Effectiveness of Code Stroke Activation Models on Clinical Decision-Making Quality: A Scoping Review
DOI:
https://doi.org/10.55018/janh.v8i2.635Keywords:
Stroke, Code Stroke, Clinical Decision-Making, Emergency Service, Quality of Health Care, ReviewAbstract
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.
Downloads
References
Adams, R. J., Smart, P., & Huff, A. S. (2017). Shades of Grey: Guidelines for Working with the Grey Literature in Systematic Reviews for Management and Organizational Studies. International Journal of Management Reviews, 19(4), 432–454. https://doi.org/10.1111/ijmr.12102
Akay, E. M. Z., Hilbert, A., Carlisle, B. G., Madai, V. I., Mutke, M. A., & Frey, D. (2023). Artificial Intelligence for Clinical Decision Support in Acute Ischemic Stroke: A Systematic Review. Stroke, 54(6), 1505–1516. https://doi.org/10.1161/STROKEAHA.122.041442
Alijanpour, S., Bahramnezhad, F., Mowla, A., Sabet, M. S., & Nayeri, N. D. (2025). Updated Protocol for Stroke Code Management in Prehospital Settings: The Iranian Comprehensive Stroke Code Management Program (ICSCM Phase II). Archives of Academic Emergency Medicine, 13(1), 1–16. https://doi.org/10.22037/aaemj.v13i1.2633
Antunes, R., Costeira, C., Pereira Sousa, J., & Santos, C. (2024). The Contribution of the Triage Nurse in the Optimisation of Door-to-Computed-Tomography Time in Stroke. Nursing Reports, 14(3), 1769–1780. https://doi.org/10.3390/nursrep14030131
Arksey, H., & O’Malley, L. (2005). Scoping studies: Towards a methodological framework. International Journal of Social Research Methodology: Theory and Practice, 8(1), 19–32. https://doi.org/10.1080/1364557032000119616
Astasio-Picado, Á., Jurado-Palomo, J., & Rodriguez-Urbaneja, C. F. (2025). Impact of Stroke Code Activation on Functional Outcomes and the Role of Nursing in Neurorehabilitation: A Systematic Review. Neurology International, 17(11), 175. https://doi.org/10.3390/neurolint17110175
Baldereschi, M., Piccardi, B., Di Carlo, A., Lucente, G., Guidetti, D., Consoli, D., Provinciali, L., Toni, D., Sacchetti, M. L., Polizzi, B. M., & Inzitari, D. (2012). Relevance of prehospital stroke code activation for acute treatment measures in stroke care: A review. Cerebrovascular Diseases, 34(3), 182–190. https://doi.org/10.1159/000341856
Baydoun, A., Jia, A. Y., Zaorsky, N. G., Kashani, R., Rao, S., Shoag, J. E., Vince, R. A., Bittencourt, L. K., Zuhour, R., Price, A. T., Arsenault, T. H., & Spratt, D. E. (2024). Artificial intelligence applications in prostate cancer. Prostate Cancer and Prostatic Diseases, 27(1), 37–45. https://doi.org/10.1038/s41391-023-00684-0
Bentley, S., Feldman, N., Boehm, L., Zavala, M., Dilos, B., McIndoe, M., Nagaswar, L., Walker, K., Bell, D., Nazarian, D., Rabinovich, J., Kessler, S., Iavicoli, L., Fairweather, P., Farraye, J., & Shoirah, H. (2021). Stroke Code From EMS to Thrombectomy: An Interdisciplinary In Situ Simulation for Prompt Management of Acute Ischemic Stroke. MedEdPORTAL : The Journal of Teaching and Learning Resources, 17, 11177. https://doi.org/10.15766/mep_2374-8265.11177
Buleu, F., Popa, D., Williams, C., Tudor, A., Sutoi, D., Trebuian, C., Ioan, C. C., Iancu, A., Cozma, G., Marin, A. M., Pah, A. M., Petre, I., & Mederle, O. A. (2024). Code Stroke Alert: Focus on Emergency Department Time Targets and Impact on Door-to-Needle Time across Day and Night Shifts. Journal of Personalized Medicine, 14(6). https://doi.org/10.3390/jpm14060596
Buus, N., Nygaard, L., Berring, L. L., Hybholt, L., Kamionka, S. L., Rossen, C. B., Søndergaard, R., & Juel, A. (2022). Arksey and O′Malleyʼs consultation exercise in scoping reviews: A critical review. Journal of Advanced Nursing, 78(8), 2304–2312. https://doi.org/10.1111/jan.15265
Correia, P. N., Roy, J., Haumesser, C., Touma, L., Jacquin, G., Poppe, A. Y., Deschaintre, Y., & Carpentier, A. V. (2025). Extended Delays in Recognition of Stroke Symptoms and Stroke Code Activation for In-Hospital Strokes: The DELAY Study. Canadian Journal of Neurological Sciences, 52(3), 502–509. https://doi.org/10.1017/cjn.2024.304
Ebker-White, A., Dinh, M., Paver, I., Bein, K., Tastula, K., Gattellari, M., & Worthington, J. (2022). Evaluating Stroke Code Activation Pathway in Emergency Departments study. EMA - Emergency Medicine Australasia, 34(6), 976–983. https://doi.org/10.1111/1742-6723.14032
Esteva, A., Feng, J., van der Wal, D., Huang, S. C., Simko, J. P., DeVries, S., Chen, E., Schaeffer, E. M., Morgan, T. M., Sun, Y., Ghorbani, A., Naik, N., Nathawani, D., Socher, R., Michalski, J. M., Roach, M., Pisansky, T. M., Monson, J. M., Naz, F., … Mohamad, O. (2022). Prostate cancer therapy personalization via multi-modal deep learning on randomized phase III clinical trials. Npj Digital Medicine, 5(1). https://doi.org/10.1038/s41746-022-00613-w
Feigin, V. L., Stark, B. A., Johnson, C. O., Roth, G. A., Bisignano, C., Abady, G. G., Abbasifard, M., Abbasi-Kangevari, M., Abd-Allah, F., Abedi, V., Abualhasan, A., Abu-Rmeileh, N. M. E., Abushouk, A. I., Adebayo, O. M., Agarwal, G., Agasthi, P., Ahinkorah, B. O., Ahmad, S., Ahmadi, S., … Murray, C. J. L. (2021). Global, regional, and national burden of stroke and its risk factors, 1990-2019: A systematic analysis for the Global Burden of Disease Study 2019. The Lancet Neurology, 20(10), 1–26. https://doi.org/10.1016/S1474-4422(21)00252-0
Fowler, M., Hafen, H., Aucoin, M. D., Chatwin, S., & Johnson, P. (2024). Abstract WP38: Nurse-Driven Emergency Evaluation Improves Timely Acute Stroke Response. Stroke, 55(Suppl_1). https://doi.org/10.1161/str.55.suppl_1.wp38
Hasan, T. F., Hasan, H., & Kelley, R. E. (2021). Overview of acute ischemic stroke evaluation and management. Biomedicines, 9(10). https://doi.org/10.3390/biomedicines9101486
Hunaifi, I., Suryani, D., Rizki, M., Yudhanto, D., Cahyawati, T. D., Harahap, H. S., & Sahidu, M. G. (2023). The PENINGKATAN KAPASITAS PELAYANAN PENANGANAN STROKE ISKEMIK AKUT MELALUI PELATIHAN CODE STROKE DI RS UNIVERSITAS MATARAM. Jurnal Abdi Insani, 10(3), 1371–1380. https://doi.org/10.29303/abdiinsani.v10i3.1029
Jamtli, B., Svendsen, E. J., Jørgensen, T. M., Kramer-Johansen, J., Hov, M. R., & Hardeland, C. (2024). Factors affecting emergency medical dispatchers decision making in stroke calls – a qualitative study. BMC Emergency Medicine, 24(1). https://doi.org/10.1186/s12873-024-01129-0
Janssen, P. M., Venema, E., & DIppel, Di. W. J. (2019). Effect of workflow improvements in endovascular stroke treatment: A systematic review and meta-analysis. Stroke, 50(3), 665–674. https://doi.org/10.1161/STROKEAHA.118.021633
Joanna Briggs Institute. (2024). JBI Manual for Evidence Synthesis. In E. Aromataris, C. Lockwood, K. Porritt, B. Pilla, & Z. Jordan (Eds.), JBI Manual for Evidence Synthesis. JBI; 2024. https://doi.org/10.46658/jbimes-24-01
Kamal, N., Smith, E. E., Jeerakathil, T., & Hill, M. D. (2018). Thrombolysis: Improving door-to-needle times for ischemic stroke treatment – A narrative review. International Journal of Stroke, 13(3), 268–276. https://doi.org/10.1177/1747493017743060
Kementerian Kesehatan Republik Indonesia, (KEMENKES). (2018). Laporan Nasional Riset Kesehatan Dasar. In Laporan Nasional Riskesdas 2018. Lembaga Penerbit Badan Penelitian dan Pengembangan Kesehatan. http://www.yankes.kemkes.go.id/assets/downloads/PMK No. 57 Tahun 2013 tentang PTRM.pdf
Kitchenham, B., Madeyski, L., & Budgen, D. (2023). How Should Software Engineering Secondary Studies Include Grey Material? IEEE Transactions on Software Engineering, 49(2), 872–882. https://doi.org/10.1109/TSE.2022.3165938
Koca, G., Kumar, M., Gubitz, G., & Kamal, N. (2023). Optimizing acute stroke treatment process: insights from sub-tasks durations in a prospective observational time and motion study. Frontiers in Neurology, 14. https://doi.org/10.3389/fneur.2023.1253065
Kuczynski, A. M., Freeman, W. D., Mooney, L. H., Huang, J. F., Demchuk, A. M., & Khosravani, H. (2025). The In-Hospital Code Stroke: A Look Back and the Road Ahead. Neurohospitalist , 15(2), 124–132. https://doi.org/10.1177/19418744241298035
Liang, X., Gao, W., Xu, J., Saymuah, S., Wang, X., Wang, J., Zhao, W., Xing, X., Wang, C., Liu, F., Feng, L., & Li, S. (2022). Triage Nurse-Activated Emergency Evaluation Reduced Door-to-Needle Time in Acute Ischemic Stroke Patients Treated with Intravenous Thrombolysis. Evidence-Based Complementary and Alternative Medicine, 2022, 9199856. https://doi.org/10.1155/2022/9199856
Liao, S., Qian, G., Huang, H., & Fu, H. (2025). Global trends and future projections of migraine burden in children aged 5 to 14 from 1990 to 2050. Frontiers in Neurology, 16, 1641599. https://doi.org/10.3389/fneur.2025.1641599
Martinez-Gutierrez, J. C., Kim, Y., Salazar-Marioni, S., Tariq, M. B., Abdelkhaleq, R., Niktabe, A., Ballekere, A. N., Iyyangar, A. S., Le, M., Azeem, H., Miller, C. C., Tyson, J. E., Shaw, S., Smith, P., Cowan, M., Gonzales, I., McCullough, L. D., Barreto, A. D., Giancardo, L., & Sheth, S. A. (2023). Automated Large Vessel Occlusion Detection Software and Thrombectomy Treatment Times A Cluster Randomized Clinical Trial. JAMA Neurology, 80(11), 1182–1190. https://doi.org/10.1001/jamaneurol.2023.3206
Mehdizadehfar, E., Sadeghi Hokmabadi, E., Taheraghdam, A., Sadeghpour, Y., Khabbaz, A., Hosseinzadeh, N., Mehdizadeh, R., Ala, A., Shams Vahdati, S., Jafari-Rouhi, A., Mohammadzadeh, N., Rostami, R., Farhoudi, M., & Rahnemayan, S. (2024). Optimizing Acute Ischemic Stroke Care: Evaluating Emergency Stroke Code Activation and Thrombolytic Therapy in East Azerbaijan Province. Archives of Neuroscience, 11(4), 10 5812--147803. https://doi.org/10.5812/ans-147803
Morey, J. R., Zhang, X., Yaeger, K. A., Fiano, E., Marayati, N. F., Kellner, C. P., De Leacy, R. A., Doshi, A., Tuhrim, S., & Fifi, J. T. (2021). Real-World Experience with Artificial Intelligence-Based Triage in Transferred Large Vessel Occlusion Stroke Patients. Cerebrovascular Diseases, 50(4), 450–455. https://doi.org/10.1159/000515320
Nussbaumer-Streit, B., Klerings, I., Dobrescu, A. I., Persad, E., Stevens, A., Garritty, C., Kamel, C., Affengruber, L., King, V. J., & Gartlehner, G. (2020). Excluding non-English publications from evidence-syntheses did not change conclusions: a meta-epidemiological study. Journal of Clinical Epidemiology, 118, 42–54. https://doi.org/10.1016/j.jclinepi.2019.10.011
Olson, D. M., Provencher, M., Stutzman, S. E., Hynan, L. S., Novakovic, S., Guttikonda, S., Figueroa, S., Novakovic-White, R., Yang, J. P., & Goldberg, M. P. (2022). Outcomes From a Nursing-Driven Acute Stroke Care Protocol for Telehealth Encounters. Journal of Emergency Nursing, 48(4), 406–416. https://doi.org/10.1016/j.jen.2022.01.013
Peters, M. D. J., Marnie, C., Colquhoun, H., Garritty, C. M., Hempel, S., Horsley, T., Langlois, E. V., Lillie, E., O’Brien, K. K., Tunçalp, Ӧzge, Wilson, M. G., Zarin, W., & Tricco, A. C. (2021). Scoping reviews: reinforcing and advancing the methodology and application. Systematic Reviews, 10(1), 1–6. https://doi.org/10.1186/s13643-021-01821-3
Pollock, D., Davies, E. L., Peters, M. D. J., Tricco, A. C., Alexander, L., McInerney, P., Godfrey, C. M., Khalil, H., & Munn, Z. (2021). Undertaking a scoping review: A practical guide for nursing and midwifery students, clinicians, researchers, and academics. Journal of Advanced Nursing, 77(4), 2102–2113. https://doi.org/10.1111/jan.14743
Powell, K., Hebb, A., & Hackett, C. T. (2025). Nursing-Led Quality Improvement Project Achieves 30-Minute Door-to-Needle Time. Journal of Emergency Nursing, S0099-1767. https://doi.org/10.1016/j.jen.2025.10.013
Raju, S., Hau, C., Woods, P., Flynn, M., Sadatis, C., McPherson, J., Tella, A., Ishani, A., Ferguson, R. E., & Leatherman, S. M. (2023). Ascertainment of stroke from administrative data to support a pragmatic embedded clinical trial. Contemporary Clinical Trials, 130, 107214. https://doi.org/10.1016/j.cct.2023.107214
Rasyid, A., Kurniawan, M., Mesiano, T., Hidayat, R., Rilianto, B., & Harris, S. (2022). Performance of door-to-CT time of code stroke in Indonesian tertiary referral center hospital. Egyptian Journal of Neurology, Psychiatry and Neurosurgery, 58(1), 1–5. https://doi.org/10.1186/s41983-022-00583-6
Saver, J. L. (2006). Time is brain - Quantified. Stroke, 37(1), 263–266. https://doi.org/10.1161/01.STR.0000196957.55928.ab
Seah, H. M., Burney, M., Phan, M., Shell, D., Wu, J., Zhou, K., Brooks, O., Coulton, B., Maingard, J., Tang, J., Yazdabadi, G., Tahayori, B., Barras, C., Kok, H. K., Chandra, R., Thijs, V., Brooks, D. M., & Asadi, H. (2019). CODE STROKE ALERT—Concept and Development of a Novel Open-Source Platform to Streamline Acute Stroke Management. Frontiers in Neurology, 10. https://doi.org/10.3389/fneur.2019.00725
Song, S., Diebolt, E., Livesay, S., Gonzaga-Reardon, M., Hondros, L., Lynch, D., & Conners, J. (2016). Nurse-Activated Acute Stroke Codes in the Emergency Setting Improves Process and Clinical Outcomes (S16.006). Neurology, 86(16_supplement). https://doi.org/10.1212/wnl.86.16_supplement.s16.006
Sung, S. F., Hung, L. C., & Hu, Y. H. (2021). Developing a stroke alert trigger for clinical decision support at emergency triage using machine learning. International Journal of Medical Informatics, 152, 104505. https://doi.org/10.1016/j.ijmedinf.2021.104505
Tricco, A. C., Lillie, E., Zarin, W., O’Brien, K. K., Colquhoun, H., Levac, D., Moher, D., Peters, M. D. J., Horsley, T., Weeks, L., Hempel, S., Akl, E. A., Chang, C., McGowan, J., Stewart, L., Hartling, L., Aldcroft, A., Wilson, M. G., Garritty, C., … Straus, S. E. (2018). PRISMA extension for scoping reviews (PRISMA-ScR): Checklist and explanation. Annals of Internal Medicine, 169(7), 467–473. https://doi.org/10.7326/M18-0850
Vaghari, D., Mohankumar, G., Tan, K., Lowe, A., Shering, C., Tino, P., & Kourtzi, Z. (2025). AI-guided patient stratification improves outcomes and efficiency in the AMARANTH Alzheimer’s Disease clinical trial. Nature Communications , 16(1). https://doi.org/10.1038/s41467-025-61355-3
Wenang, S., Schaefers, J., Afdal, A., Gufron, A., Geyer, S., Dewanto, I., & Haier, J. (2021). Availability and Accessibility of Primary Care for the Remote, Rural, and Poor Population of Indonesia. Frontiers in Public Health, 9. https://doi.org/10.3389/fpubh.2021.721886
Wibowo, M. F., Pyle, A., Lim, E., Ohde, J. W., Liu, N., & Karlström, J. (2025). Insights Into the Current and Future State of AI Adoption Within Health Systems in Southeast Asia: Cross-Sectional Qualitative Study. Journal of Medical Internet Research, 27. https://doi.org/10.2196/71591
Wisni, A., Nurhidayati, E., Anton, S. R., Alifah, N., & Murdiyanto, J. (2025). Building a Code Stroke at Hospital without CT-scan Facilities. Proceeding ISETH (International Summit on Science, Technology, and Humanity), 974–980. https://doi.org/10.23917/iseth.5489
Wong, J. Z. W., Park, P. S. W., Frost, T., Stephens, K., Newk-Fon Hey Tow, F. K., Garcia, P. G., Senanayake, C., & Choi, P. M. C. (2023). Using body cameras to quantify the duration of a Code Stroke and identify workflow issues: A continuous observation workflow time study. BMJ Open, 13(1), 67816. https://doi.org/10.1136/bmjopen-2022-067816
Woodward, A., Keesler, S., Barajas, K., & Singh, P. (2024). Nurse-Led Novel Stroke Response Unit Improves Door-to-Needle Metrics and Patient Outcomes. Journal of Radiology Nursing, 43(4), 240–244. https://doi.org/10.1016/j.jradnu.2024.08.011
Zheng, Y., Guo, Z., Zhang, Y., Shang, J., Yu, L., Fu, P., Liu, Y., Li, X., Wang, H., Ren, L., Zhang, W., Hou, H., Tan, X., & Wang, W. (2022). Rapid triage for ischemic stroke: a machine learning-driven approach in the context of predictive, preventive and personalised medicine. EPMA Journal, 13(2), 285–298. https://doi.org/10.1007/s13167-022-00283-4
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Purwandi, Pahria, T, Pratiwi, S. H., Fauzan, N. H.
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
