Implementing robotic-assisted therapy in stroke rehabilitation: a structured narrative review of evidence and technology adoption barriers in Indonesia

IntroductionRobotic-assisted therapy (RAT) has emerged as a promising intervention for post-stroke recovery; however, its implementation in developing countries such as Indonesia remains limited by economic, technical, and systemic barriers. This structured narrative review summarizes clinical effect reported by systematics review and meta-analyses and examines implementation consideration and potential economic feasibility within the Indonesian healthcare context.MethodA structured review of systematic reviews and meta-analyses (2015–2025) was conducted using PubMed, Scopus, PEDro, and Cochrane Library. Studies involving adult with stroke were included. Findings were synthesized narratively because of heterogeneity. Implementation barriers were analyzed, and a scenario-based economic model was developed to assess feasibility of RAT in Indonesia. QALY-equivalent outcomes (QEOs) were estimated using ratio-based value-transfer approach that scaled published reference values according to observed functional effects.ResultEvidence from 22 systematic reviews indicated that RAT improved functional outcomes compared with conventional therapy, particularly gait and upper-limb function (effect sizes: 0.02–0.52). Key implementation barriers included limited device availability, inadequate workforce training, and regulatory constraints. Modelled ICERs ranged from US$236.23–752.31 per QEO for upper-limb rehabilitation and US$558.86–4,890 per QEO for gait-related rehabilitation. Most estimates remained below the Indonesian willingness-to-pay threshold (<US$5,030), suggesting a favourable economic profile under model assumptions, particularly in higher-resource provinces, while lower-income regions may face affordability constraints.ConclusionRAT shows potential clinical benefits, but implementation in Indonesia should consider clinical effectiveness, economic feasibility, infrastructure, workforce, technology acceptance, and reimbursement. A phased approach in suitable tertiary-care settings may support wider adoption based on local evidence.