Venkata Vamsi Krishna Karatam¹*, Bandireddy Durga Vikas², Pujari Jagannadharao³ and Dinesh Babu Kannan⁴

¹B.Tech., PG Diploma in e-Business (UK) Founder and CEO, India
²B.Tech., Back-end Developer, India
²B.Tech., Embedded Engineer, India
²B.E. (Biomedical), Embedded Engineer, India

*Corresponding Author: Venkata Vamsi Krishna Karatam, B.Tech., PG Diploma in e-Business (UK) Founder and CEO, India.

Received: July 03, 2026; Published: July 27, 2026

Background: Efficient prehospital care requires timely patient onboarding in ambulances; however, this is frequently delayed by manual documentation and recording of vital signs. The proRITHM Integrated Remote Monitoring Platform is a wearable monitoring system that utilises the Internet of Things (IoT) to enhance ambulance workflows by automatically collecting physiological data, documenting it digitally, and transmitting it in real-time. This pilot study evaluated the efficiency of the program to reduce ambulance patient onboarding time.

Methods: We conducted a prospective, single-center, operational validation study of the proRITHM platform on 5 ambulances during 15 days. Twenty consecutive adult patients were enrolled who had been transported by participating ambulances. The patient onboarding time, defined as the time from first clinical contact until patient identification, clinical documentation, vital parameter acquisition and successful data transmission, was compared between the conventional manual workflow and the proRITHM-enabled digital workflow. Workflow components and Emergency Medical Technician (EMT) usability were also evaluated.

Results: The mean onboarding time was significantly lower with the proRITHM platform than with the conventional manual workflow (5.2 ± 1.17 vs 17.85 ± 10.19 minutes; p < 0.001), a mean reduction of 12.65 minutes (64.9%). Mean times for device connection, patient data entry, and vital acquisition were 0.88, 1.50, and 2.83 minutes, respectively. The usability evaluation of EMT showed a very high acceptance: 70% of participants found the platform very easy to use and 70% rated their overall satisfaction as very high. The system was successfully embedded into routine ambulance workflows with no significant operational disruption.

Conclusions: The proRITHM Integrated Remote Monitoring Platform improved the ambulance patient onboarding time significantly and showed excellent usability and operational feasibility. IoT-powered digital onboarding can boost the efficiency of prehospital workflows, allow for earlier communication with receiving hospitals, and lay the groundwork for future AI-driven decision support in emergency medical services. Its impact on patient outcomes and utilisation of healthcare resources needs to be assessed in larger multi-center studies.

Keywords: Prehospital Care; Ambulance; Remote Patient Monitoring; Internet of Things; Wearable Devices; Digital Health; Emergency Medical Services; Workflow Efficiency; Artificial Intelligence

References

1. “Prehospital trauma care systems”. Who.int. World Health Organization (2005).
2. “PHTLS : prehospital trauma life support course manual”. Jones & Bartlett Learning; (2023).
3. Cone DC., et al. “Analysis and impact of delays in ambulance to emergency department handovers”. Emergency Medicine Australasia5 (2012): 525-533.
4. Carter AJE., et al. “Information loss in emergency medical services handover of trauma patients”. Prehospital Emergency Care3 (2009): 280-285.
5. Harmsen AMK., et al. “The influence of prehospital time on trauma patients outcome: A systematic review”. Injury4 (2015): 602-609.
6. Semeraro F., et al. “European Resuscitation Council Guidelines 2021: Systems saving lives”. Resuscitation 161 (2021): 80-97.
7. Islam S., et al. “The Internet of Things for health care: A comprehensive survey”. IEEE Access 3 (2015): 678-708.
8. Dimitrov DV. “Medical internet of things and big data in healthcare”. Healthcare Information Research3 (2016): 156.
9. Majumder S., et al. “Wearable sensors for remote health monitoring”. Sensors (Basel)1 (2017): 130.
10. Steinhubl SR., et al. “The emerging field of mobile health”. Science Translational Medicine283 (2015).
11. Gathright R. “Overview of wearable healthcare devices for clinical decision support in prehospital medical settings”. Sensors (Basel)24 (2024).
12. Chee ML., et al. “Artificial intelligence and machine learning in prehospital emergency care: A scoping review”. iScience 26 (2023).
13. Bignami EG. “Wearable devices in healthcare beyond the one-size-fits-all approach: Current applications and future directions”. Healthcare (2025).
14. Jat AS and Grønli TM. “Harnessing the digital revolution: A comprehensive review of mHealth applications for remote monitoring in transforming healthcare delivery”. (2024).
15. “Reviews successful implementation of digital technologies in EMS, emphasizing ease of use, training, and workflow integration”. In: EMS Telemedicine in the Prehospital Setting. FL: StatPearls Publishing; (2025).
16. “Discusses user acceptance, implementation barriers, and clinician adoption of wearable technologies”. (2025).
17. Su JS., et al. “EMS telemedicine in the prehospital setting”. StatPearls Publishing, Treasure Island (2025).
18. Jat AS and Grønli TM. “Reviews remote monitoring, cloud platforms, interoperability, and clinical decision-making”. (2024).
19. Chee ML and Huang H. “Artificial intelligence and machine learning in prehospital emergency care: A scoping review”. iScience (2023): 26.
20. Abedi A. “AI-Driven Real-Time Monitoring of Cardiovascular Conditions Using Wearable Devices: A Systematic Review”. JMIR mHealth and uHealth (2025).

Citation

Citation: Venkata Vamsi Krishna Karatam. et al. “Validation of proRithm Device for Reduction of Patient Onboarding Time in Ambulances". Acta Scientific Medical Sciences 10.8 (2026): 66-77.

Copyright

Copyright: © 2026 Venkata Vamsi Krishna Karatam. et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.