Suppr超能文献

违反医嘱离院的创伤患者特征:一项基于2009 - 2016年国家医院门诊医疗调查的八年调查分析

Characteristics of trauma patients that leave against medical advice: An eight-year survey analysis using the National Hospital Ambulatory Medical Care Survey, 2009-2016.

作者信息

Adeyemi Oluwaseun John, Veri Shelby

机构信息

Department of Public Health, University of North Carolina at Charlotte, North Carolina, 28223, USA.

Department of Health Services Research, University of North Carolina at Charlotte, North Carolina, 28223, USA.

出版信息

J Clin Orthop Trauma. 2021 Jan 27;17:18-24. doi: 10.1016/j.jcot.2021.01.011. eCollection 2021 Jun.

Abstract

BACKGROUND

Leaving against medical advice (AMA) is associated with increased readmission rates, fragmented patient care, and healthcare litigation. Understanding the factors associated with trauma patients leaving AMA from acute care settings will help guide better communication with trauma patients and improve patient satisfaction. This study aims to assess the sociodemographic and in-hospital care characteristics of trauma patients that leave AMA from acute care centers across the U.S.

METHODS

We pooled and analyzed eight years of data (2009-2016) from the National Hospital Ambulatory Medical Care Survey. The outcome variable was whether the patient left AMA or not. The main predictors were the triage class, weekend presentation, health insurance status, the presence of chronic diseases, and the receipt of therapeutic and diagnostic procedures. The sociodemographic characteristics -age, sex, and race/ethnicity, were measured as potential confounders in the developed model. We performed logistic regression and reported the unadjusted and adjusted odds of leaving AMA as well as the 95% confidence intervals.

RESULTS

The weighted percent of the trauma patient population that left AMA was 1.8%. The odds of leaving AMA decreased with advancing age, and increased among non-Hispanic Blacks, compared with non-Hispanic Whites. After adjusting for age, race, and gender, the odds of leaving AMA increased among patients that lacked health insurance (AOR: 1.86; 95% CI: 1.51-2.31), and had diagnostic procedures (AOR: 2.79; 95% CI: 2.32-3.36). The odds of leaving AMA reduced among trauma patients who were classified as emergent (AOR: 0.70; 95% CI: 0.50-0.98) and had therapeutic procedures (AOR: 0.39; 95% CI: 0.32-0.47).

CONCLUSION

Predicting trauma patients with increased odds of leaving AMA will inform intentional communication that may reduce leaving AMA rates and improve care.

摘要

背景

擅自离院(AMA)与再入院率增加、患者护理碎片化以及医疗纠纷相关。了解创伤患者在急症护理环境中擅自离院的相关因素,将有助于指导与创伤患者进行更好的沟通并提高患者满意度。本研究旨在评估美国各地急症护理中心擅自离院的创伤患者的社会人口统计学特征和住院护理特点。

方法

我们汇总并分析了来自国家医院门诊医疗调查的八年数据(2009 - 2016年)。结果变量为患者是否擅自离院。主要预测因素为分诊类别、周末就诊情况、健康保险状况、慢性病的存在以及接受治疗和诊断程序的情况。社会人口统计学特征——年龄、性别和种族/民族,在构建的模型中作为潜在混杂因素进行测量。我们进行了逻辑回归分析,并报告了未经调整和调整后的擅自离院几率以及95%置信区间。

结果

擅自离院的创伤患者加权百分比为1.8%。擅自离院的几率随着年龄增长而降低,与非西班牙裔白人相比,非西班牙裔黑人中擅自离院的几率增加。在调整年龄、种族和性别后,没有健康保险的患者(调整后优势比:1.86;95%置信区间:1.51 - 2.31)以及接受诊断程序的患者(调整后优势比:2.79;95%置信区间:2.32 - 3.36)擅自离院的几率增加。被分类为急症的创伤患者(调整后优势比:0.70;95%置信区间:0.50 - 0.98)以及接受治疗程序的患者(调整后优势比:0.39;95%置信区间:0.32 - 0.47)擅自离院的几率降低。

结论

预测擅自离院几率增加的创伤患者,将有助于进行有针对性的沟通,这可能会降低擅自离院率并改善护理。

相似文献

2
"I'm Leaving": Factors That Impact Against Medical Advice Disposition Post-Trauma.
J Emerg Med. 2020 Apr;58(4):691-697. doi: 10.1016/j.jemermed.2019.12.023. Epub 2020 Mar 12.
3
Leaving Against Medical Advice From Children's Hospitals.
Pediatrics. 2024 Nov 1;154(5). doi: 10.1542/peds.2023-064958.
4
Predictors of discharge against medical advice in adult trauma patients.
Surgeon. 2020 Feb;18(1):12-18. doi: 10.1016/j.surge.2019.04.001. Epub 2019 May 2.
6
Factors associated with pediatric trauma patients leaving against medical advice.
Am J Emerg Med. 2024 May;79:152-156. doi: 10.1016/j.ajem.2024.02.036. Epub 2024 Feb 24.
8
HIV-positive injection drug users who leave the hospital against medical advice: the mitigating role of methadone and social support.
J Acquir Immune Defic Syndr. 2004 Jan 1;35(1):56-9. doi: 10.1097/00126334-200401010-00008.
10
Racial Differences in Length of Stay for Patients Who Leave Against Medical Advice from U.S. General Hospitals.
Int J Environ Res Public Health. 2015 Dec 31;13(1):95. doi: 10.3390/ijerph13010095.

引用本文的文献

1
Utility of an Artificial Intelligence Language Model for Post-Operative Patient Instructions Following Facial Trauma.
Craniomaxillofac Trauma Reconstr. 2024 Dec;17(4):291-294. doi: 10.1177/19433875231222803. Epub 2023 Dec 16.
2
A study of "left against medical advice" emergency department patients: an optimized explainable artificial intelligence framework.
Health Care Manag Sci. 2024 Dec;27(4):485-502. doi: 10.1007/s10729-024-09684-5. Epub 2024 Aug 13.
3
Characterizing long-term outcomes following AMA discharges after assault-related penetrating trauma.
J Inj Violence Res. 2024 Mar 2;16(1):41-8. doi: 10.5249/jivr.v16i1.1875.

本文引用的文献

1
"I'm Leaving": Factors That Impact Against Medical Advice Disposition Post-Trauma.
J Emerg Med. 2020 Apr;58(4):691-697. doi: 10.1016/j.jemermed.2019.12.023. Epub 2020 Mar 12.
2
The Prevalence of Chronic Diseases Among Current and Ex-Miners in the United States.
J Occup Environ Med. 2020 Mar;62(3):227-231. doi: 10.1097/JOM.0000000000001809.
3
Understanding why patients with substance use disorders leave the hospital against medical advice: A qualitative study.
Subst Abus. 2020;41(4):519-525. doi: 10.1080/08897077.2019.1671942. Epub 2019 Oct 22.
6
Patients Leaving Against Medical Advice-A National Survey.
Indian J Crit Care Med. 2019 Mar;23(3):143-148. doi: 10.5005/jp-journals-10071-23138.
7
Retrospective Evaluation of Patients Leaving against Medical Advice in aTertiary Care Teaching Hospital.
Indian J Crit Care Med. 2019 Mar;23(3):139-142. doi: 10.5005/jp-journals-10071-23137.
8
Predictors of discharge against medical advice in adult trauma patients.
Surgeon. 2020 Feb;18(1):12-18. doi: 10.1016/j.surge.2019.04.001. Epub 2019 May 2.
9
Retrospective Evaluation of Patients Who Leave against Medical Advice in a Tertiary Teaching Care Institute.
Indian J Crit Care Med. 2018 Aug;22(8):591-596. doi: 10.4103/ijccm.IJCCM_375_17.

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验