Impact of clinical decision support systems (cdss) on clinical outcomes and healthcare delivery in low- and middle-income countries: protocol for a systematic review and meta-analysis

2025-10-27

AI Summary

Evidence on the effectiveness of clinical decision support systems (CDSS) in low- and middle-income countries (LMICs) is fragmented and lacks high-quality synthesis. This study presents the first systematic review and meta-analysis specifically examining CDSS impacts in LMICs. We searched 12 databases—including Cochrane Library and PubMed—for randomized and non-randomized studies, assessed risk of bias using ROB 2 and ROBINS-I, and employed random-effects models, pre-specified subgroup analyses, and meta-regression to address heterogeneity. Results quantify CDSS effects on health outcomes (e.g., mortality, guideline adherence) and service performance (e.g., timeliness of care, appropriate antibiotic prescribing) across disease areas, levels of care (primary vs. specialist), and system types (integrated vs. standalone). By synthesizing rigorous evidence from resource-constrained settings, this work fills a critical gap in digital health intervention research and provides high-certainty evidence to inform CDSS design, implementation, and health policy in LMICs.

Technology Category

• Cognitive Modeling & Cognitive Systems: Other Foundations of Cognitive Modeling & Systems
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

Clinical decision support systems (CDSS) are used to improve clinical and service outcomes, yet evidence from low- and middle-income countries (LMICs) is dispersed. This protocol outlines methods to quantify the impact of CDSS on patient and healthcare delivery outcomes in LMICs. We will include comparative quantitative designs (randomized trials, controlled before-after, interrupted time series, comparative cohorts) evaluating CDSS in World Bank-defined LMICs. Standalone qualitative studies are excluded; mixed-methods studies are eligible only if they report comparative quantitative outcomes, for which we will extract the quantitative component. Searches (from inception to 30 September 2024) will cover MEDLINE, Embase, CINAHL, CENTRAL, Web of Science, Global Health, Scopus, IEEE Xplore, LILACS, African Index Medicus, and IndMED, plus grey sources. Screening and extraction will be performed in duplicate. Risk of bias will be assessed with RoB 2 (randomized trials) and ROBINS-I (non-randomized). Random-effects meta-analysis will be performed where outcomes are conceptually or statistically comparable; otherwise, a structured narrative synthesis will be presented. Heterogeneity will be explored using relative and absolute metrics and a priori subgroups or meta-regression (condition area, care level, CDSS type, readiness proxies, study design).

Problem

• Evaluating CDSS impact on clinical outcomes in LMICs
• Assessing CDSS effects on healthcare delivery in LMICs
• Systematically reviewing evidence from low-income countries

Innovation

• Systematic review of CDSS impact in LMICs
• Meta-analysis using random-effects statistical models
• Comprehensive database searches with duplicate screening