Building AI with MongoDB: How Flagler Health's AI-Powered Journey is Revolutionizing Patient Care

Angela Lee
February 7, 2024 | Updated: February 21, 2024
#genAI#Vector Search

Flagler Health is dedicated to supporting patients with chronic diseases by matching them with the right physician for the right care. Typically, patients grappling with severe pain conditions face limited options, often relying on prolonged opioid use or exploring costly and invasive surgical interventions. Unfortunately, the latter approach is not only expensive but also has a long recovery period. Flagler finds these patients and triages them to the appropriate specialist for an advanced and comprehensive evaluation.

Diagram of the current state without Flagler. The diagram shows the categories of Physical therapy, payor, substance use disorder, mental health, care management, and physician all interacting with each other with the Physician at the focal point of all the interactions.
Current state without Flagler

Flagler Health employs sophisticated AI techniques to rapidly process, synthesize, and analyze patient health records to aid physicians in treating patients with advanced pain conditions. This enables medical teams to make well-informed decisions, resulting in improved patient outcomes with an accuracy rate exceeding 90% in identifying and diagnosing patients.

As the company built out its offerings, it identified the need to perform similarity searches across patient records to match conditions. Flagler’s engineers identified the need for a vector database but found standalone systems to be inefficient. They decided to use MongoDB Atlas Vector Search. This integrated platform allows the organization to store all data in a single location with a unified interface, facilitating quick access and efficient data querying.

Diagram of what Flagler can offer. The Flagler Health Tech Platform puts the sectors of Physical Therapy, Care Management, Dietitian, Mental Health, and Substance Use Disorder into one platform that the Physician then interacts with.
What Flagler can offer

Will Hu, CTO, and Co-founder of Flagler Health, emphasizes the importance of a flexible database that can evolve with the company's growth. A relational model was deemed too rigid, leading the company to choose MongoDB's document model. This flexibility allows for easy customization of client configuration files, streamlining data editing and evolution. The managed services provided on MongoDB's developer data platform save time and offer reliability at scale throughout the development cycle.

Flagler Health collaborates with many clinics, first processing millions of electronic health record (EHR) files in Databricks and transforming PDFs into raw text. Using the MongoDB Spark Connector and Atlas Data Federation, the company seamlessly streams data from AWS S3 to MongoDB. Combined with the transformed data from Databricks, Flagler’s real-time application data in MongoDB is used to generate accurate and personalized treatment plans for its users. MongoDB Atlas Search facilitates efficient data search across Flagler Health's extensive patient records. Beyond AI applications, MongoDB serves critical functions in Flagler Health's business, including its web application and patient engagement suite, fostering seamless communication between patients and clinics.

This comprehensive application architecture, consolidated on MongoDB's developer data platform, simplifies Flagler Health's operations, enabling efficient development and increased productivity. By preventing administrative loops, the platform ensures timely access to potentially life-saving care for patients.

Looking ahead, Flagler Health aims to enhance patient experiences by developing new features, such as a digital portal offering virtual therapy and mental health services, treatment and recovery tracking, and a repository of physical therapy videos. Leveraging MongoDB’s AI Innovators program for technical support and free Atlas credits, Flagler Health is rapidly integrating new AI-backed functionalities on the MongoDB Atlas developer data platform to further aid patients in need.

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MongoDB Enterprise Advanced in Google Distributed Cloud Hosted

Today, we’re excited to strengthen our "run anywhere" approach and deepen our relationship with Google Cloud by announcing that MongoDB Enterprise Advanced is now available for use within Google Distributed Cloud Hosted (GDC Hosted). "Google Cloud is happy to welcome MongoDB as a preferred partner for our Google Distributed Cloud Hosted product," said Rohan Grover, Director of Product for GDC Hosted. "MongoDB's powerful document database aligns with our data analytics focus, empowering our shared customers to unlock the full potential of their sensitive data in an air-gapped private cloud." GDC Hosted is Google Cloud’s air-gapped private cloud that does not require connectivity to Google Cloud or the public internet to manage the infrastructure, services, APIs, or tooling. GDC Hosted enables public sector organizations and regulated enterprises to address strict data residency and security requirements, while continuing to deliver innovation to their users. MongoDB Enterprise Advanced combines the power of MongoDB —– the leading NoSQL, document-oriented database that supports a variety of data structures — with an industry-leading offering catering to customers with the most advanced security and data sovereignty needs. As a flexible and scalable solution, MongoDB allows diverse datasets to be stored in a schemaless format, ensuring easy data manipulation and real-time analytics. Together, GDC Hosted and MongoDB Enterprise Advanced offer a solution that enables users to scale their operations while adhering to the strictest data governance and security standards. The bridge between GDC Hosted and MongoDB Enterprise Advanced is Kubernetes : GDC Hosted is built on Kubernetes allowing teams to self-manage MongoDB through the use of the MongoDB Enterprise Kubernetes Operator. The MongoDB Enterprise Kubernetes Operator is the only officially supported way to run Enterprise Advanced deployments of MongoDB in Kubernetes. To enable customers to manage deployments within their environment of choice (GDC Hosted in this case), the operator works in conjunction with the MongoDB self-hosted Ops Manager, which the operator can also install and manage in Kubernetes. This gives customers the ability to deploy, monitor, back up, and scale MongoDB. The Enterprise Operator drastically simplifies both the setup and day-two operations like upgrades, making it possible to run MongoDB in Kubernetes with far less Kubernetes expertise. Creation and configuration of database deployments can be managed via a Git repo, saving developers from needing the permissions or knowledge needed to work directly with Kubernetes. By leveraging the Enterprise Kubernetes Operator, users can manage their MongoDB deployments with even greater power and scale, and maximize their investment in both MongoDB and Google Cloud. GDC Hosted is built to meet high regulatory, durability, and availability requirements, which aligns with MongoDB Enterprise Advanced’s commitment to giving users the tools and support they need to have complete control over the management and security of their self-managed MongoDB environments. While MongoDB Atlas is the best way to run MongoDB on Google Cloud, MongoDB Enterprise Advanced in GDC Hosted is the best option for teams that need absolute self-managed control over data governance and compliance, while still allowing for scalability. Once you have GDC Hosted up and running, you can get started with MongoDB Enterprise Advanced through the MongoDB Enterprise Advanced listing in the GDC Hosted Marketplace. Alternatively, teams can access MongoDB Enterprise Advanced through the Google Cloud Platforms Marketplace. MongoDB customers who want to get started using Enterprise Advanced in their GDC Hosted environments will need to sign up for a MongoDB Enterprise Advanced license through MongoDB first. For more information, reach out to gcp@mongodb.com . To learn more about the Enterprise Kubernetes Operator, visit our documentation . To learn more about Enterprise Advanced, visit our product page or download the latest version .

February 6, 2024
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The Converged AI and Application Datastore for Insurance

In the inherently information-driven insurance industry, companies ingest, analyze, and process massive amounts of data, requiring extensive decision-making. To manage this, they rely on a myriad of technologies and IT support staff to keep operations running smoothly but often lack effectiveness due to their outdated nature. Artificial intelligence (AI) holds great promise for insurers by streamlining processes, enhancing decision-making, and improving customer experiences with significantly less time, resources, and staff compared with traditional IT systems. The convergence of AI and innovative application datastores is transforming how insurers work with data. In this post, we’ll look at how these elements are reshaping the insurance industry and offering greater potential for AI-powered applications, with MongoDB at the heart of the converged AI and application datastore. Scenario planning and flexible data layers One of the primary concerns for IT leaders and decision-makers in the insurance industry is making smart technology investments. The goal is to consolidate existing technology portfolios, which often include a variety of systems like SQL Server, Oracle, and IBM IMS. Consolidation helps reduce inventory and prepare for the future. But what does future-proofing really look like? Scenario planning is an effective strategy for future-proofing. This involves imagining different plausible futures and investing in the common elements that remain beneficial across all scenarios. For insurance companies, a crucial common thread is the data layer. By making data easier to work with, companies can ensure that their technology investments remain valuable regardless of how future scenarios unfold. MongoDB’s flexible developer data platform offers a distinct architectural advantage by making data easier to work with, regardless of the cloud vendor or AI application in use. This flexibility is vital for preparing for disruptive future scenarios, whether they involve regulatory changes, market shifts, or technological advancements. Watch now: The Converged AI and Application Datastore: How API's, AI & Data are Reshaping Insurance The role of AI and data in insurance Generative AI is revolutionizing the insurance sector, offering new ways to manage and utilize data. According to Celent's 2023 Technology Insight and Strategy Survey, 33% of companies across different industries have AI projects in planning, 29% in development, and 19% in production (shown in Figure 1 below). This indicates a significant shift towards AI-driven solutions by insurers actively experimenting with gen AI. Figure 1: Celent Technology Insight and Strategy Survey 2023 However, there's tension between maintaining existing enterprise systems and innovating with AI. Insurance companies must balance keeping the lights on with investing in AI to meet the expectations of boards and stakeholders. The solution lies in integrating AI in a way that enhances operational efficiency without overwhelming existing systems. However, data challenges need to be addressed to achieve this, specifically around access to data. According to a Workday Global Survey , only 4% of respondents said their data is fully accessible, and 59% say their enterprise data is somewhat or completely siloed. Without a solid data foundation, insurers will struggle to achieve the benefits they are looking for from AI. Data architectures and unstructured data When adopting advanced technologies like AI and ML, which require data as the foundation, organizations often grapple with the challenge of integrating these innovations into legacy systems due to their inflexibility and resistance to modification. A robust data architecture is essential for future-proofing and consolidating technology investments. Insurance companies often deal with a vast amount of unstructured data, such as claim images and videos, which can be challenging to manage. By leveraging AI, specifically through vector search and large language models, companies can efficiently process and analyze this data. MongoDB is ideal for managing unstructured data due to its flexible, JSON-like document model, which accommodates a wide variety of data types and structures without requiring a predefined schema. Additionally, MongoDB’s flexibility enables insurers to integrate seamlessly with various technologies, making it a versatile and powerful solution for unstructured data management. For example, consider an insurance adjuster assessing damage from claim photos. Traditionally, this would require manually reviewing each image. With AI, the photos can be converted into vector embeddings and matched against a database of similar claims, drastically speeding up the process. This not only improves efficiency but also enhances the accuracy of assessments. The converged AI and application datastore with MongoDB Building a single view of data across various systems is a game-changer for the insurance industry. Data warehouses and data lakes have long provided single views of customer and claim data, but they often rely on historical data, which may be outdated. The next step is integrating real-time data with these views to make them more dynamic and actionable. A versatile database platform plays a crucial role in this integration. By consolidating data into a single, easily accessible view, insurance companies can ensure that various personas, from underwriters to data scientists, can interact with the data effectively. This integration allows for more responsive and informed decision-making, which is crucial for staying competitive in a rapidly evolving market. This can be achieved with a converged AI and application datastore, as shown in Figure 2 below. This is where operational data, analytics insights, and unstructured data become operationally ready for the applications that leverage AI. Figure 2: Converged AI and application datastore reference architecture The convergence of AI, data, and application datastores is reshaping the insurance industry. By making smart technology investments, leveraging AI to manage unstructured data, and building robust data architectures, insurance companies can future-proof their operations and embrace innovation. A versatile and flexible data platform provides the foundation for these advancements, enabling companies to make their data more accessible, actionable, and valuable. The MongoDB Atlas developer data platform puts powerful AI and analytics capabilities directly in the hands of developers and offers the capabilities to enrich applications by consolidating, ingesting, and acting on any data type instantly. Because MongoDB serves as the operational data store (ODS)—with its flexible document model—insurers can efficiently handle large volumes of data in real-time. By integrating MongoDB with AI/ML platforms, insurers can develop models trained on the most accurate and up-to-date data, thereby addressing the critical need for adaptability and agility in the face of evolving technologies. With built-in security controls across all data, whether managed in a customer environment or through MongoDB Atlas, a fully managed cloud service, MongoDB ensures robust security with features such as authentication (single sign-on and multi-factor authentication), role-based access controls, and comprehensive data encryption. These security measures act as a safeguard for sensitive data, mitigating the risk of unauthorized access from external parties and providing organizations with the confidence to embrace AI and ML technologies. If you would like to learn more about the convergence of AI and application datastores, visit the following resources: Video: The Converged AI and Application Datastore: How API's, AI & Data are Reshaping Insurance Paper: Innovation in Insurance with Artificial Intelligence The MongoDB Solutions Library is curated with tailored solutions to help developers kick-start their projects

July 18, 2024