teleo-codex/inbox/queue/2026-03-15-nct07328815-behavioral-nudges-automation-bias-mitigation.md

type	title	author	url	date	domain	secondary_domains	format	status	priority	tags	processed_by	processed_date	enrichments_applied	extraction_model
source	NCT07328815: Ensemble-LLM Confidence Signals as Behavioral Nudge to Mitigate Physician Automation Bias (RCT, Registered 2026)	Follow-on research group to NCT06963957 (Pakistan MBBS physician cohort)	https://clinicaltrials.gov/study/NCT07328815	2026-03-15	health	ai-alignment	research paper	enrichment	medium	automation-bias behavioral-nudge ensemble-llm clinical-ai-safety system-2-thinking multi-agent-ui centaur-model belief-5 nct07328815	vida	2026-03-23	human-in-the-loop clinical AI degrades to worse-than-AI-alone because physicians both de-skill from reliance and introduce errors when overriding correct outputs.md	anthropic/claude-sonnet-4.5

Content

Registered at ClinicalTrials.gov as NCT07328815: "Mitigating Automation Bias in Physician-LLM Diagnostic Reasoning Using Behavioral Nudges." This is the direct follow-on to NCT06963957 (the automation bias RCT archived March 22, 2026).

Study design:

• Single-blind, randomized controlled trial, two parallel arms (1:1)
• Target sample: 50 physicians (25/arm)
• Population: Medical doctors (MBBS) — same cohort as NCT06963957

Intervention — dual-mechanism behavioral nudge:

1. Anchoring cue: Before evaluation begins, participants are shown ChatGPT's average diagnostic reasoning accuracy on standard medical datasets — establishing realistic performance expectations and anchoring System 2 engagement
2. Selective attention cue: Color-coded confidence signals generated for each AI recommendation

Confidence signal generation (the novel multi-agent element):

• Three independent LLMs each provide confidence ratings for every AI recommendation: Claude Sonnet 4.5, Gemini 2.5 Pro Thinking, and GPT-5.1
• Mean confidence across three models determines the signal color (presumably red/yellow/green or equivalent)
• When models DISAGREE on confidence (ensemble spread is high), the signal flags uncertainty
• This is a form of multi-agent architecture used as a UI layer safety tool, not as a clinical reasoning tool

Primary outcome:

• Whether the dual-mechanism nudge reduces physicians' uncritical acceptance of incorrect LLM recommendations (automation bias)
• Secondary: whether anchoring + color signal together outperform either mechanism alone

Related documents:

• Protocol/SAP available at: cdn.clinicaltrials.gov/large-docs/15/NCT07328815/Prot_SAP_000.pdf
• Parent study: NCT06963957 (archived queue: 2026-03-22-automation-bias-rct-ai-trained-physicians.md)
• Arxiv preprint on evidence-based nudges in biomedical context: 2602.10345

Current status: Registered but results not yet published (as of March 2026). Study appears to be recently registered or currently enrolling.

Agent Notes

Why this matters: This is the first operationalized solution to the physician automation bias problem that is being tested in an RCT framework. The parent study (NCT06963957) showed that even 20-hour AI-literacy training fails to prevent automation bias — this trial tests whether a UI-layer intervention (behavioral nudge) can succeed where training failed. The ensemble-LLM confidence signal is a creative design: it doesn't require the physician to know anything about the underlying model; it uses model disagreement as an automatic uncertainty flag. This is a novel application of multi-agent architecture — not for better clinical reasoning (NOHARM's use case) but for better physician reasoning about clinical AI.

What surprised me: The specific models used (Claude Sonnet 4.5, Gemini 2.5 Pro Thinking, GPT-5.1) include three frontier models from three different companies. The design implicitly assumes these models' confidence ratings are correlated enough with accuracy to be informative — if the models all confidently give the same wrong answer, the signal would fail. This is a real limitation: ensemble overconfidence is a known failure mode of multiple models trained on similar data.

What I expected but didn't find: No published results yet. The trial is likely in data collection or analysis. Results would answer the most important open question in automation bias research: can a lightweight UI intervention do what 20 hours of training cannot?

KB connections:

• Direct extension of NCT06963957 (parent study): the automation bias RCT → nudge mitigation trial
• Connects to Belief 5 (clinical AI safety): the centaur model problem requires structural solutions; this trial is testing whether UI design is a viable structural solution
• The ensemble-LLM signal design connects to the Mount Sinai multi-agent architecture paper (npj Health Systems, March 2026) — both are using multi-model approaches but for different purposes
• Cross-domain: connects to Theseus's alignment work on human oversight mechanisms — this is a domain-specific test of whether UI design can maintain meaningful human oversight

Extraction hints: Primary claim: the first RCT of a UI-layer behavioral nudge to reduce physician automation bias in LLM-assisted diagnosis uses an ensemble of three frontier LLMs to generate color-coded confidence signals — operationalizing multi-agent architecture as a safety tool rather than a clinical reasoning tool. This is "experimental" confidence (trial registered, results unpublished). Note the parent study (NCT06963957) as context — the clinical rationale for this trial is established.

Context: This trial is being conducted by researchers who studied automation bias in AI-trained physicians. The 50-participant sample is small; generalizability will be limited even if the nudge shows a significant effect. The trial design is methodologically novel enough to generate high-citation follow-on work regardless of outcome. If the nudge works, it provides a deployable solution. If it fails, it suggests the problem requires architectural (not UI) solutions — which points back to NOHARM's multi-agent recommendation.

Curator Notes (structured handoff for extractor)

PRIMARY CONNECTION: "erroneous LLM recommendations significantly degrade diagnostic accuracy even in AI-trained physicians" (parent study finding) — this trial is testing the UI solution WHY ARCHIVED: First concrete solution attempt for physician automation bias; the ensemble-LLM confidence signal is a novel multi-agent safety design; results (expected 2026) will be highest-value near-term KB update for Belief 5 EXTRACTION HINT: Extract as "experimental" confidence claim about the nudge intervention design. Don't claim efficacy (unpublished). Focus on the design's novelty: multi-agent confidence aggregation as a UI safety layer — the architectural insight is valuable independent of trial outcome. Note that ensemble overconfidence (all models wrong together) is the key limitation to flag in the claim.

Key Facts

• NCT07328815 is a single-blind RCT with 50 physicians (25 per arm) testing automation bias mitigation
• The trial uses three frontier LLMs for confidence signal generation: Claude Sonnet 4.5, Gemini 2.5 Pro Thinking, and GPT-5.1
• The trial is registered at ClinicalTrials.gov as of March 15, 2026
• Protocol and statistical analysis plan available at cdn.clinicaltrials.gov/large-docs/15/NCT07328815/Prot_SAP_000.pdf
• Related arxiv preprint on evidence-based nudges: 2602.10345
• Parent study NCT06963957 showed 20-hour AI-literacy training failed to prevent automation bias