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.

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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
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MongoDB’s 2024 Year in Review

It’s hard to believe that another year is almost over! 2024 was a transformative year for MongoDB, and it was marked by both innovation and releases that further our commitment to empowering customers, developers, and partners worldwide. So without further ado, let’s dive into MongoDB’s 2024 highlights. We’ll also share our executive team’s predictions of what 2025 might have in store. A look back at 2024 MongoDB 8.0: The most performant version of MongoDB ever In October we released MongoDB 8.0 , the fastest, most resilient, secure, and reliable version of MongoDB yet. Architectural optimizations in MongoDB 8.0 have significantly improved the database’s performance, with 36% faster reads and 59% higher throughput for updates. Our new architecture also makes horizontal scaling cheaper and faster. Finally, working with encrypted data is easier than ever, thanks to the addition of range queries in Queryable Encryption (which allows customers to encrypt, store, and perform queries directly on data). Whether you’re a startup building your first app, or you’re a global enterprise managing mission-critical workloads, MongoDB 8.0 offers unmatched power and flexibility, solidifying MongoDB’s place as the world’s most popular document database. Learn more about what makes 8.0 the best version of MongoDB ever on the MongoDB 8.0 page . Delivering customer value with the MongoDB AI Applications Program AI applications have become a cornerstone of modern software, and MongoDB is committed to equipping customers with the technology, tools, and support they need to succeed on their AI journey. That’s why we launched the MongoDB AI Applications Program (MAAP) in 2024, a comprehensive program designed to accelerate the development of AI applications. By offering customers resources like access to AI specialists, an ecosystem of leading AI and tech companies, and AI architectural best practices supported by integrated services, MAAP helps solve customers’ most pressing business challenges, unlocks competitive advantages, and accelerates time to value for AI investments. Overall, MAAP’s aim is to set customers on the path to AI success. Visit the MongoDB AI Applications Program page or watch our session from AWS re:Invent to learn more! Advancing AI with MongoDB Atlas Vector Search In 2024, MongoDB further cemented its role in the AI space with enhancements to MongoDB Atlas Vector Search . Recognized in 2024 (for the second consecutive year!) as one of the most loved vector databases , MongoDB continues to provide a scalable, unified, and secure platform for building cutting-edge AI use cases. Recent advancements like vector quantization in Atlas Vector Search help deliver even more value to our customers, enabling them to scale applications to billions of vectors at a lower cost. Head over to our Atlas Vector Search quick start guide to get started with Atlas Vector Search today, or visit our AI resources hub to learn more about how MongoDB can power AI applications. Search Nodes: Performance at scale Search functionality is indispensable in modern applications, and with Atlas Search Nodes, organizations can now optimize their search workloads like never before. By providing dedicated infrastructure for Atlas Search and Vector Search workloads, Search Nodes ensure high performance (e.g., a 40–60% decrease in query times), scalability, and reliability, even for the most demanding use cases. As of this year , Search Nodes are generally available across AWS, Google Cloud, and Microsoft Azure. This milestone underscores MongoDB’s commitment to delivering powerful solutions that scale alongside our customers’ needs. To learn more about Search Nodes, check out our documentation or watch our tutorial . Looking ahead: MongoDB’s 2025 predictions After the excitement of the past few years, 2025 will be defined by ensuring that technology investments deliver tangible value. Organizations remain excited about the potential AI and emerging technologies hold to solve real business challenges, but are increasingly focused on maintaining a return on investment. “Enterprises need to innovate faster than ever, but speed is no longer the only measure of success. Increasingly, organizations are laser-focused on ensuring that their technology investments directly address critical business challenges and provide clear ROI and competitive advantage—whether it’s optimizing supply chains, delivering hyper-personalized customer experiences, or scaling operations efficiently,” said Sahir Azam, Chief Product Officer at MongoDB. “In 2025, I expect to see organizations make significant strides in driving this innovation and efficiency by applying AI to more production use cases and by maturing the way they leverage their data to build compelling and differentiated customer experiences.” Indeed, we expect to see organizations make more strategic investments in emerging technologies like gen AI—innovating with a sharp focus on solving business challenges. “In 2025, we can expect the focus to shift from ‘what AI can do’ to ‘what AI should do,’ moving beyond the hype to a clearer understanding of where AI can provide real value and where human judgment is still irreplaceable,” said Tara Hernandez, VP of Developer Productivity at MongoDB. “As we advance, I think we’ll see organizations begin to adopt more selective, careful applications of AI, particularly in areas where stakes are high, such as healthcare, finance, and public safety. A refined approach to AI development will be essential—not only for producing quality results but also to build trust, ensuring these tools genuinely support human goals rather than undermining them.” With more capable, accessible application development tools and customer-focused programs like MAAP at developers’ fingertips, 2025 is an opportunity to make a data-driven impact faster than ever before. "Right now, organizations have an opportunity to leverage their data to reimagine how they do business, to more effectively adapt to a changing world, and to revolutionize our quality of life,” said Andrew Davidson, SVP of Products at MongoDB. “By harnessing our latest technologies, developers can build a foundation for a transformative future." Head over to our updates page to learn more about the new releases and updates from MongoDB in 2024. Keep an eye on our events page to learn what's to come from MongoDB in 2025!

December 19, 2024