Connected Data: How IoT Will Save Healthcare and Why MongoDB Matters
Over the next decade, healthcare systems around the world will face a two-fold challenge: Delivering higher quality care while managing rising costs, and doing so for increasingly larger populations of patients. For decades, healthcare systems have operated predominantly with traditional fee-for-service models, in which reimbursements are given to providers based on services rendered. Value-based healthcare, in contrast, attempts to lower the cost of care by keeping patients healthier longer through more effective and efficient use of healthcare systems. This article — Part 2 of our series on connected healthcare data — looks at how IoT, with support from MongoDB, can help meet future healthcare challenges. Read Part 1 of this series on connected healthcare data Increased demand It's expected that by 2050, 22% of the world's population will be over 60 years old . This adds increased pressure to the goals of optimizing both patient outcomes and healthcare spend, because there are more people within healthcare systems than ever before. And, as these patients live longer, they experience more chronic conditions and, therefore, require more care. Constraints on the ability to graduate enough doctors and nurses to meet this surge of healthcare demand suggest that innovation will be needed to provide adequate supply. Additionally, many healthcare services are delivered in an exam or hospital room, where patient vitals and observations are captured, a chart is reviewed, and medications and treatments are ordered. According to a recent study from the Annals of Internal Medicine , providers spend more than 16 minutes per encounter on these tasks alone. Observation and data collection in healthcare is critical to identifying and subsequently adjusting treatment pathways; however, the process is heavily reliant on in-person visits. How IoT will save healthcare Global adoption of the Internet of Things (IoT) is soaring across numerous industries. In fact, healthcare is forecasted to be the second largest industry in value for IoT by 2030. IoT offers the ability to remotely monitor patients via wearables and connected devices. It provides the means to collect data beyond the patient exam or hospital room and can help providers deliver care outside of traditional, in-person methods. With this power to collect more information, more often, and do so with fewer patient encounters, IoT plays a role in solving the two-fold challenge of delivering better quality of care for increasingly larger populations of patients. A patient wearing a smartwatch, for example, may be able to stream heart rate and oxygen saturation levels during real-world activities to an electronic healthcare record, where the data can be aggregated and summarized for a physician to review, or even for a machine-learning algorithm to periodically interrogate. IoT devices can help collect more data, more often, to help providers deliver more meaningful, timely, and impactful healthcare recommendations and treatments to patients. Through this added value, IoT can further the benefits of telemedicine and promote the idea of “care anywhere,” in which healthcare is not directly tied to or dependent upon in-person encounters. Challenges of healthcare data on the move What challenges face developers when it comes to capturing and leveraging data from healthcare IoT devices? Four significant capabilities top the list, which we will look at in turn: Scalable and efficient storage Global coverage and data synchronization Interoperability Security and privacy Scalable and efficient storage IoT devices have the capability to produce massive volumes of continuous data. In fact, market intelligence provider International Data Corporation (IDC) predicts that IoT devices alone will produce 74.9 ZB of data by 2025, from a staggering 55.9 billion devices. A cloud-based developer data platform will be critical to support these kinds of massive data volumes, which may also exhibit unpredictable peaks in workloads. Additionally, as is the case for many IoT use cases, often only the most recent data is used for analysis. In this scenario, the ability to automatically archive raw and historical data to a more cost-effective storage, and yet still be able to query it when and if needed, would be ideal. MongoDB’s Atlas Online Archive lets developers do just that, with minimal setup and configuration required, as shown in Figure 1. Figure 1. MongoDB automates data tiering while keeping it queryable with Atlas Online Archive. Not all databases are ready to deal with the massive, continuous data generated by IoT devices. Sensor data is typically collected with high frequency, which may mean high concurrency of writes, unpredictable workload peaks, and the need for dynamic scalability. Additionally, IoT data is almost by definition time-series data, meaning it typically comes with a timestamp that allows following the evolution of a parameter through time, at regular or irregular time intervals. Storing time-series data efficiently at scale can be difficult. In fact, specialized time-series databases exist to tackle workloads such as these. Additionally, storing the data is simply one side of the challenge. Another aspect involves running analytics as the data is collected, such as discovering heart anomalies and sending alerts in real time to the patient. Using specialized time-series databases solves these challenges but also introduces new ones: Developers will need to learn the nuances of working with a niche platform, slowing development cycles. Building and maintaining ETL pipelines to move data and merge data across different platforms. Integrating, securing, and maintaining an additional database platform, thereby increasing operational overhead. MongoDB's new time series collection feature allows you to automatically optimize your schema and deployment for high storage efficiency and low-latency queries, without the need of an additional, niche database. Additionally, MongoDB integrates time-series data with operational data and analytics capabilities in one unified environment with built-in scalability, delivering the performance your IoT applications need while simplifying your architecture. Global coverage and data synchronization For many IoT scenarios, users are effectively on the move: They go to work, they go shopping, and they get on planes to see the new beautiful shiny star on top of Barcelona's Sagrada Família. With all of this mobility, they might lose connectivity for a few minutes or even hours. Tracking their health effectively in real time is not just a nice feature, it may be mandatory. Using MongoDB’s Atlas Device Sync , developers can easily deploy IoT applications that seamlessly handle drops in connectivity, without missing critical write operations of the most important data workloads. Interoperability Most IoT devices use proprietary protocols and operating systems, which seriously limit interoperability. The IoT industry advocates the use of standard communication protocols such as MQTT, but, as of this writing, there is no single industry standard. Custom solutions exist that serve one single type of sensor and/or healthcare provider, but these solutions tend to suffer from interoperability challenges when interlinking data across different healthcare networks. As discussed in our first post , sharing healthcare data across different participants of the healthcare ecosystem requires standards such as JSON-based FHIR, which is key to mitigate healthcare fragmentation. Learn how we used MongoDB and MQtt to "listen" and "talk" remotely to an IoT-powered facility. Downloadable code available. Security and privacy Given its sensitive and personal nature (and relatively easy monetization through theft), health data is especially appealing to bad actors. The number of security incidents impacting healthcare systems is sobering. According to a report by Crowdstrike , 82% of health systems experienced some form of IoT cyberattack in 2020. With IoT proliferation on the rise, the need for the highest level of security at the application level and at the database level, becomes non-negotiable. Unsurprisingly, McKinsey cites interoperability, security, and privacy as major headwinds for IoT adoption, especially for healthcare. How MongoDB supports IoT challenges Here's a visual view of how MongoDB helps developers bring IoT applications to market faster: Scalability and efficient storage Global coverage and data synchronization High availability and scalability are built in via replication and native sharding. Online Archive automatically archives aged data to a fully managed cloud object storage, so you can optimize cost and performance without sacrificing data accessibility. Time series collections automatically optimize your schema for high storage efficiency, low-latency queries, and real-time analytics. MongoDB Atlas is a global, multi-cloud platform that lets your apps run anywhere in the world. Atlas Device Sync solves conflict resolution and keeps your data up to date across devices, users, and your backend, regardless of connectivity. Interoperability Security and privacy The document model provides a flexible schema and maps exactly to the objects that developers work with in their code. Different industry communication standards are being built over JSON, such as FHIR, which is a natural fit to MongoDB's document model. Thanks to MongoDB Client-side Field Level Encryption , data is encrypted in motion, in memory, and at rest. Queryable Encryption allows running expressive queries on fully randomized encrypted data. MongoDB provides the strongest levels of data privacy and security for regulated workloads. MongoDB Atlas takes care of the backend, removing friction from the development process and simplifying your technology stack, so you can focus on building differentiating features for your applications. Atlas is a developer data platform that supports a broad array of use cases, from operational to transactional and through analytical workloads. Atlas also offers the following features: Ability to service more loads of the data lifecycle: Enabling development teams to seamlessly analyze, transform, and move data while reducing reliance on batch processes or ETL jobs Built on a modern data model: Aligning to the way developers think and code Integrated: Delivering an elegant developer experience Figure 2. Atlas is a developer data platform built on three pillars: the document model, a unified interface for different data use cases, and a multi-cloud, enterprise-ready foundation. MongoDB for IoT-powered healthcare apps IoT and specifically wearables will play a major role in solving the two-fold challenge of delivering better quality care for increasingly larger populations of patients. The soaring adoption of wearables is accelerating the need for a developer data platform that helps software delivery teams build and manage health applications with: Scalable and efficient storage Global coverage and data synchronization Interoperability Security and privacy MongoDB Atlas is a developer data platform designed to manage the heavy lifting for you, by providing an elegant developer experience and unifying a broad range of data workloads with world-class privacy and security features. Read Part 1 of this series on connected healthcare data , and learn more about MongoDB Atlas and the healthcare industry .
Connected Healthcare Data: Interoperability to Solve Fragmentation and Drive Better Patient Outcomes
Many differences exist across healthcare systems around the globe, but there is one unfortunate similarity: fragmentation. Fragmentation is a consequence of the inability of various healthcare organizations (both public and private) to communicate with each other or to do so in a timely or consistent manner, and it can have a dramatic impact on patient and population well-being. Interoperability and communication A patient can visit a specialist for a specific condition and the family doctor for regular checkups, perhaps even on the same day. But how can both doctors make appropriate decisions if patient data is not shared between them? Fragmented healthcare delivery, as described in this scenario, also leads to data fragmentation. Such data fragmentation can cause misdiagnosis and services duplication. It can also lead to billing issues, fraud, and more, causing preventable harm and representing a massive economic burden for healthcare systems worldwide. To improve healthcare fragmentation, we need truly interoperable health data. The longitudinal patient record A longitudinal patient record (LPR) is a full, life-long view of a patient’s healthcare history and the care they’ve received. It’s an electronic snapshot of every interaction patients have, regardless of provider and service. Ideally, this record can be shared across any or all entities within a country’s healthcare system. The LPR represents a step beyond the electronic health record, extending past a specific healthcare network to a regional or national level. It’s critical that LPRs use the same data format and structure to guarantee the ability of healthcare providers to easily and quickly interact with them. Data standards for LPRs are key to interoperability and can help address healthcare fragmentation, which, in turn, can help save lives by improving care. FHIR Fast Healthcare Interoperability Resources (FHIR) is a commonly used schema that comprises a set of API and data standards for exchanging healthcare data. FHIR enables semantic interoperability to allow effective communication between independent healthcare institutions and essentially defines “how healthcare information can be exchanged between different computer systems regardless of how it is stored in those systems” ( ONC Fact Sheet, “What is FHIR?” ). FHIR aims to solve the fragmentation problem of the healthcare system by directly attacking the root of the problem: miscommunication. As is the case for many other modern communication standards (for example, ISO 20022 for finance ), FHIR builds its REST API from a JSON schema. This foundation is convenient, considering most modern applications are built with object-oriented programming languages that have JSON as the standard file and data interchange format. This approach also makes it easier for developers to build applications, which is perhaps the most important point: The future of healthcare delivery may increasingly depend on the creation of applications that will transform how patients and providers interact with healthcare systems for the better. MongoDB: FHIR and healthcare app-ification MongoDB is a document database and is therefore a natural fit for building FHIR applications. With JSON as the foundation of the MongoDB document model developers can easily store and retrieve data from their FHIR APIs to and from the database, with no translation or change of format needed. In fact, organizations can adopt FHIR resources as the basis of a new, canonical data model that existing internal systems can begin to shift and conform to. One example is the Exafluence FHIR API , which is built on top of MongoDB. Exafluence's API allows for real-time data interchange by leveraging Apache Kafka and Spark, in either an on-premise or multi-cloud deployment. Software teams leveraging the Exafluence solution have experienced velocity increases of their FHIR interoperability projects by 40% to 60% . MongoDB's tool set can develop value-add business solutions on the FHIR-native dataset — without ETL. Beyond FHIR , the trend toward healthcare app-ification (i.e., the increasing use of applications in healthcare) clashes with pervasive legacy architectures, which typically are not optimized for the developer experience. Because of this reliance on legacy architectures, modernization or transformation initiatives often fail to take hold or are postponed as companies perceive the risks to be too high and the return on investment is not evident. It doesn’t have to be this way, however. MongoDB’s industry-proven iterative approach to modernization reduces the risk of application and infrastructure migration and unlocks developer productivity and innovation. Interoperable, modern healthcare applications can now be built in a developer-friendly environment, with all the benefits expected from traditional databases (i.e., ACID transactions, expressive query language, and enterprise-grade security). MongoDB provides the freedom for solutions to be deployed anywhere (e.g., on-premises, multi-cloud), providing a major advantage for healthcare organizations, which typically have multi-environment deployments. Healthcare and the cloud Digital healthcare will accelerate the adoption of cloud technologies within the industry, enabling innovation at scale and unlocking billions of dollars in value. Healthcare organizations, however, have so far been reluctant to move workloads to the cloud, mostly because of data privacy and security concerns. To support such cloud adoption initiatives, MongoDB Atlas offers a unique multi-cloud data platform , integrating MongoDB in a fully managed environment with enterprise-grade security measures and data encryption capabilities. MongoDB Atlas is HIPPA-ready and a key facilitator for GDPR compliance. A holistic view of patient care Interoperable healthcare records and communication standards will make longitudinal patient records possible by providing a much-sought-after holistic view of the patient, helping to fix healthcare fragmentation. Many challenges still exist, including transforming legacy infrastructures into modern, flexible data platforms that can adapt to the exponential changes happening in the healthcare industry. MongoDB provides a developer data platform designed to unlock developer productivity and ultimately giving healthcare organizations the power to focus on what matters most: the patient. Read Part 2 of this series on connected healthcare data , and learn more about MongoDB Atlas and the healthcare industry .
MACH Aligned for Retail: Microservices
MACH is an approach to architecting modern applications through open tech ecosystems. It is an acronym representing Microservices, API-first, Cloud-native SaaS, and Headless. With the accelerating digitalization of retail experiences requiring new technology stacks that provide agility, flexibility, and performance at scale, MACH is especially relevant for retail and ecommerce , a far cry from current legacy, monolithic architectures. The MACH Alliance is an organization, of which MongoDB is a member, dedicated to educating and driving the adoption of the MACH framework and to “future proof enterprise technology and propel current and future digital experiences.” This is the first of a series of blog posts dedicated to MACH and how retail organizations are leveraging this framework to gain a competitive advantage. Let us begin with the first letter of MACH: microservices. Read the next post in this series, "MACH Aligned for Retail: API-First." What are microservices and why should I care? In simplest terms, microservices are an approach to building applications in which business functions are broken down into smaller, self-contained components called services. These services function autonomously and are usually developed and deployed independently. This independence means the failure or outage of one microservice will not affect another. Each service serves a particular business function or objective. The benefits of a microservices-based architecture are clear. The modular approach of microservices provides companies with quicker time to market and value, ultimately leading to a better customer experience. Development teams can work independently on different app functionalities, consequently shortening development cycles to get more features deployed in less time, which means the reaction to changing customer demands improves dramatically. Also, since services are deployed in independent environments, scalability concerns are managed in a much more convenient (and efficient) way, and resilience is strengthened significantly because there is no single point of failure, as there would be with monolithic applications. Microservices provide a modern architecture for app development, which ultimately delivers the best experience for customers. Learn how Boots modernized its stack with MongoDB and Microservices . Applying microservices for retail What does a microservice-based application look like in a real-world scenario? Let’s say an ecommerce application is being built. Microservices will greatly optimize the following challenges: Dynamic product catalog: An ecommerce app might involve a large number of products (maybe from different suppliers) with changing availability. With each supplier and/or product category as a part of a microservice, it becomes easier and more efficient to manage and provide an always up-to-date product catalog for users. Changing customer needs: A microservice-based architecture increases speed of development and testing, ultimately allowing new features to be deployed faster and enabling developers to quickly pivot to new customer needs. Different teams can work in parallel and independently, with little to no dependencies, rolling out or rolling back features as needed without risk. Scale flexibly: Independently scale app functionalities up during peaks or down for valleys with on-demand cloud-based microservices. The world before microservices Before microservices were an option, the typical data infrastructure would look like a data access layer on top of a database in order to get all the datasets containing information needed for running the application, as seen in Figure 1. There would be many databases to pull data from and various information silos, making for a painful process. Business logic had to be generated to transform these datasets to perform specific functions, namely a product catalog, cart, checkout, payments, and the like. Before building any application, the relational data objects would need to be mapped out to match an object-oriented programming paradigm. Figure 1: The monolithic application diagram before microservices This is not easily scalable or flexible for modern applications because every change in a dataset needs to be pushed upstream, and every new feature request for the app implies a data schema change downstream. This complicates life for developers and makes adaptation to customer needs a nightmare. For a deeper look into technical details about microservices, check out MongoDB’s specific guides dedicated to this topic. Decoupled app functionality with microservices With microservices, business functions are decoupled as much as possible in order to create a bounded context that is clearly independent of the others, meaning a failure or outage in one does not affect the others. This often means having a separate database per service, as seen in Figure 2. Figure 2: A first approach to microservices In this first approach to microservices, decoupled application functionalities can be developed, maintained, and scaled independently. However, having a separate database for each business functionality is not the ideal. It adds operational complexity, defeating the purpose of a microservices approach since maintaining and scaling a distributed system is not a simple task. But there is light in all of this: a middle ground between strong decoupling and operational efficiency can be found with MongoDB. MongoDB and microservices MongoDB is built under a number of fundamental technology principles that ensure companies can reap the advantages of microservices, specifically around a flexible data model, redundancy, automation, and scalability. MongoDB can be deployed in any environment (on-premises or cloud for example), but all industries are moving or have already moved toward the cloud, with its lower cost of ownership and flexibility. Retail is no exception. The cloud is again the natural next step for microservices. It provides dynamic scalability and high availability, freeing teams of the operational burden of maintaining a distributed system in-house. This is why MongoDB clients are choosing MongoDB Atlas as their cloud database-as-a-service to deploy applications based on microservices. As a step to modernization , MongoDB can be used as an operational data store, as seen in Figure 3. Legacy data silos are needfully connected via change data capture (CDC) and/or ETL processes in order to have an up-to-date copy of the data, stored as JSON documents. This is a first major advantage, since now applications can be developed against a data model that fits how developers think and code, therefore providing unprecedented agility to the development cycle. Figure 3: Microservices with MongoDB, acting as an operational data Store. Applications can be developed taking advantage of its flexible data model and scalability MongoDB Atlas allows for all of the benefits and flexibility of a fully managed, API-driven database. It allows for environment automation without dealing with every detail of database operation and scalability. This makes development teams more agile so that they can evolve applications at the pace customers expect and require today. Read the next post in this series, "MACH Aligned for Retail: API-First."