Systems analysis and improvement approach to optimize task-shifted mental health diagnosis and treatment cascades (SAIA-MH)

摘要 精神障碍是全球残疾的主要原因，但在许多低收入和低收入国家， 中等收入国家（LMIC）。虽然在获得精神保健方面取得了重大进展， 通过将任务转移到较低级别的医疗保健提供者，基于证据的医疗保健提供者之间存在着紧迫的质量差距 协议和精神卫生提供系统。很少有研究描述和评估了 高质量的全球精神卫生保健服务所需的相互作用的护理组件的性能。此外，本发明还 目前还没有用于全球心理健康级联分析和质量改进的循证一揽子方案。 以前在莫桑比克开展的加强预防艾滋病毒母婴传播的工作 （预防母婴传播）护理级联成套系统工程工具，采用5步法，针对一线工作人员， 包括：（1）级联分析，以可视化PMTCT级联下降和系统优先领域 （2）流程映射，以确定可修改的设施级瓶颈;（3）识别和 实施修改，以消除系统瓶颈;（4）评估修改对 （5）反复分析和改进循环。最近的一项集群RCT（“系统分析和 改进方法（SAIA）”（R 01 HD 075057，PI：Sherr））确定SAIA干预实质上 在撒哈拉以南非洲的三个研究国家中，孕产妇抗逆转录病毒治疗开始和婴儿早期诊断增加， 并提供了一个以证据为基础的灵活模型，以优化中低收入国家的护理级联。本项目的目标 是调整基于证据的SAIA级联分析和优化工具/协议，并将其应用于 改善莫桑比克的精神卫生保健服务。我们的具体目标是：（1）适应级联 分析SAIA-MH的指标和数据工具，并评估其在最终用户中的可用性和可行性; (2)试点测试的有效性，实用性，和完整的SAIA-MH级联分析，过程的过程保真度 映射，以及相关的任务共享的心理健康质量改善方法。综合 实施研究框架将指导实施成果衡量，以了解内部 影响SAIA-MH实施的设置、干预和过程结构。 该项目是一个新的应用系统工程干预-与证明有效， PMTCT -全球精神卫生系统优化。它利用了25年的合作， 华盛顿大学和莫桑比克卫生部。此应用程序符合以下目标： PAR-16-236中的实施研究，通过测试一种新的策略来提高 在临床环境中使用系统干预来影响组织结构的循证干预， 气候、文化和过程。该项目旨在关注团队科学和参与 最终用户在整个过程中。如果成功，该奖项的信息将用于改进SAIA-MH 实施策略，为评估SAIA-MH有效性的更大规模的集群对照试验做准备。

ABSTRACT Mental disorders are the leading cause of disability worldwide, yet treatment gaps exceed 90% in many low- and middle-income countries (LMICs). While significant progress is being made in access to mental healthcare through task-shifting to lower-level healthcare providers, urgent gaps in quality exist between evidence-based protocols and mental health delivery systems globally. Few studies have characterized and assessed the performance of interacting care components needed for high-quality global mental healthcare delivery. Further, there are no evidence-based packages for global mental health cascade analysis and quality improvement. Previous work in Mozambique to improve the prevention of the mother-to-child HIV transmission of HIV (PMTCT) care cascade packaged systems engineering tools in a 5-step approach aimed at frontline workers, including: (1) cascade analysis to visualize PMTCT cascade drop-offs and prioritize areas for systems modifications; (2) process mapping to identify modifiable facility-level bottlenecks; (3) identification and implementation of modifications to eliminate system bottlenecks; (4) assessment of modification effects on the cascade; and (5) repeated analysis and improvement cycles. A recent cluster RCT (“Systems Analysis and Improvement Approach (SAIA)” (R01HD075057, PI: Sherr)) established that the SAIA intervention substantially increased maternal ARV initiation and early infant diagnosis across three study countries in sub-Saharan Africa, and provides an evidence-based, flexible model to optimize care cascades in LMICs. The objective of this project is to adapt the evidence-based SAIA cascade analysis and optimization tools/protocols and apply them to improving mental health care delivery in Mozambique (SAIA-MH). Our specific aims are to: (1) adapt cascade analysis indicators and data tools to SAIA-MH, and assess their usability and feasibility among end-users; and (2) pilot-test the effectiveness, practicality, and process fidelity of the full SAIA-MH cascade analysis, process mapping, and associated task-shared mental health quality improvement approaches. The Consolidated Framework for Implementation Research will guide implementation outcome measurement to understand inner setting, intervention, and process constructs affecting SAIA-MH implementation. This project is a novel application of a systems engineering intervention – with proven effectiveness in PMTCT – to global mental health systems optimization. It leverages over 25 years of collaboration between the University of Washington and the Ministry of Health in Mozambique. This application meets the goals of implementation research in PAR-16-236 by testing a novel strategy to improve adoption and implementation of evidence-based interventions in clinical settings using a systems intervention to impact organizational structure, climate, culture, and processes. This project has been designed to focus on team science and the engagement of end users throughout the process. If successful, information from this award will be used to improve SAIA-MH implementation strategies in preparation for a larger cluster-controlled trial assessing SAIA-MH effectiveness.