Connected Devices - How GE HealthCare Uses MongoDB to Manage IoT Device Lifecycle

Humza Akhtar
April 25, 2023 | Updated: April 26, 2023
#Healthcare

GE HealthCare, a global leader in medical technology, has turned to MongoDB to manage the lifecycle of its IoT devices, from deployment (Beginning of Life or BoL) to retirement (End of Life or EoL).

At GE HealthCare, MongoDB Atlas is used to persist device and customer data. These related data layers are utilized by the organization to develop customer experience strategies by providing greater efficiency, improving patient outcomes, and increasing access to care. The MongoDB document model easily combines data from diverse source systems while preserving its full fidelity. This flexibility allows seamless onboarding of new customers and related data sources without requiring time consuming schema modifications.

According to Emir Biser, Senior Data Architect at GE HealthCare, MongoDB Atlas is very appealing to the team because of its effective management, built-in monitoring and backup, global vertical and horizontal scalability, built-in security, and multi-cloud support.

MongoDB Atlas is a gamechanger. This technology stack is helping us streamline commercialization and bring market-ready solutions to deliver advanced healthcare.

Some of the recent tests resulted in an *83% decrease in retrieval time for critical data elements.

When all these features are put together, the tech stack is designed to help healthcare providers enhance productivity by reducing the complexity and time required to manage databases, enabling faster deployment of IoT devices.

Enhancing the IoT life cycle with MongoDB

GE HealthCare’s tech-stack is designed to accelerate the integration of healthcare applications by connecting IoT devices together with additional data sources into an aggregated clinical data layer. As the IoT device connections are established, multiple services are applied on the platform to support analytic and clinical applications.

Beginning of life - Device provisioning and configuration

As the device is being manufactured, the device parameters such as MAC and serial number are stored in MongoDB as a device digital representation. When the device is turned on, the GEHC team gets information about the device usage and the customer information. This information is used to validate the device. MongoDB is playing a crucial role in device provisioning by persisting the configuration information and making sure that the device is set up with the right configuration parameters. MongoDB change streams are used at this stage to make sure that the device gets the right parameters at the BoL stage.

Middle of life - Device usage and maintenance

Once the device comes online, it transmits both clinical and non-clinical information. The team at GE HealthCare uses MongoDB Atlas to help ensure clear separation between clinical and non-clinical as permissions, sensitivity, and access differs. Additionally, to understand how the device is being used compared to its standard configuration parameters. MongoDB’s real-time analytics capabilities help track key device performance metrics, such as battery life and identify trends and patterns in device usage. This enables the team to proactively address device issues, improve overall device performance and reliability for customers. GEHC is able to share these insights with customers to help optimize use of devices within their enterprise.

MongoDB Atlas Search is used to retrieve information about status of connected devices and usage patterns. Search Compound Geo JSON queries are used to look at products in a certain geographic region. Horizontal scalability with automatic sharding across clusters ensures Edison applications can continue to be cost effective while delivering real-time results.

MongoDB’s security features, including authorization, authentication and encryption, work with GEHC processes to enable teams working to protect device data from unauthorized access.

End of life - Device decommissioning and archiving

When the IoT device reaches the end of its lifecycle, GE HealthCare needs to decommission it and ensure that any data associated with the device is securely archived. By using MongoDB’s TTL (time-to-live) collections feature, the team automates the process of data deletion, reducing the data footprint. In addition, Atlas Online Archive helps to ensure that the data is always backed up and securely archived, reducing the risk of data loss and corruption. The authentication and authorization mechanisms help to ensure that decommissioned devices data can only be accessed by authorized personnel.

The future of GE HealthCare

According to Emir, the teams using MongoDB Atlas are excited about the benefits it brings, and they are looking forward to exciting new developments in Atlas platform.

We are helping teams achieve business goals across Imaging, Ultrasound Digital Solutions, and Patient Care Solutions. Our current strategy focuses on building solid pipelines to further help our medical device engineering teams deliver interoperability resulting in better care for our customers.

More on managing massive IoT devices

Internet of Things (IoT) is transforming the healthcare industry by providing real-time, actionable insights that improve patient outcomes and drive operational efficiencies. According to market analyst reports, the global IoT healthcare market is projected to reach around USD 446.52 billion by 2028 while exhibiting a CAGR of 25.9% between 2021 and 2028. In hospitals, IoT-enabled medical devices help improve patient safety and clinical experience by transmitting real-time monitoring and alerts in the event of device malfunctions or irregularities.

The life of an IoT device can be divided into three main stages: Beginning of Life (BoL), Middle of Life (MoL) and End of Life (EoL). During the BoL stage, the key activities are deployment design and provisioning. In this stage the device may be pre loaded with default credentials and configuration files. Once the device is installed and comes online, the focus in the MoL is to maintain its basic functional purpose as well as regularly updating firmware for reliability and security purposes. Over time, as new versions of devices are manufactured, the deployed devices need to be decommissioned by revoking the device certificate, archiving device data and disabling the model of device in the cloud as part of the EoL stage.

Figure 1: Three stages of IoT device lifecycle management

In each of the stages, the device has to be maintained to stay reliable, efficient, persistent and secure. Setting up telemetry from device to cloud/back end is just the tip of the iceberg. As the number of IoT devices deployed in healthcare continues to grow, so does the challenge of managing them efficiently. The large amount of data generated creates scalability challenges for IoT device management systems, which need to be able to handle large amounts of data and support the increased traffic. Different communication protocols make it challenging to integrate these devices into a unified system. Maintaining standard communication protocols and interoperability is critical to ensure seamless communication between devices and cloud backend. Finally, with the increasing number of cyber-attacks targeting IoT devices, it is critical to have robust security measures in place to protect against threats.

To learn more about GEHC digital offerings please visit https://apps.gehealthcare.com/

  • Test performed internally by GE HealthCare on company datasets and may not be replicable.

To learn more about MongoDB’s role in the healthcare and manufacturing industry, please visit our Manufacturing and Industrial IoT and Healthcare pages.

← Previous

Three Major IoT Data-Related Challenges and How to Address Them

IoT has formed itself a crucial component for future-oriented solutions and holds a massive potential of economic value. McKinsey & Company estimates that by 2030, IoT (Internet of Things) will enable $5.5 trillion to $12.6 trillion in value worldwide, including the value captured by consumers and customers. For proof of its growing popularity and consumers’ dependency on it, you likely don't need to look any further than your own wrist. From fitness bands to connected vehicles, smart homes, and fleet-management solutions in manufacturing and retail, IoT already connects billions of devices worldwide, with many more to come. As more IoT enabled devices come online, with increasingly sophisticated sensors, choosing the right underlying technology to make IoT solutions easier to implement and help companies seize new innovative opportunities is essential. In this blog we will discuss how MongoDB has successfully addressed three major IoT data-related challenges across various industries, including Manufacturing, Retail, Telecommunications, and Healthcare. The challenges are the following: Data Management Real-Time Analytics Supply Chain Optimization FIgure 1: MongoDB Atlas for IoT Let's dive right in! Data management Storing, transmitting, and processing the large amount of data that IoT devices produce is a significant challenge. Additionally, the data produced by IoT devices often comes in variable structures. This data must be carefully timestamped, indexed, and correlated with other data sources to make the context required for effective decision-making. This data volume and complexity combination makes it difficult to effectively and efficiently process data from IoT devices. Bosch Consider Bosch IoT Suite , a family of products and services in IoT device management, IoT data management, and IoT edge by Bosch Digital. These products and services hold over 250 international IoT projects and over 10 million connected devices. Bosch implemented MongoDB to store, manage, and analyze data in real time. MongoDB’s ability to handle structured, semi-structured, and unstructured data, and efficient data modeling with JSON make it easy to map the information model of each device to its associated document in the database. In addition, dynamic schemas support agile development methodologies and make it simple to develop apps and software. Adding new devices, sensors, and assets is easy, which means the team can focus on creating better software. ThingSpace Another example is that of ThingSpace , Verizon’s market-leading IoT connectivity management platform, which provides the network access required to deliver various IoT products and services. Verizon works with companies that purchase network access from it to connect their devices, bundled together with their own solutions, which they sell to end users. ThingSpace’s customers each sell an IoT product that needs reliable connectivity to ensure the devices always work, which WiFi cannot offer. Verizon’s monolithic RDBMS-based system would not be able to scale to handle both transactional and time-series workloads, so Verizon decided it needed a distributed database architecture going forward. MongoDB proved to be the only solution that scaled to meet Verizon’s requirements across different use cases and combinations of workload types. The immense processing needs resulting from the high number of devices and high velocity of messages coming in were only addressed by MongoDB’s highly available, scalable architecture. Native MongoDB Time Series allow for improved performance, through optimized storage with clustered indexes and optimized Time-Series query operators. MongoDB's advanced capabilities, such as flexible data modeling, powerful indexing and Time Series provide an effective solution for managing the complex and diverse data generated by IoT devices. Real-time analytics Real-time data analytics, one of the most crucial parts of big data analytics today, brings value to businesses for making more data-driven real-time decisions. However, despite its importance, very few can respond to changes in data minute by minute or second by second. Many challenges arise when it comes to the implementation of real-time analytics for enterprises. Storing such a huge volume of data and analyzing it in real time is an entirely different story. Thermo Fisher Cloud Let’s consider the Thermo Fisher Cloud, one of the largest cloud platforms for the scientific community on AWS. MS Instrument Connect allows Thermo Fisher customers to see live experiment results from any mobile device or browser. Each experiment produced millions of "rows" of data, which led to suboptimal performance with existing databases. Internal developers needed a database that could easily handle a wide variety of fast-changing data. MongoDB's expressive query language and rich secondary indexes provided the flexibility to support both ad-hoc and predefined queries customers needed for their scientific experiments. Anytime I can use a service like MongoDB Atlas, I’m going to take that so that we at Thermo Fisher can focus on what we’re good at, which is being the leader in serving science. Joseph Fluckiger, Sr. Software Architect @Thermo Fisher MongoDB Atlas scales seamlessly and is capable of ingesting enormous amounts of sensor and event data to support real-time analysis for catching any critical events or changes as they happen. That gives organizations new capabilities, including: Capturing streaming or batch data of all types without excessive data mapping Analyzing data easily and intuitively with a built-in aggregation framework Delivering data insights rapidly and at scale with ease With MongoDB organizations can optimize queries to quickly deliver results to improve operations and drive business growth. Supply chain optimization Items move through different locations in the supply chain, making it hard to maintain end-to-end visibility throughout their journey. The lack of control on any stage can dramatically harm the efficiency of planning, slow down the entire supply chain and ultimately result in lower return on investment. From optimizing warehouse space by sourcing raw materials as needed, to real-time supply chain insights, IoT-enabled supply chains can help significantly optimize these processes by eliminating blind spots and inefficiencies. Longbow Advantage Longbow Advantage delivers substantial business results by enabling clients to optimize their supply chains. Millions of shipments move through multiple warehouses every day, generating massive quantities of data throughout the day that must be analyzed for real-time visibility and reporting. Its flagship warehouse visibility platform, Rebus, combines real-time performance reporting with end-to-end warehouse visibility and intelligent labor management. Longbow needed a database solution that could process quantities of that scale and deliver real-time warehouse visibility and reporting at the heart of Rebus, and it knew it could not rely on monolithic, time-consuming spreadsheets to do so. It became clear that MongoDB’s document database model was a good match and would allow Rebus to gather, store, and build visibility into disparate data in near real time. Another key component of smart supply chain solutions is IoT-enabled mobile apps that provide real-time visibility and facilitate on-the-spot data-driven decisions. In such situations, the offline first paradigm becomes crucial, since staff need access to data in areas where connectivity is poor or nonexistent. Realm by MongoDB is a lightweight, object-oriented embedded database technology for resource constrained environments. It is an ideal solution for storing data on mobile devices. By utilizing MongoDB’s Realm SDKs, which wrap the Realm database, and Atlas Device Sync , which enables seamless data synchronization between MongoDB and Realm on your mobile phone with minimal developer efforts, businesses can rapidly develop mobile applications and drive innovation. MongoDB provides a powerful solution for IoT-enabled supply chains that can optimize processes and eliminate inefficiencies, enabling organizations to make data-driven decisions and improve supply chain efficiency. Conclusion The IoT industry is rapidly evolving, and as the number of connected devices grows, so do the challenges faced by businesses leveraging these solutions. Through a range of real-world use cases, we have seen how MongoDB has helped businesses deal with IoT data management, perform real-time analytics and optimize their supply chains, driving innovation in a variety of industries. With its unique features and capabilities, designed to manage the heavy lifting for you, MongoDB is well-positioned to continue playing a crucial role in the ongoing digital transformation of the IoT landscape. Want to learn more or get started with MongoDB? Check out our IoT resources MongoDB IoT Reference Architecture Migrate existing applications - with Relational Migrator MongoDB & IIoT ebook IoT webpage

April 24, 2023
Next →

MongoDB Database Observability: Integrating with Monitoring Tools

This post is the final in a three-part series on leveraging database observability. Welcome back to our series on Leveraging Database Observability! Our previous post showcased a real-world use case highlighting how MongoDB Atlas’s observability tools effectively tackle database performance challenges. Whether you’re a developer, DBA, or DevOps engineer, our mission is to empower you to harness the full potential of your data through our observability suite . Integrating Atlas metrics with your central enterprise observability tools can simplify your operations. By seamlessly working with popular observability tools, our approach helps teams streamline workflows and enhance visibility across systems. Integrating MongoDB Atlas with third-party monitoring tools MongoDB’s developer data platform combines all essential data services for building modern applications within a unified experience. Our purpose-built observability tools for Atlas environments offer automatic monitoring and optimization, guiding diagnostics tailored specifically for MongoDB. Additionally, we extend Atlas metrics into your existing enterprise observability stack, enabling seamless integration without replacing your current tools. This creates a consolidated, single-pane view that unifies Atlas telemetry with other tech and application metrics, ensuring comprehensive visibility into both database and full-stack performance. This integration empowers you to monitor, receive alerts, and make data-driven decisions within your existing workflows, driving greater efficiency. Below is a quick guide to modifying integration settings through the Atlas UI and the popular integrations we support: Navigate to the Project Integrations page in Atlas. Choose the organization and project you want to configure from the navigation bar. On the Project Integrations page, select the third-party services you’d like to integrate. Configure the chosen services with the required API keys and regions. Critical integrations for your observability platform With Atlas’s Datadog and Prometheus integrations, you can send critical MongoDB metrics to these platforms, empowering detailed, real-time monitoring. Through Datadog , you can track database operation counts, query efficiency, and resource usage, ideal for pinpointing bottlenecks and managing resources. Similarly, Prometheus enables you to monitor essential metrics like query times, connection rates, and memory usage, supporting flexible tracking of database health and performance. Both integrations facilitate proactive detection of issues, alert configuration for resource thresholds, and a cohesive view of Atlas data when visualized in Grafana. Atlas’s integration with PagerDuty streamlines incident management by sending metrics like performance alerts, billing anomalies, and security events directly to PagerDuty. This integration records incidents automatically, notifies teams upon alerts, and supports two-way syncing, ensuring resolved alerts in Atlas are reflected in PagerDuty. It enables efficient incident response and resource allocation to maintain system stability. With Atlas integrations for Microsoft Teams and Slack, you can route key metrics—such as query latency, disk usage, and throughput—to these channels for timely updates. Teams can use these insights for real-time performance monitoring, incident response, and collaboration. Notifications through these platforms ensure your team stays informed on database performance, storage health, and user activity changes as they occur. Use case: Centralized observability with MongoDB Atlas, Datadog, and Slack Let’s walk through a hypothetical scenario for ShopSmart, an e-commerce company that leverages MongoDB Atlas to manage its product catalog and customer data. As traffic surges, the DevOps team faces challenges in monitoring application performance and database health effectively. To tackle these challenges, the team leverages MongoDB Atlas’ integration with Datadog and Slack, creating a powerful observability ecosystem. Integrating MongoDB Atlas with Datadog: The team pushes key MongoDB Atlas metrics into Datadog, such as query performance, connection counts, and Atlas Vector Search metrics. With Datadog, they can visualize these metrics and correlate overall MongoDB performance with their other applications. Out-of-the-box monitors and dedicated dashboards allow the team to track metrics like throughput, average read/write latency, and current connections. This visibility helps pinpoint bottlenecks in real time, ensuring optimal database performance and improving overall application responsiveness. Setting up alerts in Datadog: The team configures alerts for critical metrics like high query latency and increased error rates. When thresholds are breached, Datadog instantly notifies the team. This proactive approach allows the team to address potential performance issues before they impact customers. Integrating Datadog with Slack: To ensure fast communication, alerts are sent directly to the dedicated Slack channel, “ShopSmart-Alerts.” This integration fosters seamless collaboration, enabling the team to discuss and resolve issues in real-time. With these integrations, ShopSmart’s engineering team can monitor performance quickly and address issues efficiently. The unified observability approach enhances operational efficiency, improves the customer experience, and supports ShopSmart’s competitive edge in the e-commerce industry. By leveraging MongoDB Atlas, Datadog, and Slack, the team ensures scalable performance and drives continuous innovation. Conclusion MongoDB Atlas empowers developers and organizations to achieve unparalleled observability and control over their database environments. By seamlessly integrating with central enterprise observability tools, Atlas enhances your ability to monitor performance metrics and ensures you can do so within your existing workflows. This means you can focus on building modern applications confidently, knowing you have the insights and alerts necessary to maintain optimal performance. Embrace the power of MongoDB Atlas and transform your approach to database management—because your applications can thrive when your data is observable. And that wraps up our Leveraging Database Observability series! We hope you learned something new and found value in these discussions. Sign up for MongoDB Atlas , our cloud database service, to see database observability in action. To dive deeper and expand your knowledge, check out this learning byte for more insights on the MongoDB observability suite and how it can enhance your database performance.

November 14, 2024