Designing a biometric IoMT solution to support health equity with AWS ProServe

Healthcare organizations worldwide are embracing the concept of connective devices using the internet of medical things (IoMT). Data from these wearable internet of things (IoT) devices can provide a real-time snapshot of a patient’s physical health, enabling care providers to determine the next level of care. These devices can help enhance healthcare efficiency, lower costs, and provide better health outcomes. More importantly, this critical health information can be lifesaving, particularly during traumatic health events.

Logiksavvy Innovations (LSI) is a healthcare technology (HealthTech) startup founded in 2021 and headquartered in Atlanta, Georgia. LSI focuses on research and development (R&D) of innovative technologies that impact healthcare, self-care, and the community. LSI founder and chief executive officer (CEO), Kathy Bratcher, a Black woman inventor with over 20 years of experience in enterprise technology, understood the challenges that underrepresented communities face in healthcare disparities and wanted to bridge the gap between health equity, innovative technology, and underrepresented communities.

LSI set out to develop an IoMT solution with the capability to detect and prevent critical health events. Working with Amazon Web Services (AWS) through the Health Equity Initiative, LSI collaborated with AWS Professional Services (AWS ProServe) to develop a viable proof-of-concept (POC) with the AWS Cloud.

Designing an IoT solution to improve health equity with AWS ProServe

LSI had a vision to design a POC for an IoMT biometric detection platform, called BioInsyte, that can monitor wearers’ health trends and anomalies in real time to prevent traumatic health events. According to the World Health Organization (WHO), noncommunicable diseases like heart disease, stroke, and respiratory diseases account for around 74 percent of all deaths worldwide. By providing real-time monitoring of critical health biomarkers with IoMT devices like BioInsyte, LSI aims to potentially mitigate adverse health events and improve health outcomes.

In 2022, LSI was selected by the AWS Diagnostic Development Initiative, a global program focused on accelerating research and innovation for the collective understanding of COVID-19 and other diseases, to help develop BioInsyte using the cloud. This program provided credits to support access to services and development on the AWS Cloud, as well as collaboration with the AWS ProServe team dedicated to nonprofit healthcare. Working closely with LSI, the AWS ProServe team provided engagement management, cloud advisors, technical architects, software developers that specialize in internet of things (IoT), artificial intelligence (AI), machine learning (ML), DevOps, cloud security, solutions integration, and healthcare compliance.

Developing IoMT healthcare solutions at scale with AWS IoT services

LSI and AWS ProServe collaborated to create a solution that captures simulated device data while leveraging FHIR-compliant standards to monitor and predict potential patient issues. The team used AWS services to expedite the design and testing of the solution, allowing LSI to showcase their vision to customers.

The LSI IoMT biometric detection solution consists of data ingestion from the IoT Device Simulator, FHIR-based data integration to Epic Systems electronic health record (EHR), time series data ingestion into Amazon Simple Storage Service (Amazon S3), and data visualization using Amazon Managed Grafana dashboards, which is a fully managed service for Grafana, a popular open-source analytics platform that lets users query, visualize, and alert on metrics, logs, and traces.

To help simulate wristband medical devices at scale, LSI used IoT Device Simulator, which helps customers test device integration and improve performance of their IoT backend services, to generate the FHIR patient vitals. To support compliance with industry standards, such as IEEE 11073 point-of-care medical device standards, the simulated devices output a JSON payload consisting of FHIR resource types like device, observation, and location.

The IoT Device Simulator is an open-source, graphical user interface (GUI)-based simulator that allows users to define devices by adding attributes. The user can then create a simulation and run it to generate the device and its attributes. In developing the BioInsyte POC, LSI used the IoT Device Simulator and modified the metrics to specifically produce healthcare biometric values like those of a real patient.

Figure 1. AWS IoT Device Simulator architecture.

Figure 1. AWS IoT Device Simulator architecture.

The IoT Device Simulator consists of two main components: creating device types and creating and running simulations. Device data from the IoT Device Simulator is sent to AWS IoT Core where it is transformed into multi-measure records and stored in the Amazon TimeStream telemetry table. The TimeStream data is then transformed into a parquet format and stored in Amazon S3 for use by Amazon Athena for historical reporting.

The solution uses AWS Step Functions to retrieve the EHR data using FHIR APIs. Step Functions are visual workflows used for distributed applications. These Step Functions include an AWS Lambda simulator function used to parse, merge, and extract data and send it to Amazon S3. The data in these buckets are subsequently stored for query access.

Figure 2. The BioInsyte architecture on AWS.

Figure 2. The BioInsyte architecture on AWS.

Visualizing key health data in real-time

For the BioInsyte POC, the solution queries patient data from Athena and vitals from the telemetry table in TimeStream and displays them in the Amazon Managed Grafana dashboard. The dashboard displays basic patient demographics and graphs of the key vital signs. The visualization illustrates the potential for IoMT device and EHR data aggregation.

Figure 3. BioInsyte real-time patient monitoring dashboard.

Figure 3. BioInsyte real-time patient monitoring dashboard.

Conclusion

Working with AWS ProServe, the LSI team created a POC of BioInsyte that integrates simulated patient data from an IoT device into a dashboard visualization that monitors key vitals that can signal the onset of an adverse health event. For the next phase of the project, LSI is developing machine learning (ML) models that use patient biometric data and clinical data to predict potential clinical events, such as respiratory failure or acute myocardial infarction (AMI), before the event occurs.

The close collaboration with the AWS ProServe team helped LSI in creating a solution that aligned with their vision for the solution and supported LSI in healthcare compliance considerations while developing their product.

Addressing health inequities requires a multi-faceted approach and a variety of solutions due to multiple complexities. Though technology is not a silver bullet, solutions like those from LSI can be a force multiplier for organizations building innovative cloud-based solutions to remove barriers and reduce disparities in health. Learn more about the AWS Health Equity Initiative.

Plus, discover how HealthTech organizations around the world use AWS to create next-generation healthcare technology to improve health outcomes, promote health equity, and more at the AWS for HealthTech hub.

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