Suppr超能文献

退伍军人事务初级保健诊所中未按时就诊患者的概况。

A Profile of Patients Who Fail to Keep Appointments in a Veterans Affairs Primary Care Clinic.

作者信息

Boos Elizabeth M, Bittner Marvin J, Kramer Michael R

出版信息

WMJ. 2016 Aug;115(4):185-90.

Abstract

BACKGROUND

Missed medical appointments (“no-shows”) affect both staff and other patients who are unable to make timely appointments. No-shows can be prevented through interventions that target those most at risk to miss appointments. Young age, low socioeconomic status, a history of missed appointments, psychosocial problems, and longer wait times are some predictors that previously have been associated with higher no-show rates.

OBJECTIVE

To determine predictors for outpatient appointment no-shows in primary care clinics of the Veterans Affairs Nebraska-Western Iowa Health Care System.

METHODS

The study included 69,908 noncancelled primary care appointments between January 1, 2012 and December 31, 2013 among patients residing in ZIP codes within the Veterans Affairs Nebraska-Western Iowa Health Care System Service Area. Age, sex, race, presence of a mental health diagnosis, previous no-show rate in the past 2 years, appointment wait time, distance to clinic, and neighborhood deprivation index were extracted or measured for each patient.

RESULTS

In log-binomial models accounting for clustering by ZIP code, the strongest predictors of no-shows were age between 20 and 39 (OR compared to 60+: 3.87, 95% CI, 3.48-4.31) or between 40 and 59 (OR compared to 60+: 2.23, 95% CI, 2.05-2.43), black (OR compared to white: 2.14, 95% CI, 1.98-2.31) or other nonwhite race (OR compared to white: 1.35, 95% CI, 1.17- 1.56), male sex (OR compared to female: 1.30, 95% CI, 1.16-1.45), and presence versus absence of mental health diagnosis (OR: 1.16, 95% CI, 1.09-1.24).

CONCLUSION

These findings show that individuals who are younger, nonwhite, male, or have been diagnosed with mental health issues are more likely to no-show. Interventions to improve compliance could be targeted at these individuals in order to decrease the burden of no-shows on health care systems.

摘要

背景

医疗预约爽约(“未就诊”)会影响工作人员以及其他无法及时预约的患者。通过针对最有可能爽约的人群采取干预措施,可以预防未就诊情况的发生。年轻、社会经济地位低、有过爽约记录、存在心理社会问题以及等待时间较长等都是此前与较高爽约率相关的一些预测因素。

目的

确定内布拉斯加-爱荷华西部退伍军人事务医疗系统基层医疗诊所门诊预约未就诊的预测因素。

方法

该研究纳入了2012年1月1日至2013年12月31日期间居住在内布拉斯加-爱荷华西部退伍军人事务医疗系统服务区邮政编码范围内的患者的69908次未取消的基层医疗预约。提取或测量了每位患者的年龄、性别、种族、是否有心理健康诊断、过去2年的既往爽约率、预约等待时间、到诊所的距离以及社区贫困指数。

结果

在考虑邮政编码聚类的对数二项式模型中,未就诊的最强预测因素为年龄在20至39岁之间(与年龄60岁及以上相比,比值比:3.87,95%置信区间,3.48 - 4.31)或40至59岁之间(与年龄60岁及以上相比,比值比:2.23,95%置信区间,2.05 - 2.43)、黑人(与白人相比,比值比:2.14,95%置信区间,1.98 - 2.31)或其他非白人种族(与白人相比,比值比:1.35,95%置信区间,1.17 - 1.56)、男性(与女性相比,比值比:1.30,95%置信区间,1.16 - 1.45)以及有无心理健康诊断(比值比:1.16,95%置信区间,1.09 - 1.24)。

结论

这些研究结果表明,年龄较小、非白人、男性或被诊断患有心理健康问题的个体更有可能爽约。为减轻未就诊对医疗系统的负担,可针对这些个体采取提高依从性的干预措施。

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