Systems Analysis and Improvement Approach to Optimize the Task-Shared Mental Health Treatment Cascade (SAIA-MH): A ClusterRandomized Trial

优化任务共享心理健康治疗级联 (SAIA-MH) 的系统分析和改进方法：集群随机试验

• 批准号: 10208400
• 负责人: Bradley Wagenaar
• 金额: $ 66.45万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-05-10 至 2026-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10208400
• 关键词: Adherence; Advocate; Affect; Area; Attention; Caring; Climate; Clinical; Complex; Consolidated Framework for Implementation Research; Consultations; Data; Development; Diagnosis; Diagnostic; Drops; Effectiveness; Engineering; Equation; Facility Controls; Goals; HIV; Health; Health Personnel; Health Services Accessibility; Health system; Healthcare; Infant; Information Systems; Intervention; Knowledge; Learning; Link; Maintenance; Mental Health; Mental disorders; Modeling; Modification; Mother-to-child HIV transmission; Motion; Mozambique; Outcome; Outpatients; Pathway interactions; Patient-Focused Outcomes; Patients; Performance; Phase; Policies; Prevention; Procedures; Process; Provider; Psyche structure; Quality of Care; Reporting; Research Project Grants; Resources; Schizophrenia; Structure; Symptoms; System; Systems Analysis; Testing; Time; Treatment Step; Work; attentional control; budget impact; care systems; clinically significant; cost effectiveness; cost estimate; cost outcomes; design; disability; effective therapy; effectiveness testing; evidence base; follow-up; functional improvement; health care delivery; implementation determinants; implementation research; implementation strategy; improved; innovation; low and middle-income countries; markov model; medication compliance; microcosting; organizational structure; response; retention rate; scale up; success; tool

ABSTRACT Mental disorders are the leading cause of disability worldwide, yet treatment gaps exceed 90% in many low- and middle-income countries (LMICs). Significant progress is being made in access to mental healthcare through task-shifting to lower-level healthcare providers. However, while task-sharing may increase access to care, limited attention has been paid to assessing and optimizing quality of care across the mental health (MH) care cascade. An urgent need exists for evidence-based strategies to optimize the MH care cascade globally. Implementation strategies focused on one step in a care cascade can contribute to unintended system bottlenecks and quality of care issues. By contrast, the “Systems Analysis and Improvement Approach (SAIA)” is a multicomponent implementation strategy focused on optimizing quality across an entire care cascade. SAIA blends external/internal facilitation, enhanced local clinical consultation, and the creation of facility-level learning collaboratives with systems-engineering tools in a 5-step approach developed for task-shared providers. The 5 steps of SAIA include: (1) cascade analysis to visualize treatment cascade drop-offs and prioritize areas for system improvements; (2) process mapping to identify modifiable facility-level bottlenecks; (3) identification and implementation of modifications to improve system performance; (4) assessment of modification effects on the cascade; and (5) repeated analysis and improvement cycles. A previous trial established that the SAIA implementation strategy improved maternal ARV initiation and early infant diagnosis for the prevention of mother- to-child transmission HIV care cascade (R01HD075057; PI: Sherr). The SAIA implementation strategy has shown effectiveness for HIV cascade improvement, although no evidence exists on the effectiveness of SAIA applied to other complex treatment cascades – such as outpatient schizophrenia treatment. The present study aims to fill this knowledge gap by testing the following specific aims: Primary Aim 1: test the effectiveness of the SAIA-MH implementation strategy for schizophrenia cascade optimization using a cluster RCT and assess determinants of implementation success; Secondary Aim 1: test causal pathway models to analyze mechanisms of action for effects of the SAIA-MH implementation strategy; Aim 2: estimate the cost and cost-effectiveness of scaling-up SAIA-MH in Mozambique. In response to PAR-19-274, this project tests a multicomponent implementation strategy affecting “organizational structure, climate, culture, and processes”, with the goal to optimize the “implementation of diagnostic interventions, effective treatments, and clinical procedures into existing care”. This project also analyzes “mechanisms of action that explain the impact of a multi-component strategy to inform how these strategies can optimally be delivered across various settings”. If effective, the SAIA-MH implementation strategy has a large potential to be rapidly scaled-up to decrease gaps in task-shared MH treatments globally.

抽象的 精神障碍是全球障碍的主要原因，但在许多低 - 和 中等收入国家（LMIC）。通过 将任务转移到低级医疗保健提供者。但是，虽然任务共享可能会增加获得护理的机会，但 对整个心理健康（MH）护理评估和优化护理质量的关注有限 级联。迫切需要基于证据的策略来优化全球MH Care Cascade。 专注于护理级联一步的实施策略可以为意外系统做出贡献 瓶颈和护理质量。相比之下，“系统分析和改进方法（SAIA）” 是一种旨在在整个护理级联中优化质量的多组分实施策略。赛亚 混合外部/内部设施，增强的当地临床咨询以及设施级学习的创建 在为任务共享提供商开发的5步方法中与系统工程工具的合作。 5 SAIA的步骤包括：（1）级联分析以可视化处理级联下降并确定优先区域的级联分析 系统改进； （2）流程映射以识别可修改的设施级瓶颈； （3）识别和 实施修改以提高系统性能； （4）评估修饰对 级联; （5）重复分析和改进周期。先前的审判确定了Saia 实施策略改善了生物ARV倡议和早期婴儿诊断，以预防母亲 待命传播艾滋病毒护理级联（R01HD075057; pi：sherr）。 SAIA实施策略已显示出对HIV级联改善的有效性，尽管没有 有证据表明SAIA适用于其他复杂处理级联的有效性 - 例如门诊病人 精神分裂症治疗。本研究旨在通过测试以下特定目的来填补这一知识差距： 主要目的1：测试SAIA-MH实施策略的精神分裂症级联的有效性 使用群集RCT和评估确定实施成功的优化；次要目标1：测试 因果途径模型，以分析SAIA-MH实施策略影响的作用机制； 目标2：估计莫桑比克扩大SAIA-MH的成本和成本效益。 为了响应Par-19-274，该项目测试了影响的多组分实施策略 “组织结构，气候，文化和过程”，目的是优化“实施” 诊断干预措施，有效的治疗和临床程序对现有护理”。 分析“行动机制解释了多组分策略的影响，以告知这些策略 策略可以在各种环境中最佳地交付”。如果有效，SAIA-MH实施策略 具有快速扩大规模的巨大潜力，以减少全球任务共享MH处理的差距。