Imagine patients in their homes post-surgery while their smartphones and wearables monitor their key vitals. This is a use-case of Remote Patient Monitoring(or RPM) being used to manage patient care and help drive outcomes.
RPM avoids patients to go through traffic, crowded waiting rooms, or exam tables to climb onto. It is a fact that such activities can cause stress and increase cost, which exacerbates most chronic diseases like diabetes, congestive heart failure, kidney disease, and Alzheimer’s. High-risk patients and stressed-out care teams seldom achieve ideal outcomes.
Monitoring devices have enabled healthcare systems to provide virtual care that makes for relaxed, engaged patients, while real-time health data is transferred to healthcare providers.
RPM is the use of digital technologies to monitor and capture medical and other health data from patients and electronically transmit this information to healthcare providers for assessment and, when necessary, recommendations and instructions. RPM allows providers to continue tracking healthcare data for patients once they are discharged. It also encourages patients to take more control of their health.
RPM is rapidly being adopted by healthcare organizations for various reasons as it is an excellent tool for population health management as well as in Value-Based Care(or VBC) arrangements. Some of the critical reasons for adopting RPM could be:
We have developed a highly scalable cloud-based IoMT(Internet of Medical Things) architecture using AWS services. We share how to connect multiple IoMT (Internet of Medical Things) devices to the AWS Cloud and collect, analyze, and interpret patients’ vitals in remote locations using an architecture that delivers cost-effective IoMT infrastructure with the AWS Cloud and its analytics and visualization services:
For this solution, we will use IoT Core, IoT Analytics, Amazon Kinesis, and Amazon QuickSight to ingest, process, enrich, and visualize our health sensor data. Then, we will use a custom web server for a remote reviewer to see the readings in real time as they are processed by the system.
Broadly, this solution consists of three phases:
Let us understand how each AWS service is being utilized in the architecture: