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Am J Public Health. 2015 Dec;105(12):2564-9. doi: 10.2105/AJPH.2015.302836. Epub 2015 Oct 16.
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A Systematic Review of Conceptual Frameworks of Medical Complexity and New Model Development.医学复杂性概念框架与新模型开发的系统评价
J Gen Intern Med. 2016 Mar;31(3):329-37. doi: 10.1007/s11606-015-3512-2. Epub 2015 Sep 30.
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Potential application of machine learning in health outcomes research and some statistical cautions.机器学习在健康结局研究中的潜在应用及一些统计学注意事项。
Value Health. 2015 Mar;18(2):137-40. doi: 10.1016/j.jval.2014.12.005. Epub 2015 Jan 29.
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Costs associated with ambulatory care sensitive conditions across hospital-based settings.基于医院环境下与非卧床护理敏感病症相关的费用。
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6
Discharge information and support for veterans Receiving Outpatient Care in the Emergency Department: study design and methods.为在急诊科接受门诊治疗的退伍军人提供出院信息与支持:研究设计与方法
Contemp Clin Trials. 2014 Nov;39(2):342-50. doi: 10.1016/j.cct.2014.10.008. Epub 2014 Nov 3.
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Case management for frequent users of the emergency department: study protocol of a randomised controlled trial.急诊科频繁使用者的病例管理:一项随机对照试验的研究方案
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Seminal quality prediction using data mining methods.使用数据挖掘方法进行精液质量预测。
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Frequent users of US emergency departments: characteristics and opportunities for intervention.美国急诊科的频繁使用者:特征及干预机会
Emerg Med J. 2014 Jul;31(7):526-532. doi: 10.1136/emermed-2013-202407. Epub 2014 Jan 28.
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Predicting risk of hospitalization or death among patients receiving primary care in the Veterans Health Administration.预测在退伍军人健康管理局接受初级保健的患者住院或死亡的风险。
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高危患者急诊复诊的风险分层。

Risk stratification for return emergency department visits among high-risk patients.

机构信息

VA Medical Center (152), 508 Fulton St, Durham, NC 27705. E-mail:

出版信息

Am J Manag Care. 2017 Aug 1;23(8):e275-e279.

PMID:29087151
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6415920/
Abstract

OBJECTIVES

To compare 2 methods of identifying patients at high-risk of repeat emergency department (ED) use: high Care Assessment Need (CAN) score (≥90), derived from a model using Veterans Health Administration (VHA) data, and "Super User" status, defined as more than 3 ED visits within 6 months of the index ED visit.

STUDY DESIGN

Retrospective cohort study.

METHODS

Using McNemar's test, we compared rates of high-risk classification between CAN score and Super User status. We examined differences in patient characteristics and healthcare utilization across 4 levels of risk classification: high CAN and Super User status (n = 198), CAN <90 and non-Super User (n = 622), high CAN and non-Super User (n = 616), or Super User and CAN score <90 (n = 106). We used logistic regression to identify associations between risk classification and any ED visit within 90 days.

RESULTS

Of 1542 veterans, 52.8% (n = 814) had a CAN score ≥90 and 19.7% (n = 304) were Super Users (P <.0001), indicating discrepant rates of high-risk classification. However, we found no differences in patient characteristics. Rates of subsequent ED use were high: 63.1% of patients had 1 or more ED visits. No levels of risk classification were associated with subsequent ED use within 90 days (P = .25).

CONCLUSIONS

Among the VHA users with multimorbidity and 3 or more prior ED visits or hospitalizations, subsequent ED use was high. Although CAN scores have demonstrated utility for predicting hospitalizations and deaths, prior utilization and multimorbidity without further risk classification identified a high-risk group for repeat ED use.

摘要

目的

比较两种识别急诊科(ED)再次使用高风险患者的方法:高护理评估需求(CAN)评分(≥90),该评分源自使用退伍军人健康管理局(VHA)数据的模型,以及“超级用户”状态,定义为在索引 ED 就诊后 6 个月内就诊 3 次以上。

研究设计

回顾性队列研究。

方法

使用 McNemar 检验比较 CAN 评分和超级用户状态之间的高危分类率。我们检查了 4 个风险分类水平的患者特征和医疗保健利用情况的差异:高 CAN 和超级用户状态(n=198)、CAN<90 和非超级用户(n=622)、高 CAN 和非超级用户(n=616)或超级用户和 CAN 评分<90(n=106)。我们使用逻辑回归来确定风险分类与 90 天内任何 ED 就诊之间的关联。

结果

在 1542 名退伍军人中,52.8%(n=814)的 CAN 评分≥90,19.7%(n=304)为超级用户(P<0.0001),表明高危分类率存在差异。然而,我们没有发现患者特征的差异。随后 ED 使用的发生率很高:63.1%的患者有 1 次或多次 ED 就诊。在 90 天内,没有任何风险分类水平与随后的 ED 使用相关(P=0.25)。

结论

在 VHA 使用者中,患有多种疾病且有 3 次以上的 ED 就诊或住院,随后 ED 的使用频率较高。尽管 CAN 评分已被证明可用于预测住院和死亡,但先前的使用情况和多种疾病而没有进一步的风险分类确定了一个 ED 再次使用的高风险群体。