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
Garima Jain, Anand Bodade, Sanghamitra Pati
TL;DR
This protocol addresses evaluating the impact of clinical decision support systems (CDSS) in LMICs by specifying a PRISMA-guided plan for searching, selecting, and synthesizing comparative quantitative studies. It details risk-of-bias assessment using RoB 2 and ROBINS-I, standardized data extraction, and a mixed synthesis strategy that includes random-effects meta-analysis when feasible and narrative synthesis otherwise, with GRADE-based assessment of evidence quality. The approach includes subgroup and sensitivity analyses to handle heterogeneity and aims to generate actionable insights for policy and practice in resource-limited settings. By consolidating LMIC-specific evidence, the study intends to clarify CDSS effectiveness, inform scalable implementations, and identify barriers to adoption in diverse health 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).