an advantage). Qualitative error analysis attributes many failures to misalignment with dataset annotation conventions and concludes that labels without rationales prevent distinguishing clinical reasoning failures from annotation artifacts, calling for reasoning-annotated datasets.

Significance. If the performance comparisons and error attributions hold after addressing annotation confounds, the work would be significant for showing that general-purpose LLMs can approach supervised performance on safety-critical clinical extraction tasks under strict privacy constraints without task-specific fine-tuning, while also identifying a key barrier in current clinical NLP evaluation datasets.

major comments (2)

[Abstract] Abstract: The claim that LLMs achieve performance 'comparable to or exceeding supervised models on binary actionability detection' is load-bearing for the central contribution, yet the abstract simultaneously states that 'many failures stem from misalignment between model reasoning and dataset annotation conventions' and that 'labels without rationales make it impossible to distinguish clinical reasoning failures from annotation convention mismatches.' This creates an unresolved tension: if annotation conventions are the primary source of mismatches, the quantitative gaps cannot be cleanly attributed to model capabilities versus label artifacts, weakening the interpretation that LLMs demonstrate clinical reasoning limits.
[Abstract] Abstract: The final interpretive sentence ('These results indicate that reported performance reflects model limitations due to lack of clinical reasoning, that is not captured by plain annotations') appears to contradict the preceding attribution of failures to annotation misalignment. This internal inconsistency is load-bearing because it directly supports the paper's call for reasoning-annotated datasets; without resolving it, the recommendation for new dataset standards rests on an ambiguous foundation.

minor comments (2)

The abstract would be strengthened by reporting at least one key quantitative result (e.g., F1 or accuracy delta between LLMs and BERT baselines) to ground the comparative claims.
The two-stage prompting framework is described at a high level; adding a brief concrete example of the staged prompts and output format would improve clarity for readers attempting replication.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments on our abstract. We agree that the current wording creates ambiguity between the reported performance results and the interpretive conclusions about annotation limitations. We will revise the abstract to resolve this tension while preserving the core empirical findings and the motivation for reasoning-annotated datasets.

read point-by-point responses

Referee: [Abstract] Abstract: The claim that LLMs achieve performance 'comparable to or exceeding supervised models on binary actionability detection' is load-bearing for the central contribution, yet the abstract simultaneously states that 'many failures stem from misalignment between model reasoning and dataset annotation conventions' and that 'labels without rationales make it impossible to distinguish clinical reasoning failures from annotation convention mismatches.' This creates an unresolved tension: if annotation conventions are the primary source of mismatches, the quantitative gaps cannot be cleanly attributed to model capabilities versus label artifacts, weakening the interpretation that LLMs demonstrate clinical reasoning limits.

Authors: We acknowledge the tension identified. The abstract was structured to first report the binary detection results (which hold under the given labels) and then present the qualitative finding that many errors arise from annotation conventions rather than pure model failure. However, the phrasing does not sufficiently separate these points. We will revise the abstract to state the performance comparison explicitly, followed by a clearer statement that the evaluation is limited by the absence of rationale annotations, thereby avoiding any implication that quantitative gaps are solely due to model capabilities. revision: yes
Referee: [Abstract] Abstract: The final interpretive sentence ('These results indicate that reported performance reflects model limitations due to lack of clinical reasoning, that is not captured by plain annotations') appears to contradict the preceding attribution of failures to annotation misalignment. This internal inconsistency is load-bearing because it directly supports the paper's call for reasoning-annotated datasets; without resolving it, the recommendation for new dataset standards rests on an ambiguous foundation.

Authors: We agree that the final sentence is ambiguously worded and risks being read as claiming inherent model limitations rather than limitations in the evaluation setup. The intended meaning is that plain annotations (without rationales) prevent us from determining whether errors reflect missing clinical reasoning in the model or mismatches with annotation rules. We will rephrase this sentence to emphasize that the current dataset format confounds such diagnosis, thereby strengthening the rationale for datasets that include annotation rationales. This change will be made in the revised abstract. revision: yes

Circularity Check

0 steps flagged

Empirical evaluation study with no derivations or self-referential reductions

full rationale

The paper is a direct empirical comparison of zero/few-shot LLMs against supervised BERT baselines on the external CLIP discharge-note dataset for action extraction. It introduces a two-stage prompting framework as a methodological contribution and reports performance metrics plus qualitative error analysis, but contains no equations, parameter fitting, predictions derived from fitted inputs, or load-bearing self-citations. All claims rest on observable outputs versus dataset labels and external baselines, with no step that reduces by construction to the paper's own inputs or prior author work.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The central claims rest on the assumption that the CLIP dataset provides a reliable ground truth for actionability and that standard prompting techniques transfer to clinical text without domain-specific adaptation beyond the two-stage structure.

axioms (2)

domain assumption Discharge notes contain explicitly or implicitly actionable clinical tasks that can be decomposed into fine-grained categories
Invoked in the description of the two-stage extraction framework and the multi-label classification task.
domain assumption Human annotations in the CLIP dataset reflect clinical reality rather than arbitrary labeling conventions
Central to the interpretation that model-label mismatches indicate lack of clinical reasoning.

pith-pipeline@v0.9.0 · 5562 in / 1444 out tokens · 64090 ms · 2026-05-08T10:12:17.535746+00:00 · methodology

discussion (0)

Reference graph

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