Original Research Article
Year: 2023 | Month: August | Volume: 13 | Issue: 8 | Pages: 210-219
DOI: https://doi.org/10.52403/ijhsr.20230829
Validation of Artificial Intelligence Based Real Time Multi-Vitals Remote Monitoring Solution - A Clinical Study
Sowjanya Patibandla1, Rajani Adepu2, B. Madhuri3, A. Ranjith4
1Associate Professor, Department of Emergency Medicine, NRI Institute of Medical Sciences, Vizag, Andhra Pradesh, India
2Associate Professor, Department of Pharmacology, SRR College of Pharmaceutical Sciences, Warangal, Telangana, India
3Clinical Research Co-ordinator, Vigocare PVT.LTD., Hyderabad, India
4Assistant Manager, Medical Affairs, Vigocare PVT. LTD., Hyderabad, India
Corresponding Author: Dr. Sowjanya Patibandla
ABSTRACT
Background: Recently the combination of telehealth with remote patient monitoring has paved the way for enhanced and augmented health-care services. Its benefits include obtaining efficient, cost and time saving data and minimizing error factors by remote monitoring methods. The validation of multi-vital artificial intelligence-based software (Vigo platform) was conducted to correlate reproducibility of real time vital recording solution.
Methods: IEC approval and informed consents were obtained. Seventeen healthy participants were deployed on the multi-vital monitoring (MVM) solution in a controlled environment and continued for 24 hours. The vitals were measured by the standard methods periodically at intervals of 0, 2, 4, 6, 8, 10, 12 and 24 hours. ECG was measured at 0, 4, 8, 12 and 24 hours.
Results: A total of 20 healthy volunteers were screened out of which seventeen volunteers participated as three volunteers withdrew consent. All the seventeen volunteers were males of mean age 21±3years and mean BMI 22.9±1.7Kg/m2. Vital parameters included Heart rate, Pulse rate, Respiratory rate, Temperature, Systolic blood pressure, Diastolic blood pressure and ECG Measures including P-wave, PR- interval, QRS-complex, RR-interval, QT-interval, ST-Segment, T-wave were strongly and significantly correlated internally (p>0.01) and 100% correlated externally (p>0.01).
Conclusion: It was found that all the vital parameters and ECG measures were strongly matched and significantly correlated internally and externally. This work supports remote monitoring technology as part of remote patient care and decentralized research. Thus, software solution Vigo platform that was developed in this work may become a route to patients’ safety.
Key words: Remote monitoring, multi-vitals, telehealth, decentralized trials, artificial intelligence, Wearable wireless biosensors.