IoT-Based Ambulatory Vital Signs Data Transfer System

In emergencies or life-threatening situations, patients are generally shifted to hospitals in ambulances. The health conditions of on-board patients can become critical if they are not evaluated and treated in time. Chances of saving lives can increase significantly if patients’ vital signs inside an ambulance or on-site triage area are transferred to a hospital in real time. If the ambulances are linked to target hospitals, then the physicians in emergency rooms can monitor on-board patients’ vital signs and issue instructions to paramedics to stabilize patients’ medical conditions before they reach the assigned hospitals. Transferred vital signs data may also be archived for medical records. The Internet of things (IoT) is a paradigm which envisions Internet connectivity of virtually everything on the earth. In this paper, an IoT-based low-cost solution is proposed to monitor, archive, analyze, and tag the vital signs data of multiple patients and transfer them to the remote hospital in real time. This opens up a lot of possibilities in telemedicine and disaster management. As a proof of concept, the functionality of the proposed system was validated by developing a prototype model utilizing an IoT-enabled medical sensor board and a Linux server mimicking the remote hospital server. Results of actual data transmission obtained during experimentation are also provided. It is hoped that the proposed system can play a role in saving human lives in disaster situations

SARS-CoV-2 vaccination modelling for safe surgery to save lives: data from an international prospective cohort study

Background: Preoperative SARS-CoV-2 vaccination could support safer elective surgery. Vaccine numbers are limited so this study aimed to inform their prioritization by modelling. Methods: The primary outcome was the number needed to vaccinate (NNV) to prevent one COVID-19-related death in 1 year. NNVs were based on postoperative SARS-CoV-2 rates and mortality in an international cohort study (surgical patients), and community SARS-CoV-2 incidence and case fatality data (general population). NNV estimates were stratified by age (18-49, 50-69, 70 or more years) and type of surgery. Best- and worst-case scenarios were used to describe uncertainty. Results: NNVs were more favourable in surgical patients than the general population. The most favourable NNVs were in patients aged 70 years or more needing cancer surgery (351; best case 196, worst case 816) or non-cancer surgery (733; best case 407, worst case 1664). Both exceeded the NNV in the general population (1840; best case 1196, worst case 3066). NNVs for surgical patients remained favourable at a range of SARS-CoV-2 incidence rates in sensitivity analysis modelling. Globally, prioritizing preoperative vaccination of patients needing elective surgery ahead of the general population could prevent an additional 58 687 (best case 115 007, worst case 20 177) COVID-19-related deaths in 1 year. Conclusion: As global roll out of SARS-CoV-2 vaccination proceeds, patients needing elective surgery should be prioritized ahead of the general population