Machine Learning to Enhance Electronic Detection of Diagnostic Errors-Reference-Cited by-同舟云学术

Machine Learning to Enhance Electronic Detection of Diagnostic Errors

Published:2024-09-09 Issue:9 Volume:7 Page:e2431982
ISSN:2574-3805
Container-title:JAMA Network Open
language:en
Short-container-title:JAMA Netw Open

Author:

Zimolzak Andrew J.¹²,Wei Li¹²,Mir Usman¹²,Gupta Ashish¹²,Vaghani Viralkumar¹²,Subramanian Devika³,Singh Hardeep¹²

Affiliation:

1. Center for Innovations in Quality, Effectiveness and Safety, Michael E. DeBakey Veterans Affairs Medical Center and Baylor College of Medicine, Houston, Texas

2. Department of Medicine, Baylor College of Medicine, Houston, Texas

3. Department of Computer Science, Rice University, Houston, Texas

Abstract

This cohort study examines whether machine learning (ML) can enhance the ability of electronic triggers to identify possible missed opportunities in diagnosis.

Publisher

American Medical Association (AMA)

Link

https://jamanetwork.com/journals/jamanetworkopen/articlepdf/2823187/zimolzak_2024_ld_240142_1724790312.5636.pdf

Reference6 articles.

1. Application of electronic trigger tools to identify targets for improving diagnostic safety.;Murphy;BMJ Qual Saf,2019

2. Validation of an electronic trigger to measure missed diagnosis of stroke in emergency departments.;Vaghani;J Am Med Inform Assoc,2021

3. Artificial intelligence in clinical diagnosis: opportunities, challenges, and hype.;Kulkarni;JAMA,2023

4. Symptom-Disease Pair Analysis of Diagnostic Error (SPADE): a conceptual framework and methodological approach for unearthing misdiagnosis-related harms using big data.;Liberman;BMJ Qual Saf,2018

5. Improving prehospital stroke diagnosis using natural language processing of paramedic reports.;Mayampurath;Stroke,2021