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一项关于调查非计划再入院模式的研究。

A study on investigating unplanned readmission patterns.

作者信息

Chan Moon Fai, Wong Frances Kam Yuet, Chang Katherine Ka Pik, Chow Susan Ka Yee, Chung Loretta Yuet Foon, Lee Rance Pui-Leung, Lee Wai Man

机构信息

School of Nursing, The Hong Kong Polytechnic University, Hong Kong SAR, China.

出版信息

J Clin Nurs. 2008 Aug;17(16):2164-73. doi: 10.1111/j.1365-2702.2006.01916.x. Epub 2007 Apr 5.

Abstract

AIM

To explain frequent hospital readmissions, this study aimed to determine whether definable subtypes exist within a cohort of subjects with chronic illness with regard to factors associated with a patient's readmission patterns and to compare whether these factors vary between subjects in groups with different profiles.

RESEARCH METHOD

A descriptive correlational survey was conducted and data were collected by using a structured questionnaire. Seventy-four readmitted subjects were recruited in three general hospitals in Hong Kong.

OUTCOME MEASURES

Five outcome variables were employed in the study: predisposing characteristic, need factors, health behaviour, health status or outcomes and enabling resources.

RESULTS

A cluster analysis yielded two clusters. Each cluster represented a different profile of the sample on patient use of healthcare services. Cluster A consisted of 41.9% (n = 31) and Cluster B consisted of 58.1% (n = 43) of the patients. Cluster A patients, more of whom were male, were younger, more educated, had higher activities of daily living scores and fewer of them had received community nurse services than patients of Cluster B. Cluster A patients (32.3%) had more than one readmission record within 28 days than Cluster B patients (9.3%, p = 0.017).

CONCLUSION

Our study shows that community nurse services can reduce the rate at which they are readmitted a second time. However, such services may have a positive effect only on a group of patients whose profile is similar to the patients in Cluster B and not for patients such as those in Cluster A. A clear profile may help healthcare policy makers make appropriate strategies to target a specific group of patients to reduce their readmission rates.

RELEVANCE TO CLINICAL PRACTICE

The identification of risk for future healthcare use could enable better targeting of interventional strategies within these groups. The results of this study might provide hospital managers with a model to design specified interventions to reduce unplanned hospital readmissions for each profile group.

摘要

目的

为解释频繁的医院再入院情况,本研究旨在确定在患有慢性疾病的受试者队列中,就与患者再入院模式相关的因素而言,是否存在可定义的亚型,并比较这些因素在具有不同特征的组中的受试者之间是否存在差异。

研究方法

进行了一项描述性相关性调查,并使用结构化问卷收集数据。在香港的三家综合医院招募了74名再入院受试者。

结果指标

本研究采用了五个结果变量:易患特征、需求因素、健康行为、健康状况或结果以及促成资源。

结果

聚类分析产生了两个聚类。每个聚类代表了样本在患者使用医疗服务方面的不同特征。聚类A占患者的41.9%(n = 31),聚类B占58.1%(n = 43)。聚类A的患者中男性更多,更年轻,受教育程度更高,日常生活活动得分更高,且接受社区护士服务的人数比聚类B的患者少。聚类A的患者(32.3%)在28天内有不止一次再入院记录的比例高于聚类B的患者(9.3%,p = 0.017)。

结论

我们的研究表明,社区护士服务可以降低再次入院的比率。然而,此类服务可能仅对一组特征与聚类B中的患者相似的患者有积极影响,而对聚类A中的患者则不然。明确的特征可能有助于医疗保健政策制定者制定适当的策略,以针对特定患者群体降低其再入院率。

与临床实践的相关性

识别未来医疗保健使用的风险可以使这些群体中的干预策略更具针对性。本研究结果可能为医院管理人员提供一个模型来设计特定干预措施,以减少每个特征组的计划外医院再入院情况。

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