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2
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本文引用的文献

1
Reducing Crowding in Emergency Departments With Early Prediction of Hospital Admission of Adult Patients Using Biomarkers Collected at Triage: Retrospective Cohort Study.利用分诊时收集的生物标志物对成年患者入院进行早期预测以减少急诊科拥挤情况:回顾性队列研究
JMIR Bioinform Biotechnol. 2022 Sep 13;3(1):e38845. doi: 10.2196/38845.
2
Models Predicting Hospital Admission of Adult Patients Utilizing Prehospital Data: Systematic Review Using PROBAST and CHARMS.利用院前数据预测成年患者住院情况的模型:采用PROBAST和CHAMRS的系统评价
JMIR Med Inform. 2021 Sep 16;9(9):e30022. doi: 10.2196/30022.
3
Predicting emergency department visits in a large teaching hospital.预测大型教学医院的急诊科就诊情况。
Int J Emerg Med. 2021 Jun 12;14(1):34. doi: 10.1186/s12245-021-00357-6.
4
Prediction admission in the older population in the Emergency Department: the CLEARED tool.急诊科老年人群入院预测:CLEARED 工具。
Neth J Med. 2020 Dec;78(6):357-367.
5
Machine Learning Versus Usual Care for Diagnostic and Prognostic Prediction in the Emergency Department: A Systematic Review.机器学习与常规护理在急诊科诊断和预后预测中的比较:系统评价。
Acad Emerg Med. 2021 Feb;28(2):184-196. doi: 10.1111/acem.14190. Epub 2021 Jan 2.
6
Using machine-learning risk prediction models to triage the acuity of undifferentiated patients entering the emergency care system: a systematic review.使用机器学习风险预测模型对进入急诊护理系统的未分化患者的 acuity 进行分诊:一项系统综述。 (注:这里“acuity”在医学语境中可能有“ acuity of illness 病情严重程度”等含义,具体需结合上下文准确理解,但按照要求不添加解释。)
Diagn Progn Res. 2020 Oct 2;4:16. doi: 10.1186/s41512-020-00084-1. eCollection 2020.
7
Can medical practitioners rely on prediction models for COVID-19? A systematic review.医疗从业者能否依赖新冠病毒预测模型?一项系统综述。
Evid Based Dent. 2020 Sep;21(3):84-86. doi: 10.1038/s41432-020-0115-5.
8
Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal.COVID-19 诊断和预后预测模型:系统评价和批判性评估。
BMJ. 2020 Apr 7;369:m1328. doi: 10.1136/bmj.m1328.
9
Clinically useful prediction of hospital admissions in an older population.对老年人群进行临床有用的住院预测。
BMC Geriatr. 2020 Mar 6;20(1):95. doi: 10.1186/s12877-020-1475-6.
10
PROBAST: A Tool to Assess Risk of Bias and Applicability of Prediction Model Studies: Explanation and Elaboration.PROBAST:一种用于评估偏倚风险和预测模型研究适用性的工具:说明和阐述。
Ann Intern Med. 2019 Jan 1;170(1):W1-W33. doi: 10.7326/M18-1377.

预测模型和系统流程方法在缓解急诊科拥挤问题中的作用:立场文件。

The Utility of Predictive Modeling and a Systems Process Approach to Reduce Emergency Department Crowding: A Position Paper.

作者信息

Monahan Ann Corneille, Feldman Sue S

机构信息

University of Alabama at Birmingham, Birmingham, AL, United States.

Department of Health Services Administration, University of Alabama at Birmingham, Birmingham, AL, United States.

出版信息

Interact J Med Res. 2023 Jul 10;12:e42016. doi: 10.2196/42016.

DOI:10.2196/42016
PMID:37428536
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10366955/
Abstract

Emergency department (ED) crowding and its main causes, exit block and boarding, continue to threaten the quality and safety of ED care. Most interventions to reduce crowding have not been comprehensive or system solutions, only focusing on part of the care procession and not directly affecting boarding reduction. This position paper proposes that the ED crowding problem can be optimally addressed by applying a systems approach using predictive modeling to identify patients at risk of being admitted to the hospital and uses that information to initiate the time-consuming bed management process earlier in the care continuum, shortening the time during which patients wait in the ED for an inpatient bed assignment, thus removing the exit block that causes boarding and subsequently reducing crowding.

摘要

急诊科(ED)拥挤及其主要原因——出口阻塞和住院滞留,继续威胁着急诊护理的质量和安全。大多数减少拥挤的干预措施并非全面的系统解决方案,仅关注护理流程的一部分,并未直接影响住院滞留的减少。本立场文件提出,通过采用系统方法,利用预测模型识别有住院风险的患者,并利用这些信息在护理连续过程中更早地启动耗时的床位管理流程,缩短患者在急诊科等待住院床位分配的时间,从而消除导致住院滞留进而减少拥挤的出口阻塞,可最佳地解决急诊科拥挤问题。