Deployment and Evaluation of an EHR-integrated, Large Language Model-Powered Tool to Triage Surgical Patients

Deployment and Evaluation of an EHR-integrated, Large Language Model-Powered Tool to Triage Surgical Patients | Signal Canvas | ScienceToStartup

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Signal Canvas proof surface

Canonical route: /signal-canvas/deployment-and-evaluation-of-an-ehr-integrated-large-language-model-powered-tool-to-triage-surgical-patients

stale

Proof freshness: stale
Proof status: unverified
Display score: 8/10
Last proof check: 2026-04-02
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 0
Source count: 0
Coverage: 17%

This page is showing the last landed evidence receipt and score bundle because the latest proof data is outside the freshness window.

Agent Handoff

Canonical ID deployment-and-evaluation-of-an-ehr-integrated-large-language-model-powered-tool-to-triage-surgical-patients | Route /signal-canvas/deployment-and-evaluation-of-an-ehr-integrated-large-language-model-powered-tool-to-triage-surgical-patients

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/signal-canvas/deployment-and-evaluation-of-an-ehr-integrated-large-language-model-powered-tool-to-triage-surgical-patients

MCP example

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source_context

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  "normalized_query": "2603.17234",
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Evidence Receipt

Route status: building

Claims: 7

References: Pending verification

Proof: Verification pending

Freshness state: computing

Source paper: Deployment and Evaluation of an EHR-integrated, Large Language Model-Powered Tool to Triage Surgical Patients

PDF: https://arxiv.org/pdf/2603.17234v1

Source count: Pending verification

Coverage: 17%

Last proof check: 2026-04-02T02:30:40.136Z

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Watch and verify: Deployment and Evaluation of an EHR-integrated, Large Language Model-Powered Tool to Triage Surgical Patients

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Subject: Deployment and Evaluation of an EHR-integrated, Large Language Model-Powered Tool to Triage Surgical Patients

Verdict

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Claim map

Strong 7Mixed 0Weak 0

Evidencepartial
we conducted a prospective, unblinded study at Stanford Health Care in which an LLM-based, electronic health record (EHR)-integrated triage tool (SCM Navigator) provided SCM recommendations followed by physician review.
Implicationpartial
The abstract explicitly states the development and purpose of the SCM Navigator tool.
Verificationpartial
partial
Evidencepartial
SCM Navigator categorized patients as appropriate, not appropriate, or possibly appropriate for SCM.
Implicationpartial
The abstract clearly outlines the categorization scheme used by the SCM Navigator.
Verificationpartial
partial
Evidencepartial
SCM Navigator displayed high sensitivity (0.94, 95% CI 0.91-0.96)
Implicationpartial
The abstract provides a specific numerical value for sensitivity with confidence intervals.
Verificationpartial
partial
Evidencepartial
and moderate specificity (0.74, 95% CI 0.71-0.77).
Implicationpartial
The abstract provides a specific numerical value for specificity with confidence intervals.
Verificationpartial
partial
Evidencepartial
Post-hoc chart review suggested most discrepancies reflect modifiable gaps in clinical criteria, institutional workflow, or physician practice variability rather than LLM misclassification, which accounted for 2 of 19 (11%) false-negative cases.
Implicationpartial
The abstract suggests that LLM misclassification was not the primary cause of discrepancies, citing other factors.
Verificationpartial
partial
Evidencepartial
LLM misclassification, which accounted for 2 of 19 (11%) false-negative cases.
Implicationpartial
The abstract provides a specific percentage for LLM misclassification in false-negative cases.
Verificationpartial
partial
Evidencepartial
and that AI-enabled screening tools can augment and potentially automate time-intensive clinical workflows.
Implicationpartial
This is a concluding statement in the abstract summarizing the broader implication of the findings.
Verificationpartial
partial

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Deployment and Evaluation of an EHR-integrated, Large Language Model-Powered Tool to Triage Surgical Patients

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