---
title: "Using Large Language Models to Detect Acute Coronary Syndrome Management Delays"
id: "pubmed-42259441"
canonical_url: "https://medichelpline.com/clinical-feed/pubmed-42259441"
content_type: "clinical_feed_article"
specialty: "Cardiology"
source_name: "PubMed / NCBI"
source_url: "https://pubmed.ncbi.nlm.nih.gov/42259441/"
doi: "10.1016/j.amjcard.2026.05.031"
published_at: "2026-08-15T00:00:00.000Z"
evidence_level: "Journal Article"
license: "CC-BY-NC-4.0 / Informational Use"
---
# Using Large Language Models to Detect Acute Coronary Syndrome Management Delays
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/pubmed-42259441
- **Specialty:** [Cardiology](https://medichelpline.com/clinical-feed/cardiology.md)
- **Primary Source:** PubMed / NCBI
- **Source URL:** [Original Journal Publication](https://pubmed.ncbi.nlm.nih.gov/42259441/)
- **DOI:** [10.1016/j.amjcard.2026.05.031](https://doi.org/10.1016%2Fj.amjcard.2026.05.031)
- **Published At:** 2026-08-15T00:00:00.000Z
- **Evidence Rating:** Journal Article
## Executive GIST (TL;DR)
- The study developed a **large language model (LLM)**–based system to identify management delays in **acute coronary syndrome (ACS)** from clinical notes. - Admissions to internal medicine residents at NYU from July 2022 to June 2025 (n = 4,642) were included as the dataset for analysis. - Prompt validation used resident admission notes and initial cardiology consult notes as ground truth; 161 notes were used for each validation set. - Discordant cases between LLM outputs and ground truth were reviewed by three physicians using a validated tool to confirm management delays. - The LLM identified management delays with a positive predictive value of 52% (35 of 67 flagged cases). - Patients with confirmed management delays were older (mean 73.4 ± 15.3 vs 68.5 ± 12.6 years, p = 0.036), more often female (56.8% vs 34%, p = 0.014), and more likely to list a preferred language other than English or Spanish (27.0% vs 15.5%, p = 0.046). - Confirmed delay cases had longer times to receiving key therapies: **heparin** (56.91 ± 56.78 vs 18.97 ± 13.76 hours, p < 0.001), **aspirin** (13.94 ± 16.64 vs 8.23 ± 9.82 hours, p = 0.005), and **cardiac catheterization** (65.12 ± 51.65 vs 39.51 ± 44.19 hours, p = 0.006). - The authors conclude the LLM system can detect ACS management delays at scale and may inform individual and systems-level interventions to improve ACS care quality. - Keywords reported: acute coronary syndrome; diagnostic errors; large language models; management delay. - The authors declared no competing financial interests or personal relationships influencing the work.
## Clinical Analysis & Structured Key Points
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Epub 2026 Jun 7. # Large Language Model-Based Identification of Acute Coronary Syndrome Management Delays [Verity Schaye](https://pubmed.ncbi.nlm.nih.gov/?term=Schaye+V&cauthor_id=42259441)[ 1 ](https://pubmed.ncbi.nlm.nih.gov/42259441/#full-view-affiliation-1 "Department of Medicine, Division of General Internal Medicine and Clinical Innovation, NYU Langone Health, New York, New York; Institute for Innovations in Medical Education, NYU Grossman School of Medicine, New York, New York; Department of Medicine, Division of Hospital Medicine, NYU Langone Health, New York, New York. Electronic address: verity.schaye@nyulangone.org."), [Bijal Rajput](https://pubmed.ncbi.nlm.nih.gov/?term=Rajput+B&cauthor_id=42259441)[ 2 ](https://pubmed.ncbi.nlm.nih.gov/42259441/#full-view-affiliation-2 "Department of Medicine, Division of General Internal Medicine and Clinical Innovation, NYU Langone Health, New York, New York; Department of Medicine, Division of Hospital Medicine, NYU Langone Health, New York, New York."), [Lexi Signoriello](https://pubmed.ncbi.nlm.nih.gov/?term=Signoriello+L&cauthor_id=42259441)[ 3 ](https://pubmed.ncbi.nlm.nih.gov/42259441/#full-view-affiliation-3 "Institute for Innovations in Medical Education, NYU Grossman School of Medicine, New York, New York."), [Jesse Burk-Rafel](https://pubmed.ncbi.nlm.nih.gov/?term=Burk-Rafel+J&cauthor_id=42259441)[ 4 ](https://pubmed.ncbi.nlm.nih.gov/42259441/#full-view-affiliation-4 "Department of Medicine, Division of General Internal Medicine and Clinical Innovation, NYU Langone Health, New York, New York; Institute for Innovations in Medical Education, NYU Grossman School of Medicine, New York, New York; Department of Medicine, Division of Hospital Medicine, NYU Langone Health, New York, New York."), [Benedict Guzman](https://pubmed.ncbi.nlm.nih.gov/?term=Guzman+B&cauthor_id=42259441)[ 5 ](https://pubmed.ncbi.nlm.nih.gov/42259441/#full-view-affiliation-5 "Division of Applied AI Technologies, NYU Langone Health, New York, New York."), [Tyler Webster](https://pubmed.ncbi.nlm.nih.gov/?term=Webster+T&cauthor_id=42259441)[ 6 ](https://pubmed.ncbi.nlm.nih.gov/42259441/#full-view-affiliation-6 "Department of Medicine, Leon H. Charney Division of Cardiology, NYU Langone Health, New York, New York."), [Daniel J Sartori](https://pubmed.ncbi.nlm.nih.gov/?term=Sartori+DJ&cauthor_id=42259441)[ 2 ](https://pubmed.ncbi.nlm.nih.gov/42259441/#full-view-affiliation-2 "Department of Medicine, Division of General Internal Medicine and Clinical Innovation, NYU Langone Health, New York, New York; Department of Medicine, Division of Hospital Medicine, NYU Langone Health, New York, New York.") Affiliations Expand ### Affiliations * 1 Department of Medicine, Division of General Internal Medicine and Clinical Innovation, NYU Langone Health, New York, New York; Institute for Innovations in Medical Education, NYU Grossman School of Medicine, New York, New York; Department of Medicine, Division of Hospital Medicine, NYU Langone Health, New York, New York. Electronic address: verity.schaye@nyulangone.org. * 2 Department of Medicine, Division of General Internal Medicine and Clinical Innovation, NYU Langone Health, New York, New York; Department of Medicine, Division of Hospital Medicine, NYU Langone Health, New York, New York. * 3 Institute for Innovations in Medical Education, NYU Grossman School of Medicine, New York, New York. * 4 Department of Medicine, Division of General Internal Medicine and Clinical Innovation, NYU Langone Health, New York, New York; Institute for Innovations in Medical Education, NYU Grossman School of Medicine, New York, New York; Department of Medicine, Division of Hospital Medicine, NYU Langone Health, New York, New York. * 5 Division of Applied AI Technologies, NYU Langone Health, New York, New York. * 6 Department of Medicine, Leon H. Charney Division of Cardiology, NYU Langone Health, New York, New York. * PMID: **42259441** * DOI: [ 10.1016/j.amjcard.2026.05.031 ](https://doi.org/10.1016/j.amjcard.2026.05.031) Item in Clipboard # Large Language Model-Based Identification of Acute Coronary Syndrome Management Delays Verity Schaye et al. Am J Cardiol. 2026. Show details Display options Display options Format Abstract PubMed PMID Am J Cardiol Actions * [ Search in PubMed ](https://pubmed.ncbi.nlm.nih.gov/?term=%22Am+J+Cardiol%22%5Bjour%5D&sort=date&sort_order=desc) * [ Search in NLM Catalog ](https://www.ncbi.nlm.nih.gov/nlmcatalog?term=%22Am+J+Cardiol%22%5BTitle+Abbreviation%5D) * [ Add to Search ](https://pubmed.ncbi.nlm.nih.gov/42259441/) . 2026 Aug 15:273:25-31. doi: 10.1016/j.amjcard.2026.05.031. Epub 2026 Jun 7. ### Authors [Verity Schaye](https://pubmed.ncbi.nlm.nih.gov/?term=Schaye+V&cauthor_id=42259441)[ 1 ](https://pubmed.ncbi.nlm.nih.gov/42259441/#short-view-affiliation-1 "Department of Medicine, Division of General Internal Medicine and Clinical Innovation, NYU Langone Health, New York, New York; Institute for Innovations in Medical Education, NYU Grossman School of Medicine, New York, New York; Department of Medicine, Division of Hospital Medicine, NYU Langone Health, New York, New York. Electronic address: verity.schaye@nyulangone.org."), [Bijal Rajput](https://pubmed.ncbi.nlm.nih.gov/?term=Rajput+B&cauthor_id=42259441)[ 2 ](https://pubmed.ncbi.nlm.nih.gov/42259441/#short-view-affiliation-2 "Department of Medicine, Division of General Internal Medicine and Clinical Innovation, NYU Langone Health, New York, New York; Department of Medicine, Division of Hospital Medicine, NYU Langone Health, New York, New York."), [Lexi Signoriello](https://pubmed.ncbi.nlm.nih.gov/?term=Signoriello+L&cauthor_id=42259441)[ 3 ](https://pubmed.ncbi.nlm.nih.gov/42259441/#short-view-affiliation-3 "Institute for Innovations in Medical Education, NYU Grossman School of Medicine, New York, New York."), [Jesse Burk-Rafel](https://pubmed.ncbi.nlm.nih.gov/?term=Burk-Rafel+J&cauthor_id=42259441)[ 4 ](https://pubmed.ncbi.nlm.nih.gov/42259441/#short-view-affiliation-4 "Department of Medicine, Division of General Internal Medicine and Clinical Innovation, NYU Langone Health, New York, New York; Institute for Innovations in Medical Education, NYU Grossman School of Medicine, New York, New York; Department of Medicine, Division of Hospital Medicine, NYU Langone Health, New York, New York."), [Benedict Guzman](https://pubmed.ncbi.nlm.nih.gov/?term=Guzman+B&cauthor_id=42259441)[ 5 ](https://pubmed.ncbi.nlm.nih.gov/42259441/#short-view-affiliation-5 "Division of Applied AI Technologies, NYU Langone Health, New York, New York."), [Tyler Webster](https://pubmed.ncbi.nlm.nih.gov/?term=Webster+T&cauthor_id=42259441)[ 6 ](https://pubmed.ncbi.nlm.nih.gov/42259441/#short-view-affiliation-6 "Department of Medicine, Leon H. Charney Division of Cardiology, NYU Langone Health, New York, New York."), [Daniel J Sartori](https://pubmed.ncbi.nlm.nih.gov/?term=Sartori+DJ&cauthor_id=42259441)[ 2 ](https://pubmed.ncbi.nlm.nih.gov/42259441/#short-view-affiliation-2 "Department of Medicine, Division of General Internal Medicine and Clinical Innovation, NYU Langone Health, New York, New York; Department of Medicine, Division of Hospital Medicine, NYU Langone Health, New York, New York.") ### Affiliations * 1 Department of Medicine, Division of General Internal Medicine and Clinical Innovation, NYU Langone Health, New York, New York; Institute for Innovations in Medical Education, NYU Grossman School of Medicine, New York, New York; Department of Medicine, Division of Hospital Medicine, NYU Langone Health, New York, New York. Electronic address: verity.schaye@nyulangone.org. * 2 Department of Medicine, Division of General Internal Medicine and Clinical Innovation, NYU Langone Health, New York, New York; Department of Medicine, Division of Hospital Medicine, NYU Langone Health, New York, New York. * 3 Institute for Innovations in Medical Education, NYU Grossman School of Medicine, New York, New York. * 4 Department of Medicine, Division of General Internal Medicine and Clinical Innovation, NYU Langone Health, New York, New York; Institute for Innovations in Medical Education, NYU Grossman School of Medicine, New York, New York; Department of Medicine, Division of Hospital Medicine, NYU Langone Health, New York, New York. * 5 Division of Applied AI Technologies, NYU Langone Health, New York, New York. * 6 Department of Medicine, Leon H. Charney Division of Cardiology, NYU Langone Health, New York, New York. * PMID: **42259441** * DOI: [ 10.1016/j.amjcard.2026.05.031 ](https://doi.org/10.1016/j.amjcard.2026.05.031) Item in Clipboard Full text links Cite Display options Display options Format Abstract PubMed PMID ## Abstract Acute coronary syndrome (ACS) requires prompt treatment, yet management delays are difficult to identify. In this study, we developed a large language model (LLM) system to identify ACS management delays and characterized delay cases. Admissions to internal medicine residents at NYU from July 2022 to June 2025 (n = 4,642) were included. Prompts were validated to determine if the resident admission note documented initiation of ACS management and if the initial cardiology consult note documented initiation of ACS management (ground truth) (n = 161 for each). Discordant cases were reviewed by three physicians using a validated tool to confirm management delays. Demographics and key clinical findings of patients with and without delays were compared. The LLM identified management delays with a 52% positive predictive value (n = 35/67). Patients who were older, females, and with preferred language other than English or Spanish were more likely to have a management delay (73.4 ± 15.3 vs 68.5 ± 12.6 years old, p = 0.036, 56.8% vs 34% females, p = 0.014, and 27.0% vs 15.5% other preferred language, p = 0.046, in management delay vs non-management delay cases). The management delay group had longer average time in hours to receiving heparin, aspirin, and cardiac catheterization (56.91 ± 56.78 vs 18.97 ± 13.76, p < 0.001, 13.94 ± 16.64 vs 8.23 ± 9.82, p = 0.005, and 65.12 ± 51.65 vs 39.51 ± 44.19, p = 0.006, respectively in management delay vs non-management delay cases). In conclusion, the LLM-based system we developed to identify ACS management delays can detect cases at scale to inform individual and systems-level interventions to improve quality of ACS care. **Keywords:** acute coronary syndrome; diagnostic errors; large language models; management delay. Copyright © 2026 Elsevier Inc. All rights reserved. [PubMed Disclaimer](https://pubmed.ncbi.nlm.nih.gov/disclaimer/) ## Conflict of interest statement Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this article. All those designated as authors meet all criteria for authorship, and all who meet the criteria are identified as authors. ## Similar articles * [ Assessing sensitivity and specificity of the Manchester Triage System in the evaluation of acute coronary syndrome in adult patients in emergency care: a systematic review protocol. ](https://pubmed.ncbi.nlm.nih.gov/26657465/) Nishi FA, de Motta Maia FO, de Lopes Monteiro da Cruz DA.Nishi FA, et al.JBI Database System Rev Implement Rep. 2015 Nov;13(11):64-73. doi: 10.11124/jbisrir-2015-2213.JBI Database System Rev Implement Rep. 2015.PMID: 26657465 * [ The management of acute coronary syndrome patients across New Zealand in 2012: results of a third comprehensive nationwide audit and observations of current interventional care. ](https://pubmed.ncbi.nlm.nih.gov/24362734/) Ellis C, Gamble G, Devlin G, Elliott J, Hamer A, Williams M, Matsis P, Troughton R, Ranasinghe I, French J, Brieger D, Chew D, White H; New Zealand Acute Coronary Syndromes (NZACS) SNAPSHOT Audit Group.Ellis C, et al.N Z Med J. 2013 Dec 13;126(1387):36-68.N Z Med J. 2013.PMID: 24362734 * [ Slow-onset and fast-onset symptom presentations in acute coronary syndrome (ACS): new perspectives on prehospital delay in patients with ACS. ](https://pubmed.ncbi.nlm.nih.gov/24126070/) O'Donnell S, McKee G, Mooney M, O'Brien F, Moser DK.O'Donnell S, et al.J Emerg Med. 2014 Apr;46(4):507-15. doi: 10.1016/j.jemermed.2013.08.038. Epub 2013 Oct 11.J Emerg Med. 2014.PMID: 24126070Clinical Trial. * [ Folic acid supplementation and malaria susceptibility and severity among people taking antifolate antimalarial drugs in endemic areas. ](https://pubmed.ncbi.nlm.nih.gov/36321557/) Crider K, Williams J, Qi YP, Gutman J, Yeung L, Mai C, Finkelstain J, Mehta S, Pons-Duran C, Menéndez C, Moraleda C, Rogers L, Daniels K, Green P.Crider K, et al.Cochrane Database Syst Rev. 2022 Feb 1;2(2022):CD014217. doi: 10.1002/14651858.CD014217.Cochrane Database Syst Rev. 2022.Update in: [Cochrane Database Syst Rev. 2026 Feb 18;2:CD014217. doi: 10.1002/14651858.CD014217.pub2.](https://pubmed.ncbi.nlm.nih.gov/41705996/)PMID: 36321557Free PMC article.Updated. * [ Inconsistent measurement of acute coronary syndrome patients' pre-hospital delay in research: a review of the literature. ](https://pubmed.ncbi.nlm.nih.gov/24532675/) Mackay MH, Ratner PA, Nguyen M, Percy M, Galdas P, Grunau G.Mackay MH, et al.Eur J Cardiovasc Nurs. 2014 Dec;13(6):483-93. doi: 10.1177/1474515114524866. 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