Connected Healthcare Data: Interoperability to Solve Fragmentation and Drive Better Patient Outcomes

Jeff Needham and Miquel Nieto
May 26, 2022 | Updated: July 25, 2022

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.

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From Tamagotchi Pets to IoT Factories: Digging In at MongoDB World’s Builder’s Fest

Everyone loves to build — whether it’s a child playing with LEGO Bricks or a startup founder building an app from scratch. At MongoDB World 2022 , attendees will have the chance to build something truly unique. Builder’s Fest, which takes place on June 9, 2022, at MongoDB World in New York, gives attendees the opportunity to get involved in hands-on workshops and coding competitions. The event will help developers learn how to master features of MongoDB — and also have a lot of fun. “Builder’s Fest is a place where builders get together and feed off each other’s vibes and collaborate,” says Karen Huaulme, a principal developer advocate at MongoDB. “People are coming in from all different levels; we have something for everyone.” A session at Builder’s Fest at MongoDB World 2019. “At Builder’s Fest, we get our hands dirty and see MongoDB in action,” says MongoDB principal consulting engineer Dawid Esterhuizen. MongoDB experts will lead workshops and coding competitions to showcase their work, show off their skill sets, and reveal their secret passions. After more than two years of remote conferences, these engineers are excited to build something in person, together with colleagues and peers. After delivering his keynote earlier in the week, MongoDB CTO Mark Porter will host four sessions during Builder’s Fest. Porter’s four sessions are: MongoDB’s Architectural Advantages , Safe Software Deployments , Engineering Culture at MongoDB , and Is Relational the New COBOL? Builder’s Pods Attendees at Builder's Fest 2019 gather around to share different ideas with each other. Much of the action at Builder’s Fest takes place in the Builder’s Pods, which will be spread throughout MongoDB World’s Partner Promenade. The Pods are set up for hands-on learning and tutorials, and they will host the mini workshops led by MongoDB experts and others. David Bradford is an engineer at MongoDB who is hosting a session in the Builder’s Pod. “For Builder’s Fest, I’m really excited to see the breadth of ways that MongoDB can be used to build unique and novel tooling and solutions,” he says. Bradford’s session will detail how to use MongoDB to export Git history. “I’m looking forward to showing off some exploration I have been doing around leveraging MongoDB to explore trends and patterns hidden in Git repositories,” Bradford says. “I’m excited to be able to show how features like the aggregation framework and MongoDB Atlas Charts can be used to quickly build powerful analysis tools.” During 2019’s Builder’s Fest, some of the Builder’s Pod topics included using MongoDB Atlas and Stitch, getting a Raspberry Pi to send IoT data to MongoDB, and creating visualizations using MongoDB Charts. New sessions this year will begin every half hour at each pod around the space. Workshops will cover a wide spectrum, from building custom Tamagotchi hardware to tinkering with our model-size IIoT smart factory. John Page, a distinguished engineer at MongoDB, is hosting the session on building custom Tamagotchi hardware. “I'm excited to show off my passion projects and to introduce people to the joy of coding for tiny computers,” Page says. “If you’ve never programmed hardware directly before, you get a chance to try it.” Says Dawid Esterhuizen, a principal consulting engineer at MongoDB: “I plan to visit the IoT and Tamagotchi pods as my first stop. I like getting involved in the technical bits," he says, "and at Builder’s Fest we get our hands dirty and see MongoDB in action.” The security topics are always of interest, Esterhuizen says, as well as the various coding challenges. At Builder’s Fest, there really is something for everyone. On two large stages at either end of the Partner Promenade, MongoDB World 2022 participants can take part in coding challenges and gaming competitions. Attendees can go head-to-head and show off their skills. Coding experts and gamers will not want to miss out on this electric activation on Day 3 of MongoDB World. Builder’s Fest will run from 11 a.m. to 3 p.m inside the Partner Promenade. Be sure to check out the full Builder’s Fest agenda within the MongoDB World app for iOS or Android to find workshops that are right for you. Register today for MongoDB World, and use code ​​MDBW22BLOG to save 25% off your tickets. We hope to see you in NYC from June 7 to June 9!

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Transforming Industries with MongoDB and AI: Healthcare

This is the sixth in a six-part series focusing on critical AI use cases across several industries . The series covers the manufacturing and motion, financial services, retail, telecommunications and media, insurance, and healthcare industries. In healthcare, transforming data into actionable insights is vital for enhancing clinical outcomes and advancing patient care. From medical professionals improving care delivery to administrators optimizing workflows and researchers advancing knowledge, data is the lifeblood of the healthcare ecosystem. Today, AI emerges as a pivotal technology, with the potential to enhance decision-making, improve patient experiences, and streamline operations — and to do so more efficiently than traditional systems. Patient experience and engagement While they may not expect it based on past experiences, patients crave a seamless experience with healthcare providers. Ideally, patient data from healthcare services, including telehealth platforms, patient portals, wearable devices, and EHR, can be shared – securely – across interoperable channels. Unfortunately, disparate data sources, burdensome and time-consuming administrative work for providers, and overly complex and bloated solution stacks at the health system level all stand in the way of that friction-free experience. AI can synthesize vast amounts of data and provide actionable insights, leading to personalized and proactive patient care, automated administrative processes, and real-time health insights. AI technologies, such as machine learning algorithms, natural language processing, and chatbots, are being used to enhance and quantify interactions. Additionally, AI-powered systems can automatically schedule appointments, send notifications, and optimize clinic schedules, all reducing wait times for patients. AI-enabled chatbots and virtual health assistants provide 24/7 support, offering instant responses, medication reminders, and personalized health education. AI can even identify trends and predict health events, allowing for early intervention and reduction in adverse outcomes. MongoDB’s flexible data model can unify disparate data sources, providing a single view of the patient that integrates EHRs, wearable data, and patient-generated health data for personalized care and better patient outcomes. For wearables and medical devices, MongoDB is the ideal underlying data platform to house time series data, significantly cutting down on storage costs while enhancing performance. With Atlas for the Edge, synchronization with edge applications, including hospital-at-home setups, becomes seamless. On the patient care front, MongoDB can support AI-driven recommendations for personalized patient education and engagement based on the analysis of individual health records and engagement patterns, and Vector Search can power search functionalities within patient portals, allowing patients to easily find relevant information and resources, thereby improving the self-service experience. Enhanced clinical decision making Healthcare decision-making is critically dependent on the ability to aggregate, analyze, and act on an exponentially growing volume of data. From EHRs and imaging studies to genomic data and wearable device data, the challenge is not just the sheer volume but the diversity and complexity of data. Healthcare professionals need to synthesize information across various dimensions to make informed, real-time, accurate decisions. Interoperability issues, data silos, lack of data quality, and the manual effort required to integrate and interpret this data all stand in the way of better decision-making processes. The advent of AI technologies, particularly NLP and LLMs, offers transformative potential for healthcare decision-making by automating the extraction and analysis of data from disparate sources, including structured data in EHRs and unstructured text in medical literature or patient notes. By enabling the querying of databases using natural language, clinicians can access and integrate patient information more rapidly and accurately, enhancing diagnostic precision and personalizing treatment approaches. Moreover, AI can support real-time decision-making by analyzing streaming data from wearable devices, alerting healthcare providers to changes in patient conditions that require immediate attention. MongoDB, with its flexible data model and powerful data development platform, is uniquely positioned to support the complex data needs of healthcare decision-making applications. It can seamlessly integrate diverse data types, from FHIR-formatted clinical data to unstructured text and real-time sensor data, in a single platform. By integrating MongoDB with Large Language Models (LLMs), healthcare organizations can create intuitive, AI-enhanced interfaces for data retrieval and analysis. This integration not only reduces the cognitive load on clinicians but also enables them to access and interpret patient data more efficiently, focusing their efforts on patient care rather than navigating complex data systems. MongoDB's scalability ensures that healthcare organizations can manage growing data volumes efficiently, supporting the implementation of AI-driven decision support systems. These systems analyze patient data in real-time against extensive medical knowledge bases, providing clinicians with actionable insights and recommendations, thereby enhancing the quality and timeliness of care provided. MongoDB's Vector Search further enriches decision-making processes by enabling semantic search across vast datasets directly within the database. This integrated approach enables the application of pre-filters based on extensive metadata, enhancing the efficiency and relevance of search results without the need to synchronize with dedicated search engines or vector stores, meaning healthcare professionals can utilize previously undiscoverable insights, streamlining the identification of relevant information and patterns. Clinical trials and precision medicine The need for innovation and transformation isn’t just limited to the patient-provider-healthcare system experience. The challenges of conducting clinical trials and advancing precision medicine are significant, from identifying and enrolling suitable participants to data management practices are fraught with the potential for errors, compromising the accuracy and reliability of trial outcomes. Moreover, the traditional one-size-fits-all approach to treatment development fails to address the unique genetic makeup of individual patients, limiting the effectiveness of therapeutic interventions. AI can make clinical trials faster and treatments more personalized. It's like having a super-smart assistant that can quickly find the right people for studies, keep track of all the data without making mistakes, and even predict which medicines will work best for different people. This means doctors can create safe, efficient treatments that fit you perfectly, just like a tailor-made suit. Plus, with AI's help, these custom treatments can be developed quicker and be more affordable, bringing us closer to a future where everyone gets the care they need, designed just for them. It's a big step towards making medicine not just about treating sickness but about creating health plans that are as unique as patients are. MongoDB plays a pivotal role in modernizing clinical trials and advancing precision medicine by addressing complex data challenges. Its flexible data model excels in integrating diverse data types, from EHRs and genomic data to real-time patient monitoring streams. This capability is crucial for clinical trials and precision medicine, where combining various data sources is necessary, sometimes through a project purpose ODL, to develop a comprehensive understanding of patient health and treatment responses. For clinical trials, MongoDB can streamline participant selection by efficiently managing and querying vast datasets to identify candidates who meet specific criteria, significantly reducing the recruitment time. Its ability to handle large-scale, complex datasets in real-time also facilitates the dynamic monitoring of trial participants, enhancing the safety and accuracy of trials. Other notable use cases Patient Flow Optimization and Emergency Department Efficiency: AI algorithms can process historical and real-time data to forecast patient volumes, predict bed availability, and identify optimal staffing levels, enabling proactive resource allocation and patient routing. Virtual Health Assistants for Chronic Disease Management: Utilizing AI-powered virtual assistants to monitor patients' health status, provide personalized advice, and support medication adherence for chronic conditions such as diabetes and hypertension. AI-Enhanced Digital Pathology and Medical Imaging: Build modern VNA (Vendor Neutral Archive and Digital pathology solutions with innovative approaches, dealing with interoperable data, and manage extensive metadata associated with all your resources enabling fast findings and automated annotations. Operational Efficiency in Hospital Resource Management: Implementing AI to optimize hospital operations, from staff scheduling to inventory management, ensuring resources are used efficiently and patient care is prioritized. Learn more about AI use cases for top industries in our new ebook, How Leading Industries are Transforming with AI and MongoDB Atlas .

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