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

优化任务转移心理健康诊断和治疗级联的系统分析和改进方法（SAIA-MH）

• 批准号: 9371841
• 负责人: Bradley Wagenaar
• 金额: $ 21.18万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9371841
• 关键词: Adoption; Advocate; Affect; Africa South of the Sahara; Area; Attention; Award; Awareness; Caring; Climate; Clinic; Clinical; Collaborations; Country; Data; Diagnosis; Diagnostic; Drops; Effectiveness; Effectiveness of Interventions; Engineering; Evaluation; Evidence based intervention; Evidence based treatment; Focus Groups; Foundations; Future; Goals; HIV; Health; Health Personnel; Health Professional; Health system; Healthcare; Impairment; Industrialization; Infant; Intervention; Interview; Investments; Measurement; Mental Health; Mental disorders; Methods; Modeling; Modification; Mother-to-child HIV transmission; Mozambique; Organizational Objectives; Outcome; Outpatients; Performance; Policies; Polypharmacy; Preparation; Prevention; Process; Protocols documentation; Provider; Psyche structure; Quality of Care; Reporting; Research Infrastructure; Research Project Grants; Resources; Schizophrenia; Science; Supervision; System; Systems Analysis; Testing; Time; Training; Universities; Washington; Work; control trial; cost effectiveness; design; diagnostic accuracy; disability; empowered; evidence base; evidence based guidelines; experience; flexibility; follow-up; functional improvement; health care delivery; health data; implementation research; improved; low and middle-income countries; medication compliance; member; novel; novel strategies; organizational structure; prototype; satisfaction; scale up; tool; usability

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.

抽象的 精神障碍是全世界残疾的主要原因，但在许多低收入和低收入国家，治疗差距超过 90% 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，基于证据的医疗服务之间存在着紧迫的质量差距 全球范围内的协议和心理健康服务系统。很少有研究描述和评估 高质量的全球精神保健服务所需的互动护理组件的性能。更远， 没有用于全球心理健康级联分析和质量改进的循证方案。 此前在莫桑比克开展的改善艾滋病毒母婴传播预防工作 (PMTCT) 护理级联打包系统工程工具采用 5 步方法，针对一线工人， 包括：(1) 级联分析，以可视化 PMTCT 级联下降情况并优先考虑系统区域 修改； (2) 绘制流程图，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) 改造效果评估 级联; (5) 反复分析和改进循环。最近的一项集群随机对照试验（“系统分析和 改进方法 (SAIA)”（R01HD075057，PI：Sherr））确定 SAIA 干预实质上 撒哈拉以南非洲三个研究国家的孕产妇抗逆转录病毒治疗和早期婴儿诊断有所增加， 并提供基于证据的灵活模型来优化中低收入国家的护理级联。该项目的目标 是采用基于证据的 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（SAIA-MH）。我们的具体目标是：（1）适应级联 向SAIA-MH分析指标和数据工具，并评估其在最终用户中的可用性和可行性；和 (2) 对整个 SAIA-MH 级联分析、流程的有效性、实用性和流程保真度进行试点测试 绘图以及相关的任务共享心理健康质量改善方法。联合公司 实施研究框架将指导实施结果衡量，以了解内部 影响 SAIA-MH 实施的环境、干预和流程结构。 该项目是系统工程干预的一个新颖应用——在以下方面已被证明是有效的： PMTCT – 全球心理健康系统优化。它利用了双方超过 25 年的合作 华盛顿大学和莫桑比克卫生部。该应用程序满足以下目标 PAR-16-236 中的实施研究，通过测试一种新的策略来提高采用和实施 在临床环境中进行基于证据的干预，使用系统干预来影响组织结构， 气候、文化和过程。该项目的设计重点是团队科学和参与度 最终用户的整个过程。如果成功，该奖项的信息将用于改进 SAIA-MH 为评估 SAIA-MH 有效性的更大规模集群对照试验做准备的实施策略。