arxiv: 2604.28010 · v1 · submitted 2026-04-30 · 💻 cs.LG · cs.AI

Recognition: unknown

Learning from Disagreement: Clinician Overrides as Implicit Preference Signals for Clinical AI in Value-Based Care

Prabhjot Singh , Abhishek Gupta , Chris Betz , Abe Flansburg , Brett Ives , Sudeep Lama , Jung Hoon Son

Authors on Pith no claims yet

Pith reviewed 2026-05-07 06:19 UTC · model grok-4.3

classification 💻 cs.LG cs.AI

keywords clinician overridespreference learningclinical AIvalue-based caresuppression biasoverride taxonomydual learning architectureRLHF

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The pith

Clinician overrides of AI recommendations provide implicit preference data to train models aligned with patient trajectories in value-based care.

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

The paper reframes clinician overrides of clinical AI suggestions as a form of preference signal similar to human feedback in reinforcement learning, but richer because experts provide the signals, real consequences are at stake, and outcomes can be tracked over time. It builds a formal framework with a five-category taxonomy that ties each override type to specific model updates, a preference model that conditions on patient state, organizational context, and clinician capability split into execution and alignment parts, and a dual training process that learns both the reward function and clinician capability through alternating optimization steps. This dual setup is designed to stop a failure mode called suppression bias, where the model stops suggesting correct but difficult actions when clinicians lack the ability to follow them. The authors claim the data properties needed for this approach arise naturally in chronic disease management under outcome-based payment contracts, which supply ongoing records, outcome labels, and variation in clinician skill. If the framework holds, AI training shifts from fitting short-term visit economics toward supporting long-term patient health.

Core claim

Clinician overrides of AI recommendations provide implicit preference data richer than standard RLHF because the annotators are domain experts, alternatives have real consequences, and outcomes are observable. The framework contributes a five-category override taxonomy that maps types to model update targets, a preference formulation conditioned on patient state s, organizational context c, and clinician capability kappa which splits into execution and alignment components, and a dual learning architecture training reward and capability models via alternating optimization to prevent suppression bias where correct but difficult recommendations are systematically suppressed when capability is低

What carries the argument

A dual learning architecture that jointly trains a reward model and a capability model via alternating optimization to prevent suppression bias when learning from clinician overrides.

Load-bearing premise

Clinician overrides in chronic disease management under outcome-based contracts supply enough longitudinal density, outcome labels, and natural capability variation to learn a reward model focused on patient trajectories rather than encounter economics, and alternating optimization prevents suppression bias without creating new biases.

What would settle it

Deploy the dual architecture versus standard preference learning in a value-based care clinic, then measure whether the dual version produces more recommendations that are correct by outcome data but initially overridden due to execution difficulty, and track whether patient trajectories improve.

Figures

Figures reproduced from arXiv: 2604.28010 by Abe Flansburg, Abhishek Gupta, Brett Ives, Chris Betz, Jung Hoon Son, Prabhjot Singh, Sudeep Lama.

**Figure 1.** Figure 1: Suppression bias and its correction. Panel A shows the true latent reward R∗ favoring action a ∗ (e.g., PCP-level SGLT2i initiation) over cardiology referral. Panel B: with uniform preference weighting β(κ) = β0, a population of 50 clinicians in which 70% have low capability for the domain (κ ≈ 0.2) produces a majority override signal against a ∗ . The naive reward model learns the inverted ranking and sup… view at source ↗

read the original abstract

We reframe clinician overrides of clinical AI recommendations as implicit preference data - the same signal structure exploited by reinforcement learning from human feedback (RLHF), but richer: the annotator is a domain expert, the alternatives carry real consequences, and downstream outcomes are observable. We present a formal framework extending standard preference learning with three contributions: a five-category override taxonomy mapping override types to distinct model update targets; a preference formulation conditioned on patient state s, organizational context c, and clinician capability kappa, where kappa decomposes into execution capability kappa-exec and alignment capability kappa-align; and a dual learning architecture that jointly trains a reward model and a capability model via alternating optimization, preventing a failure mode we term suppression bias-the systematic suppression of correct-but-difficult recommendations when clinician capability falls below the execution threshold. We argue that chronic disease management under outcome-based payment contracts produces override data with uniquely favorable properties-longitudinal density, concentrated decision space, outcome labels, and natural capability variation-and that training environments combining longitudinal outcome measurement with aligned financial incentives are a necessary condition for learning a reward model aligned with patient trajectory rather than with encounter economics. This framework emerged from operational work to improve clinician capability in a live value-based care deployment.

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

Overrides in value-based care could supply useful preference data for clinical AI, but the dual architecture's alternating optimization has no equations, algorithm, or tests to show it actually prevents suppression bias.

read the letter

The paper's core move is to treat clinician overrides of AI recommendations as richer preference signals than standard RLHF, because the annotators are experts, the stakes are real, and outcomes are trackable in chronic disease management under outcome-based contracts. It adds a five-category taxonomy that maps override types to different update targets, conditions the preference model on patient state, organizational context, and a clinician capability score split into execution and alignment parts, and proposes joint training of a reward model and capability model through alternating optimization to avoid what it calls suppression bias—where the system learns to suppress correct but difficult recommendations when capability is low. The data properties it highlights—longitudinal density, concentrated decisions, outcome labels, and natural capability variation—follow directly from the payment model and make the setting distinct from generic preference datasets. That framing is the useful part: it connects a practical data source to existing preference-learning ideas without forcing the usual synthetic or crowd-sourced labels. The main weakness is that the central technical claim rests on description alone. No loss functions, update rules, convergence argument, or even pseudocode appear for the alternating optimization, so there is no way to check whether it separates capability from preference or simply trades one bias for another. The favorable data properties are asserted rather than measured against any actual override logs or compared to counter-examples. This leaves the framework as an interesting proposal rather than a demonstrated method. Readers working on healthcare AI alignment or domain-specific RLHF will find the taxonomy and the value-based-care angle worth thinking about; anyone looking for validated algorithms or empirical results will not. The work is coherent on its own terms and engages the relevant literature, so it deserves a serious referee even if the next step is clearly empirical validation.

Referee Report

3 major / 2 minor

Summary. The paper reframes clinician overrides of clinical AI recommendations as implicit preference signals richer than standard RLHF, due to domain expertise, real consequences, and observable outcomes. It presents a formal framework with three contributions: (1) a five-category override taxonomy that maps override types to distinct model update targets; (2) a preference formulation conditioned on patient state s, organizational context c, and clinician capability kappa, where kappa decomposes into execution (kappa-exec) and alignment (kappa-align) components; and (3) a dual learning architecture that jointly trains a reward model and a capability model via alternating optimization to prevent a failure mode termed suppression bias. The authors argue that chronic disease management under outcome-based payment contracts supplies override data with uniquely favorable properties (longitudinal density, concentrated decision space, outcome labels, and capability variation) that are necessary for learning reward models aligned with patient trajectories rather than encounter economics. The framework is said to have emerged from operational work in a live value-based care deployment.

Significance. If the framework can be formalized with explicit loss functions, update rules, and empirical validation, the work could meaningfully extend preference learning to clinical AI by treating overrides as high-quality signals and explicitly modeling clinician capability to avoid suppressing difficult but correct recommendations. The focus on value-based care data properties and the necessity of aligned financial incentives for trajectory-aligned learning identifies a practical setting where such signals may be dense enough to overcome typical preference learning limitations. Credit is due for grounding the proposal in real deployment experience and for identifying suppression bias as a distinct failure mode. However, without the missing mathematical derivations or experiments, the significance remains prospective rather than demonstrated.

major comments (3)

[Abstract] Abstract (dual learning architecture): The central claim that alternating optimization of a reward model and capability model prevents suppression bias is unsupported by any equations, loss functions, update rules, convergence analysis, or pseudocode. This architecture is presented as the third core contribution and the mechanism that avoids systematic suppression of correct-but-difficult recommendations when kappa falls below the execution threshold; without formalization it is impossible to verify that alternation recovers patient-trajectory preferences rather than oscillating or introducing new biases.
[Abstract] Abstract (preference formulation): The decomposition of clinician capability kappa into independent kappa-exec and kappa-align components is introduced as an axiom without derivation, identifiability conditions, or justification for why they can be jointly learned via alternation. This decomposition is load-bearing for the dual architecture and the claim that the model can separate execution limitations from alignment failures; the independence assumption requires explicit modeling to support the taxonomy-to-update-target mapping.
[Abstract] Abstract (data properties argument): The assertion that chronic disease management under outcome-based contracts supplies longitudinal density, outcome labels, and natural capability variation sufficient to learn patient-trajectory-aligned models is stated as a necessary condition but receives no quantitative characterization, simulation study, or counter-example analysis. This data-sufficiency claim underpins the practical relevance of the entire framework; without evidence or bounds it remains an untested assumption.

minor comments (2)

[Abstract] The term 'suppression bias' is newly introduced; a precise definition, mathematical characterization, and distinction from related concepts such as reward hacking or capability-induced preference noise would improve clarity and allow readers to evaluate novelty.
[Abstract] The five-category override taxonomy is described at a high level; providing even one concrete example per category with the corresponding model update target would make the mapping from taxonomy to learning objective concrete.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive review and for recognizing the framework's grounding in deployment experience as well as the identification of suppression bias. We agree that the current manuscript is primarily conceptual and that the abstract claims require explicit formalization, derivations, and supporting analysis to move from prospective to demonstrated. We will revise the paper accordingly by adding a dedicated formalization section, derivations, pseudocode, and a data-characterization subsection with simulation support. Below we address each major comment in turn.

read point-by-point responses

Referee: [Abstract] Abstract (dual learning architecture): The central claim that alternating optimization of a reward model and capability model prevents suppression bias is unsupported by any equations, loss functions, update rules, convergence analysis, or pseudocode. This architecture is presented as the third core contribution and the mechanism that avoids systematic suppression of correct-but-difficult recommendations when kappa falls below the execution threshold; without formalization it is impossible to verify that alternation recovers patient-trajectory preferences rather than oscillating or introducing new biases.

Authors: We acknowledge that the manuscript presents the dual architecture at a high level without explicit equations or analysis. This stems from the paper's origin in operational deployment insights rather than a purely theoretical treatment. In revision we will add a new section that defines the joint objective, specifies the reward-model loss (conditioned on estimated kappa) and capability-model loss, details the alternating update rules with explicit gradients, provides pseudocode for the procedure, and includes a short convergence sketch under the independence assumptions. We will also discuss conditions under which the alternation mitigates suppression bias versus potential oscillation, directly addressing the verifiability concern. revision: yes
Referee: [Abstract] Abstract (preference formulation): The decomposition of clinician capability kappa into independent kappa-exec and kappa-align components is introduced as an axiom without derivation, identifiability conditions, or justification for why they can be jointly learned via alternation. This decomposition is load-bearing for the dual architecture and the claim that the model can separate execution limitations from alignment failures; the independence assumption requires explicit modeling to support the taxonomy-to-update-target mapping.

Authors: The decomposition is motivated by observable patterns in override data, where execution failures (e.g., time or procedural limits) are separable from alignment failures (e.g., priority mismatches) via repeated clinician observations. While introduced concisely in the abstract, the full manuscript will be revised to include an explicit derivation from the five-category taxonomy, identifiability conditions based on longitudinal outcome labels, and a justification showing how alternation permits separate parameter updates without conflating the components. This will strengthen the mapping from taxonomy categories to distinct model-update targets. revision: yes
Referee: [Abstract] Abstract (data properties argument): The assertion that chronic disease management under outcome-based contracts supplies longitudinal density, outcome labels, and natural capability variation sufficient to learn patient-trajectory-aligned models is stated as a necessary condition but receives no quantitative characterization, simulation study, or counter-example analysis. This data-sufficiency claim underpins the practical relevance of the entire framework; without evidence or bounds it remains an untested assumption.

Authors: We agree that the data-properties claim currently rests on deployment experience without quantitative support in the text. In revision we will add a subsection providing anonymized summary statistics from the value-based care program (encounter density per patient, outcome-label coverage, and observed override variation across clinicians). We will also include a minimal simulation study demonstrating how aligned incentives enable trajectory-aligned learning and a brief counter-example analysis showing failure modes when these properties are absent. This will convert the assertion into a supported argument while respecting data-privacy constraints. revision: yes

Circularity Check

0 steps flagged

No significant circularity; framework proposal is self-contained without self-referential derivations

full rationale

The manuscript presents a conceptual framework extending RLHF-style preference learning to clinician overrides. It defines a taxonomy, a state-conditioned preference model involving patient state s, context c, and capability kappa (decomposed into exec/align), and a dual architecture using alternating optimization to avoid suppression bias. No equations, loss functions, update rules, or convergence proofs appear in the provided text. No self-citations are invoked to justify uniqueness or load-bearing premises. No parameters are fitted to data and then relabeled as predictions. The claims rest on operational motivation and stated data properties of value-based care rather than any reduction of outputs to inputs by construction. The derivation chain is therefore independent of the paper's own fitted values or prior self-referential results.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 2 invented entities

The framework rests on domain assumptions about the quality and structure of override data in value-based care and introduces new constructs for capability without independent evidence or derivations.

axioms (2)

domain assumption Clinician overrides represent implicit preference signals that reflect alignment with patient outcomes under outcome-based payment contracts.
Central to the reframing of overrides as preference data; invoked in the abstract's argument for favorable properties of chronic disease management data.
ad hoc to paper Clinician capability decomposes into independent execution (kappa-exec) and alignment (kappa-align) components that can be jointly learned via alternating optimization.
Introduced specifically to address the suppression bias failure mode; no prior justification or evidence provided in the abstract.

invented entities (2)

suppression bias no independent evidence
purpose: Describes the systematic suppression of correct-but-difficult recommendations when clinician capability falls below the execution threshold.
New failure mode term defined to motivate the dual learning architecture.
kappa-exec and kappa-align no independent evidence
purpose: Decomposition of clinician capability for conditioning the preference formulation.
New constructs introduced in the preference model to enable the dual architecture.

pith-pipeline@v0.9.0 · 5538 in / 1753 out tokens · 122398 ms · 2026-05-07T06:19:46.840555+00:00 · methodology

discussion (0)

Forward citations

Cited by 1 Pith paper

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Reference graph

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