Robotic Triage Systems: Bridging the Gap in Initial Call and Emergency Assessment

Authors

  • Kshitij Shinghal

    Department of Electronics & Communication Engineering, Moradabad Institute of Technology, Moradabad 244001, Uttar Pradesh, India
  • Amit Saxena

    Department of Electronics & Communication Engineering, Moradabad Institute of Technology, Moradabad 244001, Uttar Pradesh, India
  • Rajul Misra

    Department of Electrical Engineering, Moradabad Institute of Technology, Moradabad 244001, Uttar Pradesh, India
  • Avnesh Verma

    Department of Instrumentation (formerly USIC), Kurukshetra University, Kurukshetra 136119, Haryana, India

DOI:

https://doi.org/10.30564/jeis.v8i1.12878
Received: 14 December 2025 | Revised: 20 March 2026 | Accepted: 31 March 2026 | Published Online: 20 April 2026

Abstract

Nowadays healthcare and clinical facilities have become an essential part of modern life. The triage system in hospitals and clinics plays a crucial role in the initial assessment of patients and emergency care. However, conventional triage processes that rely on human judgment often suffer from variability, limited resources, and the possibility of human error. In many situations, the lack of trained personnel further worsens the problem, leading to delays and the potential misclassification of high-acuity patients. To address these challenges, this work proposes an autonomous robotic sensor-based triage system (RTS) designed to automate the initial patient assessment process. By using sensors for data collection, the system standardizes information gathering and reduces errors associated with manual triage. The RTS is designed with a robust and adaptable architecture that can be easily integrated with existing clinical systems and electronic health record (EHR) platforms without disrupting current hospital workflows. The system utilizes non-contact sensors to capture physiological parameters, ensuring patient comfort and reducing the risk of contamination, particularly during infectious disease situations. Embedded artificial intelligence analyzes the collected data and generates a structured symptom report, which is processed by a Clinical Acuity Measurement (CAM) unit to assign a Clinical Acuity Score (CAS) within four minutes. Experimental results demonstrate 92.5% accuracy with expert clinical consensus and 98.1% sensitivity in identifying high-acuity patients, while reducing the time required for initial assessment by nearly 70%.

Keywords:

Emergency Medical Services (EMS); Triage; Robotics; Health Care Automation

References

[1] Mahapatra, Koelling, Patvivatsiri, et al., 2003. Pairing emergency severity index5-level triage data with computer aided system design to improve emergency department access and throughput. In Proceedings of the 2003 Winter Simulation Conference, New Orleans, LA, USA, 7–10 December 2003; pp. 1917–1925. DOI: https://doi.org/10.1109/WSC.2003.1261654

[2] Wilkes, D.M., Franklin, S., Erdemir, E., et al., 2010. Heterogeneous artificial agents for triage nurse assistance. In Proceedings of the 2010 10th IEEE-RAS International Conference on Humanoid Robots, Nashville, TN, USA, 6–8 December 2010; pp. 130–137. DOI: https://doi.org/10.1109/ICHR.2010.5686839

[3] Chong, H.A., Gan, K.B., 2016. Development of automated triage system for emergency medical service. In Proceedings of the 2016 International Conference on Advances in Electrical, Electronic and Systems Engineering, Putrajaya, Malaysia, 14–16 November 2016; pp. 642–645. DOI: https://doi.org/10.1109/ICAEES.2016.7888125

[4] Tsumura, R., Hardin, J.W., Bimbraw, K., et al., 2021. Tele-Operative Low-Cost Robotic Lung Ultrasound Scanning Platform for Triage of COVID-19 Patients. IEEE Robotics and Automation Letters. 6(3), 4664–4671. DOI: https://doi.org/10.1109/LRA.2021.3068702

[5] Erol, M., Ülengin, F., Dinçer, H.A., 2022. A Novel Triage System Design. In Proceedings of the 2022 International Congress on Human-Computer Interaction, Optimization and Robotic Applications, Ankara, Turkey, 9–11 June 2022; pp. 1–7. DOI: https://doi.org/10.1109/HORA55278.2022.9799950

[6] Ashaolu, O., Lyons, W., Stefanakos, I., et al., 2023. Autonomous Emergency Triage Support System. In Proceedings of the 2023 International Conference on Computational Science and Computational Intelligence, Las Vegas, NV, USA, 13–15 December 2023; pp. 1332–1337. DOI: https://doi.org/10.1109/CSCI62032.2023.00220

[7] Townsend, B.A., Plant, K.L., Hodge, V.J., et al., 2023. Medical practitioner perspectives on AI in emergency triage. Frontiers in Digital Health. 5, 1297073. DOI: https://doi.org/10.3389/fdgth.2023.1297073

[8] Khanchai, S., Intawong, K., Boonchieng, W., et al., 2023. Mobile Application for Triage Medical System in Emergency Rooms: Enhancing Accuracy and Attitude—A Case Study in Hospitals of Chiang Mai Province. In Proceedings of the 2023 27th International Computer Science and Engineering Conference, Samui Island, Thailand, 14–15 September 2023; pp. 429–433. DOI: https://doi.org/10.1109/ICSEC59635.2023.10329365

[9] Jasim, A.A., Ata, O., Salman, O.H., 2024. AI-Driven Triage: A Graph Neural Network Approach for Prehospital Emergency Triage Patients in IoMT-Based Telemedicine Systems. In Proceedings of the 2024 International Symposium on Electronics and Telecommunications, Timisoara, Romania, 7–8 November 2024; pp. 1–7. DOI: https://doi.org/10.1109/ISETC63109.2024.10797314

[10] Mejia, J.M.R., Rawat, D.B., 2024. Exploring the Advancements of AI Enabled Clinical Decision Support Systems for Patient Triage in Healthcare. In Proceedings of the 2024 IEEE International Conference on E-health Networking, Application & Services, Nara, Japan, 18–20 November 2024; pp. 1–4. DOI: https://doi.org/10.1109/HealthCom60970.2024.10880833

[11] Sharma, D., Rashno, E., Zulkernine, F., et al., 2024. Triage-Bot: An Assistive Triage Framework. In Proceedings of the 2024 IEEE International Conference on Digital Health, Shenzhen, China, 7–13 July 2024; pp. 138–140. DOI: https://doi.org/10.1109/ICDH62654.2024.00033

[12] Da’Costa, A., Teke, J., Origbo, J.E., et al., 2025. AI-driven triage in emergency departments: A review of benefits, challenges, and future directions. International Journal of Medical Informatics. 197, 105838. DOI: https://doi.org/10.1016/j.ijmedinf.2025.105838

[13] Verma, A., Dhingra, S., Soni, M.K., 2009. VLSI-cell placement technique for Architecture of Field Programmable Gate Array (FPGA) design. Turkish Journal of Electrical Engineering and Computer Sciences. 17(3), 327–336. DOI: https://doi.org/10.3906/elk-0908-173

[14] Verma, A., Dhingra, S., Soni, M.K., 2009. Design and synthesis of FPGA for speed control of induction motor. International Journal of Physical Sciences. 4(11), 645–650. Available from: https://academicjournals.org/journal/IJPS/article-stat/93C172219712

[15] Rani, R., Verma, M., Verma, A., 2025. Application of Fuzzy Inference System for Interpreting Physical Fitness Metrics of Male and Female Trainee Athletes: De-Fuzzification through Hull and Sigma Scale Analysis. Journal of Electronic & Information Systems. 7(2), 13–24. DOI: https://doi.org/10.30564/jeis.v7i2.10609

[16] Verma, A., Tiwari, V.A., 2019. A fuzzy logic controller to minimize the effect of input temperature fluctuations on the deviation of relative humidity to avoid instrument’s deterioration. International Journal of Research in Advent Technology. 7(6), 73–77. DOI: https://doi.org/10.32622/ijrat.76201948

[17] Verma, A., Tiwari, V.A., 2013. Optimum Fuzzy Based Approach to Improve the Instrument’s Performance Affected by Environmental Conditions. Serbian Journal of Electrical Engineering. 10(2), 309–318. DOI: https://doi.org/10.2298/SJEE121219006V

[18] Čabarkapa, V., Čavoški, S., Vujović, V., et al., 2025. Automating Triage: Identifying Gaps in Traditional Approaches and Proposing Solutions. In Proceedings of the 2025 International Symposium INFOTEH-JAHORINA, East Sarajevo, Bosnia and Herzegovina, 19–21 March 2025; pp. 1–6. DOI: https://doi.org/10.1109/INFOTEH64129.2025.10959221

[19] Petrica, A., Marza, A.M., Barsac, C., et al., 2026. Artificial intelligence in emergency department triage: Perspective of human professionals. Frontiers in Digital Health. 7, 1693060. DOI: https://doi.org/10.3389/fdgth.2025.1693060

[20] Abdalhalim, A.Z., Ahmed, S.N., Ezzelarab, A.M., et al., 2025. Clinical Impact of Artificial Intelligence-Based Triage Systems in Emergency Departments: A Systematic Review. Cureus. 17(6), e85667. DOI: https://doi.org/10.7759/cureus.85667

[21] Hassanpour, A., Yang, B., 2025. Contactless Vital Sign Monitoring: A Review towards Multi-Modal Multi-Task Approaches. Sensors. 25(15), 4792. DOI: https://doi.org/10.3390/s25154792

[22] Rajkomar, A., Dean, J., Kohane, I.S., 2019. Machine learning in medicine. New England Journal of Medicine. 380(14), 1347–1358. DOI: https://doi.org/10.1056/NEJMra1814259

[23] Jorge, J., Villarroel, M., Tomlinson, H., et al., 2022. Non-contact physiological monitoring of post-operative patients in the intensive care unit. NPJ Digital Medicine. 5(1), 4. DOI: https://doi.org/10.1038/s41746-021-00543-z

[24] Ahmed, A.A., Mojiri, M.E., Daghriri, A.A., et al., 2024. The Role of Telemedicine in Emergency Department Triage and Patient Care: A Systematic Review. Cureus. 16(12), e75505. DOI: https://doi.org/10.7759/cureus.75505

[25] Choi, S.W., Ko, T., Hong, K.J., et al., 2019. Machine Learning-Based Prediction of Korean Triage and Acuity Scale Level in Emergency Department Patients. Healthcare Informatics Research. 25(4), 305–312. DOI: https://doi.org/10.4258/hir.2019.25.4.305

[26] Krones, F., Marikkar, U., Parsons, G., et al., 2025. Review of multimodal machine learning approaches in healthcare. Information Fusion. 114, 102690. DOI: https://doi.org/10.1016/j.inffus.2024.102690

[27] Dantulurumuralidhar, V., Abrol, S., Rocque, M., et al., 2023. CoVETED Study: Contactless Vitals for Effective Triaging in Emergency Department—A System to Reduce Cross-Infection. Open Forum Infectious Diseases. 10(Suppl 2), ofad500.809. DOI: https://doi.org/10.1093/ofid/ofad500.809

[28] Chai, P.R., Dadabhoy, F.Z., Huang, H.-W., et al., 2021. Assessment of the Acceptability and Feasibility of Using Mobile Robotic Systems for Patient Evaluation. JAMA Network Open. 4(3), e210667. DOI: https://doi.org/10.1001/jamanetworkopen.2021.0667

[29] Faheem, M., Iqbal, A., 2025. Real-time clinical decision support with explainable AI: Balancing accuracy and interpretability in high-stakes environments. International Journal of Science and Research Archive. 16(1), 1204–1220. DOI: https://doi.org/10.30574/ijsra.2025.16.1.1992

[30] Tun, H., Rahman, H., Naing, L., et al., 2025. Trust in Artificial Intelligence–Based Clinical Decision Support Systems Among Health Care Workers: Systematic Review. Journal of Medical Internet Research. 27, e69678. DOI: https://doi.org/10.2196/69678

[31] Gershov, S., Raz, A., Karpas, E., et al., 2024. Towards an autonomous clinical decision support system. Engineering Applications of Artificial Intelligence. 127, 107215. DOI: https://doi.org/10.1016/j.engappai.2023.107215

[32] Abadi, A.S.S., Koguciuk, A., Jangas, M., et al., 2025. Advances in Nano-BioElectronics, Robotic Surgery, and Technologies for Medical and Healthcare. Journal of Electronic & Information Systems. 7(1), 1–21. DOI: https://doi.org/10.30564/jeis.v7i1.9353

[33] Yang, G.-Z., Cambias, J., Cleary, K., et al., 2017. Medical robotics—Regulatory, ethical, and legal considerations for increasing levels of autonomy. Science Robotics. 2(4), eaam8638. DOI: https://doi.org/10.1126/scirobotics.aam8638

[34] Topol, E.J., 2019. High-performance medicine: The convergence of human and artificial intelligence. Nature Medicine. 25, 44–56. DOI: https://doi.org/10.1038/s41591-018-0300-7

[35] Bajwa, J., Munir, U., Nori, A., et al., 2021. Artificial intelligence in healthcare: Transforming the practice of medicine. Future Healthcare Journal. 8(2), e188–e194. DOI: https://doi.org/10.7861/fhj.2021-0095

[36] Abbas, Q., Jeong, W., Lee, S.W., 2025. Explainable AI in Clinical Decision Support Systems: A Meta-Analysis of Methods, Applications, and Usability Challenges. Healthcare. 13(17), 2154. DOI: https://doi.org/10.3390/healthcare13172154

[37] Baharum, A., Ismail, R., Daruis, D., et al., 2025. Human-Robot Interaction-Healthcare: Systematic Review. Artificial Intelligence and Human-Computer Interaction. 404, 537–543. DOI: https://doi.org/10.3233/FAIA250160

[38] Sagona, M., Dai, T., Macis, M., et al., 2025. Trust in AI-assisted health systems and AI’s trust in humans. npj Health Systems. 2, 10. DOI: https://doi.org/10.1038/s44401-025-00016-5

[39] Islam, S.M.R., Kwak, D., Kabir, M.H., et al., 2015. The Internet of Things for health care: A comprehensive survey. IEEE Access. 3, 678–708. DOI: https://doi.org/10.1109/ACCESS.2015.2437951

[40] Kim, C.-M., Choi, Y.-S., 2023. Facial Video-based Remote Photoplethysmography Signal Estimation with Vision Transformer. In Proceedings of the 2023 IEEE International Conference on Consumer Electronics-Asia, Busan, Korea, 23–25 October 2023; pp. 1–4. DOI: https://doi.org/10.1109/ICCE-Asia59966.2023.10326412

[41] Hao, Z., Wang, Y., Li, F., et al., 2025. Detection of vital signs based on millimeter wave radar. Scientific Reports. 15(1), 28112. DOI: https://doi.org/10.1038/s41598-025-09112-w

[42] Perpetuini, D., Filippini, C., Cardone, D., et al., 2021. An Overview of Thermal Infrared Imaging-Based Screenings during Pandemic Emergencies. International Journal of Environmental Research and Public Health. 18(6), 3286. DOI: https://doi.org/10.3390/ijerph18063286

[43] Esteva, A., Robicquet, A., Ramsundar, B., et al., 2019. A guide to deep learning in healthcare. Nature Medicine. 25, 24–29. DOI: https://doi.org/10.1038/s41591-018-0316-z

[44] Beam, A.L., Kohane, I.S., 2018. Big data and machine learning in health care. JAMA: Journal of the American Medical Association. 319(13), 1317–1318. DOI: https://doi.org/10.1001/jama.2017.18391

[45] Fawcett, T., 2006. An introduction to ROC analysis. Pattern Recognition Letters. 27(8), 861–874. DOI: https://doi.org/10.1016/j.patrec.2005.10.010

[46] Levin, S., Toerper, M., Hamrock, E., et al., 2018. Machine-Learning-Based Electronic Triage More Accurately Differentiates Patients with Respect to Clinical Outcomes Compared with the Emergency Severity Index. Annals of Emergency Medicine. 71(5), 565–574. DOI: https://doi.org/10.1016/j.annemergmed.2017.08.005

[47] Raita, Y., Goto, T., Faridi, M.K., et al., 2019. Emergency department triage prediction of clinical outcomes using machine learning models. Critical Care. 23(1), 64. DOI: https://doi.org/10.1186/s13054-019-2351-7

[48] Obermeyer, Z., Powers, B., Vogeli, C., et al., 2019. Dissecting racial bias in an algorithm used to manage the health of populations. Science. 366(6464), 447–453. DOI: https://doi.org/10.1126/science.aax2342

[49] McCoy, L.G., Banja, J.D., Ghassemi, M., et al., 2020. Ensuring machine learning for healthcare works for all. BMJ Health & Care Informatics. 27(3), e100237. DOI: https://doi.org/10.1136/bmjhci-2020-100237

[50] Rajkomar, A., Dean, J., Kohane, I.S., 2018. Scalable and accurate deep learning with electronic health records. npj Digital Medicine. 1, 18. DOI: https://doi.org/10.1038/s41746-018-0029-1

[51] Jiang, F., Jiang, Y., Zhi, H., et al., 2017. Artificial intelligence in healthcare: Past, present and future. Stroke and Vascular Neurology. 2(4), 230–243. DOI: https://doi.org/10.1136/svn-2017-000101

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How to Cite

Shinghal, K., Saxena, A., Misra, R., & Verma, A. (2026). Robotic Triage Systems: Bridging the Gap in Initial Call and Emergency Assessment. Journal of Electronic & Information Systems, 8(1), 47–63. https://doi.org/10.30564/jeis.v8i1.12878

Issue

Vol. 8 , Iss. 1 (April 2026): In Progress

Article Type

ARTICLE

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Copyright © 2026 Kshitij Shinghal, Amit Saxena, Rajul Misra, Avnesh Verma

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This is an open access article under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License.

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