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

> **NIH NIH R01** · UNIVERSITY OF WASHINGTON · 2024 · $622,221

## Abstract

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...

## Key facts

- **NIH application ID:** 10798259
- **Project number:** 5R01MH123682-04
- **Recipient organization:** UNIVERSITY OF WASHINGTON
- **Principal Investigator:** Bradley Wagenaar
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $622,221
- **Award type:** 5
- **Project period:** 2021-05-10 → 2026-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10798259

## Citation

> US National Institutes of Health, RePORTER application 10798259, Systems Analysis and Improvement Approach to Optimize the Task-Shared Mental Health Treatment Cascade (SAIA-MH): A ClusterRandomized Trial (5R01MH123682-04). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10798259. Licensed CC0.

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