Suppr超能文献

比较标准与强化实施干预措施以提高有效重新参与计划在严重精神疾病患者中的应用率的聚类随机适应性实施试验。

Cluster randomized adaptive implementation trial comparing a standard versus enhanced implementation intervention to improve uptake of an effective re-engagement program for patients with serious mental illness.

机构信息

VA Center for Clinical Management Research, VA Ann Arbor Healthcare System, 2215 Fuller Road, Mailstop 152, Ann Arbor, MI 48105, USA.

出版信息

Implement Sci. 2013 Nov 20;8:136. doi: 10.1186/1748-5908-8-136.

Abstract

BACKGROUND

Persons with serious mental illness (SMI) are disproportionately burdened by premature mortality. This disparity is exacerbated by poor continuity of care with the health system. The Veterans Health Administration (VA) developed Re-Engage, an effective population-based outreach program to identify veterans with SMI lost to care and to reconnect them with VA services. However, such programs often encounter barriers getting implemented into routine care. Adaptive designs are needed when the implementation intervention requires augmentation within sites that do not initially respond to an initial implementation intervention. This protocol describes the methods used in an adaptive implementation design study that aims to compare the effectiveness of a standard implementation strategy (Replicating Effective Programs, or REP) with REP enhanced with External Facilitation (enhanced REP) to promote the uptake of Re-Engage.

METHODS/DESIGN: This study employs a four-phase, two-arm, longitudinal, clustered randomized trial design. VA sites (n = 158) across the United States with a designated Re-Engage provider, at least one Veteran with SMI lost to care, and who received standard REP during a six-month run-in phase. Subsequently, 88 sites with inadequate uptake were stratified at the cluster level by geographic region (n = 4) and VA regional service network (n = 20) and randomized to REP (n = 49) vs. enhanced REP (n = 39) in phase two. The primary outcome was the percentage of veterans on each facility outreach list documented on an electronic web registry. The intervention was at the site and network level and consisted of standard REP versus REP enhanced by external phone facilitation consults. At 12 months, enhanced REP sites returned to standard REP and 36 sites with inadequate participation received enhanced REP for six months in phase three. Secondary implementation outcomes included the percentage of veterans contacted directly by site providers and the percentage re-engaged in VA health services.

DISCUSSION

Adaptive implementation designs consisting of a sequence of decision rules that are tailored based on a site's uptake of an effective program may produce more relevant, rapid, and generalizable results by more quickly validating or rejecting new implementation strategies, thus enhancing the efficiency and sustainability of implementation research and potentially leading to the rollout of more cost-efficient implementation strategies.

TRIAL REGISTRATION

Current Controlled Trials ISRCTN21059161.

摘要

背景

患有严重精神疾病(SMI)的人过早死亡的比例不成比例。这种差异因与卫生系统的护理连续性差而加剧。退伍军人健康管理局(VA)开发了 Re-Engage,这是一种有效的基于人群的外展计划,旨在确定与 VA 服务失去联系的患有 SMI 的退伍军人,并重新与 VA 服务联系起来。然而,当实施干预措施需要在最初对初始实施干预措施没有反应的站点中进行增强时,此类计划通常会遇到实施障碍。当实施干预措施需要增强时,需要自适应设计,而站点最初没有响应初始实施干预措施。本方案描述了在适应性实施设计研究中使用的方法,该研究旨在比较标准实施策略(复制有效计划或 REP)与增强外部促进的 REP(增强 REP)对促进 Re-Engage 采用的有效性。

方法/设计:本研究采用四阶段、双臂、纵向、聚类随机试验设计。在美国,有指定的 Re-Engage 提供者的 VA 站点(n = 158),至少有一名患有 SMI 的退伍军人失去联系,并在六个月的试行阶段接受标准 REP。随后,在聚类水平上按地理区域(n = 4)和 VA 区域服务网络(n = 20)对 88 个吸收不足的站点进行分层,并在第二阶段随机分配到 REP(n = 49)或增强 REP(n = 39)。主要结局是在电子网络注册表上记录的每个设施外展名单上的退伍军人百分比。干预措施是在站点和网络层面进行的,包括标准 REP 与通过外部电话促进咨询增强的 REP。在 12 个月时,增强 REP 站点恢复为标准 REP,36 个参与不足的站点在第三阶段接受增强 REP 六个月。次要实施结果包括站点提供者直接联系的退伍军人百分比和重新参与 VA 卫生服务的百分比。

讨论

由基于站点对有效计划的吸收情况定制的一系列决策规则组成的适应性实施设计,通过更快地验证或拒绝新的实施策略,可能会产生更相关、更快速和更具普遍性的结果,从而提高实施研究的效率和可持续性,并可能导致推出更具成本效益的实施策略。

试验注册

当前对照试验 ISRCTN21059161。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ba65/3874628/7f9aab66109b/1748-5908-8-136-1.jpg

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