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:

• Quality of Care: RPM enables to access patients instantly and provides them with care, right when they are experiencing pain or any symptoms.

• Patient Engagement: One on one engagement with the patients can happen easily and the patients don’t have to book appointments for everything.

• Patient Education: Patients can also learn about their levels through their smartphones and can be guided with the right suggestions and tips to maintain healthy levels.

• Instant Support: Even if the care team is not available live, patients can watch their recorded videos on any assistance they might require.
  

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:

1. Ingestion of health data from the connected sensors,
2. Transforming the data for further use, and
3. Consuming the data into our application and dashboard.

Let us understand how each AWS service is being utilized in the architecture:

• Proprietary Sensors: They are used to collect the vital signs for monitoring the patient’s health like Heart Rate, Blood Sugar levels, etc.,
• MQTT: It is used for connecting the AWS IoT Devices as the SDKs support MQTT which supports the security requirements of client connections.
• IoT Core: AWS IoT Core lets you connect IoT devices to the AWS cloud without the need to provision or manage servers. AWS IoT Core can support billions of devices and trillions of messages and can process and route those messages to AWS endpoints and other devices reliably and securely.
• IoT Analytics: IoT Analytics is a fully managed service that operationalizes, analyses and scales automatically to support up to petabytes of IoT data. With AWS IoT Analytics, you can analyze data from millions of devices and build fast, responsive IoT applications without managing hardware or infrastructure.
• QuickSight: Amazon QuickSight is fast, easy-to-use, cloud-powered Analytics service that makes it easy for the Remote Monitoring User to build visualizations, perform ad-hoc analysis, and quickly get insights from their data, anytime, on any device.
• EC2 Instances: An Amazon EC2 instance is a virtual server in Amazon’s Elastic Compute Cloud (EC2) for running applications on the Amazon Web Services (AWS) infrastructure.
• Kinesis Analytics: The service is used by the Remote Monitoring user to analyze the live streaming data of their patients.
• Kinesis Data Streaming: It is used for streaming the live data that is being sent to Kinesis Analytics for getting insights.