Welcome to Tenovi’s weekly remote patient monitoring summary of the advancements in remote patient monitoring (RPM) research. In today’s fast-paced world, technology transforms how we live, work, and even manage our health. Remote patient monitoring allows healthcare providers to keep track of their patient’s health remotely using FDA-cleared Iot health monitoring devices and other wearable technology.
RPM is becoming increasingly mainstream, enabling healthcare providers to deliver personalized care to their patients, even from a distance. This summary will explore the latest trends, innovations, and advancements in remote patient monitoring. We will provide the insights and information you need to stay up-to-date on this exciting field. So join us as we explore the world of remote patient monitoring and discover how RPM and IoT transform healthcare delivery.
Advancements in Remote Patient Monitoring: Telemedicine and Men’s Health
This week’s first study highlights advancements in remote patient monitoring research in men’s health and their perceptions of telemedicine. A meta-analysis was conducted on 12 studies that included more than 4,000 cases, evaluating patients’ preference for telemedicine over in-person visits, the acceptance of the current telemedicine arrangement, and their willingness to do a teleconsultation.
The results showed that telemedicine was compatible with andrology and sexual medicine practices and saved an average cost of $149-$252 per patient. Additionally, 68.7% of patients had positive perceptions of telemedicine, 65.1% would recommend telemedicine. The researched uncovered that 84.6% preferred video appointments compared to 38.9% preferring telephone appointments. However, some patients posed concerns about the security and privacy with telecommunication. These findings suggest that telemedicine will likely continue to play a critical role in andrology practices.
Disparities in Telehealth Access
This study surrounding advancements in remote patient monitoring research aimed to determine if there were differences in access or willingness to use telehealth between rural and non-rural and low-income and non-low-income adults. Additionally researchers measured the prevalence of perceived barriers. The study found that rural and low-income adults were less likely to report telehealth access than non-rural and non-low-income counterparts. However, there were no differences in willingness to use telehealth between rural and non-rural or low-income and non-low-income adults. The study suggests that lack of access is the primary driver of telehealth disparities in rural areas. Moreover, race and ethnicity does not affect willingness to use telehealth once access is granted.
Advancements in Remote Patient Monitoring: Labor Progress Monitoring System
Pregnant women often go to the hospital too early when they start having contractions. Usually, monitoring contractions requires specialized medical devices and a doctor’s expertise. This article proposes a system that allows low-risk first-time mothers to monitor labor progress at home in order to determine a suitable time for hospital admission.
The system uses a sensing device worn on the abdomen to detect contractions. An AI algorithm used on a mobile phone classifies the woman’s contraction state as either deferred, optional, or recommended admission. An internet-connected platform also provides guidance from doctors. In tests, the system achieved over 96% accuracy in classifying contraction states. The researchers conclude that the proposed system enables remote monitoring of labor progress at home, avoiding overly early hospital admission.
IoT-Based Smart Baby Monitoring System with Emotion Recognition
IoT and machine learning have proven helpful in advancements in remote patient monitoring research and various fields, such as smart homes, computer vision, and agriculture. The proposed system in this study aims to improve the monitoring of babies by working parents. Parents can monitor their babies through online streaming via a camera.
Using different sensors, the system monitors vital parameters like room temperature and humidity. The sensor data is transferred to a server via controllers with an internet connection. In addition, the system can detect the facial emotions of babies using a machine-learning model.
Parents can monitor their child’s live activities and emotions through an external web camera. The application allows the parent to swing the baby cradle remotely when a cry is detected. They can also check the real-time room temperature and humidity level.
The system sends instant notifications to the parent’s application when abnormal activity is detected. The proposed approach combines machine learning and IoT to overcome the limitations of existing baby monitoring systems.
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