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首次因 COPD 住院后的医疗保健利用:基于“6W”多维护理轨迹模型的状态序列分析新方法。

Healthcare utilization after a first hospitalization for COPD: a new approach of State Sequence Analysis based on the '6W' multidimensional model of care trajectories.

机构信息

Groupe de recherche PRIMUS, Centre de recherche du Centre hospitalier universitaire de Sherbrooke (CRCHUS), 3001 12e avenue nord, Sherbrooke, QC, J1H 5N4, Canada.

Département de médecine de famille et de médecine d'urgence, Université de Sherbrooke, 3001 12e avenue nord, Sherbrooke, QC, J1H 5N4, Canada.

出版信息

BMC Health Serv Res. 2020 Mar 6;20(1):177. doi: 10.1186/s12913-020-5030-0.

DOI:10.1186/s12913-020-5030-0
PMID:32143702
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7059729/
Abstract

BACKGROUND

Published methods to describe and visualize Care Trajectories (CTs) as patterns of healthcare use are very sparse, often incomplete, and not intuitive for non-experts. Our objectives are to propose a typology of CTs one year after a first hospitalization for Chronic Obstructive Pulmonary Disease (COPD), and describe CT types and compare patients' characteristics for each CT type.

METHODS

This is an observational cohort study extracted from Quebec's medico-administrative data of patients aged 40 to 84 years hospitalized for COPD in 2013 (index date). The cohort included patients hospitalized for the first time over a 3-year period before the index date and who survived over the follow-up period. The CTs consisted of sequences of healthcare use (e.g. ED-hospital-home-GP-respiratory therapists, etc.) over a one-year period. The main variable was a CT typology, which was generated by a 'tailored' multidimensional State Sequence Analysis, based on the "6W" model of Care Trajectories. Three dimensions were considered: the care setting ("where"), the reason for consultation ("why"), and the speciality of care providers ("which"). Patients were grouped into specific CT types, which were compared in terms of care use attributes and patients' characteristics using the usual descriptive statistics.

RESULTS

The 2581 patients were grouped into five distinct and homogeneous CT types: Type 1 (n = 1351, 52.3%) and Type 2 (n = 748, 29.0%) with low healthcare and moderate healthcare use respectively; Type 3 (n = 216, 8.4%) with high healthcare use, mainly for respiratory reasons, with the highest number of urgent in-hospital days, seen by pulmonologists and respiratory therapists at primary care settings; Type 4 (n = 100, 3.9%) with high healthcare use, mainly cardiovascular, high ED visits, and mostly seen by nurses in community-based primary care; Type 5 (n = 166, 6.4%) with high healthcare use, high ED visits and non-urgent hospitalisations, and with consultations at outpatient clinics and primary care settings, mainly for other reasons than respiratory or cardiovascular. Patients in the 3 highest utilization CT types were older, and had more comorbidities and more severe condition at index hospitalization.

CONCLUSIONS

The proposed method allows for a better representation of the sequences of healthcare use in the real world, supporting data-driven decision making.

摘要

背景

描述和可视化护理轨迹(Care Trajectories,CTs)作为医疗保健使用模式的方法非常有限,通常不完整,并且非专业人士难以理解。我们的目标是提出一种 COPD 首次住院后一年的 CT 分类法,并描述 CT 类型并比较每种 CT 类型的患者特征。

方法

这是一项从魁北克省 2013 年 COPD 住院患者的医疗管理数据中提取的观察性队列研究。该队列包括在索引日期前 3 年内首次住院且在随访期间存活的患者。CTs 由一年内的医疗保健使用序列(例如,ED-医院-家庭-GP-呼吸治疗师等)组成。主要变量是一种 CT 分类法,它是通过基于“6W”护理轨迹模型的“量身定制”多维状态序列分析生成的。考虑了三个维度:护理场所(“何处”)、咨询原因(“为何”)和护理提供者的专业(“哪些”)。患者被分为特定的 CT 类型,使用常用的描述性统计方法比较护理使用属性和患者特征。

结果

2581 名患者分为五种不同且同质的 CT 类型:Type 1(n=1351,52.3%)和 Type 2(n=748,29.0%)分别为低医疗保健和中医疗保健使用率;Type 3(n=216,8.4%)高医疗保健使用率,主要是呼吸系统原因,住院天数最紧急,在初级保健环境中由肺病专家和呼吸治疗师治疗;Type 4(n=100,3.9%)高医疗保健使用率,主要是心血管疾病,急诊就诊次数多,主要由社区初级保健护士治疗;Type 5(n=166,6.4%)高医疗保健使用率,高急诊就诊次数和非紧急住院治疗,以及在门诊和初级保健环境就诊,主要是出于呼吸或心血管以外的其他原因。在使用医疗保健最多的 3 种 CT 类型中,患者年龄较大,在索引住院时合并症更多,病情更严重。

结论

提出的方法可以更好地表示现实世界中医疗保健使用的序列,支持数据驱动的决策。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6cff/7059729/817cc366a14a/12913_2020_5030_Fig7_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6cff/7059729/817cc366a14a/12913_2020_5030_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6cff/7059729/be4c774c923e/12913_2020_5030_Fig1_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6cff/7059729/1c7e46b4179e/12913_2020_5030_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6cff/7059729/ddb2a48b4824/12913_2020_5030_Fig6_HTML.jpg
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