Effectiveness of Code Stroke Activation Models on Clinical Decision-Making Quality: A Scoping Review - Purwandi, Purwandi; T, Pahria,; H., Pratiwi, S.; H., Fauzan, N. (Chakra Brahmanda Lentera Institute, 2026)
Background: Traditional medicine is widely used worldwide, particularly in rural communities of developing countries. Cultural beliefs, cost, accessibility, and social factors influence its use. This study aims to identify factors associated with the use of traditional medicine for Ear, Nose, and Throat (ENT) conditions among patients in Kumasi. Methods: This scoping review was conducted in accordance with PRISMA-ScR guidelines and used the PCC (Population, Concept, Context) framework and the Arksey & O’Malley approach. A systematic literature search was conducted in the PubMed, ScienceDirect, Scopus, and EBSCOhost databases for the period 2021–2026. Inclusion criteria included studies on the Code Stroke activation model in the prehospital and ED settings. In contrast, exclusion criteria included in-hospital activation in the ICU or wards without ED involvement. The screening phase was conducted independently by four reviewers using the PRISMA protocol. Data were mapped through a documentation process using the JBI critical appraisal tools, and data synthesis was performed via thematic analysis to group activation models. Results: Of the 1,690 identified records, the literature was found to be dominated by single-center studies or those conducted in high-income countries with advanced technological infrastructure. In contrast, evidence regarding the effectiveness of these activation models in Low- and Middle-Income Countries (LMICs) and the specific context of Indonesia remains very limited. The synthesis results identified four main themes of activation models: (1) nurse-based activation, (2) AI-based models (such as automated LVO detection), (3) pre-hospital activation via ambulance notifications, and (4) workflow optimization through a “pit-crew” model that transforms sequential processes into simultaneous parallel ones. Although these models have proven effective at accelerating time metrics such as door-to-needle time, a research gap remains: a lack of direct comparative analysis between models in resource-limited settings. Conclusion: The integration of nurse leadership, parallel task delegation, and advanced technological support is critical to minimizing permanent neurological damage. Practice and policy implications include the need to implement a “no-fault” activation system for triage nurses and standardize AI reporting. Future research should focus on validating these models in geographically challenging contexts, such as in Indonesia, and conducting multicenter comparative studies to determine the optimal balance between diagnostic accuracy and hospital resource availability.