FHIR Technology is Driving Healthcare's Digital Revolution
Technology supporting healthcare’s digital transformation is so pervasive that the question isn’t what technology to choose, but rather, what problems need to be solved. Advancing technology and access to secure and real-time data analytics will vastly improve patients’ health and happiness, and growing interoperability standards are pushing organizations forward in their digital transformations. Together with the Healthcare Information and Management Systems Society (HIMSS) and leading healthcare insurance provider Humana , MongoDB recently released a three-part podcast series chronicling the ways Fast Healthcare Interoperability Resources (FHIR), AI, and the cloud are reshaping healthcare for the better. Here’s a quick roundup of our discussions. Data is the future of healthcare . Whether providers are driving patient engagement through wearable devices, wellness programs or connected care, data will take healthcare to the next digital frontier. We’ll see these advancements through AI, FHIR, and the cloud. FHIR is revolutionizing healthcare technology . Not only is FHIR implementation a requirement, it’s also a crossroads for data architects. Choosing the right approach has deep implications for healthcare IT. The operational data layer (ODL) approach to interoperability makes the impossible possible . Through Humana’s digital transformation journey, it became clear that meaningful progress isn’t possible using core legacy database systems. AI, FHIR, and the cloud: Why data is the future of healthcare In this episode , we dive into what a digital transformation would look like for the healthcare industry, and what are some of the biggest technology challenges facing healthcare today. A digitally transformed healthcare industry will weave real-time data analytics with more personalized care. Patients today want a more modern healthcare experience that includes telemedicine, digital forms and touchless mobile check ins. The end goal is simple: maximize the human experience while advancing away from legacy technology systems that slow down both healthcare practitioners and patients. When it comes to today’s biggest healthcare challenges, the cloud stands out as a key driver of promise and peril. The promise is that we can build applications, go to market and reach patients through wellness programs more quickly. The peril lies in the infrastructure, which is unknown to many healthcare organizations. This presents a unique challenge for the architects and certainly the developers at organizations with older legacy systems. The challenge here is avoiding a simple left hand shift or cloud for the sake of cloud, and moving from simple modernization to actual transformation. Listen below to hear the entire conversation Your browser does not support the audio element. Bring the FHIR inside for digital transformation In episode 2 , HIMSS and MongoDB take a closer look at why FHIR is a change agent in healthcare technology, and how healthcare organizations globally are using the new data standard to jump start legacy modernization and digital transformation. What is FHIR? The FHIR standard is a common set of schema definitions and APIs that helps providers and patients manage and exchange healthcare data. Using FHIR, records provided by healthcare organizations are standardized into a common data model over rest-based APIs. It makes the data that healthcare providers and payers use easier to exchange. Growing regulatory pressure has accelerated U.S. FHIR adoption among healthcare organizations and technology vendors.The Centers for Medicare and Medicaid Services (CMS) started a rolling deadline for FHIR compliance in 2020, with fines for institutions that fall behind. As a result, for most U.S.-based healthcare providers, payers, and their technology vendors, the past few years were a headlong race to adopt FHIR. Here are three reasons why FHIR is hugely significant for healthcare technology leaders: It’s a federal mandate from the Centers for Medicare & Medicaid Services. It’s a complex data integration challenge. Legacy systems built before the mid 2010s are not interoperable with the FHIR mandate. FHIR implementation approaches For large organizations with huge data requirements, data architects can experience paralysis from the sheer volume of legacy systems to unwind. These groups have all of their patients’ electronic healthcare record information, payer information and more bound up in legacy systems, none of which is interoperable with FHIR. The second challenge is cloud migration, which can be skirted by organizations using a checkbox compliance approach. In those cases, API layers are used to ingest and serve data to legacy systems, but are not really integrated with the legacy system in real time. The most successful approach to tackling this challenge is not to rewrite, unwind or replace legacy systems completely, but keep them contained. We recommend bringing in an operational data layer that exposes the information in the legacy system and keeps it in sync with the legacy system, but then lands it in an ODL in the FHIR standard. With the FHIR API, patients and providers can interact with data in real time and access records in milliseconds after a diagnosis. Real-time records synced with legacy systems and patients’ private data is protected. Delve into the full conversation below Your browser does not support the audio element. FHIR and the future of healthcare at Humana You don't have to take the rip and replace approach when modernizing your legacy systems with an ODL method. This was a key to successful modernization for Humana, as discussed in the third and final episode in our series. For large enterprises that may have decades’ worth of acquired legacy systems, often pulling similar datasets from disparate databases, the pursuit of modernized interoperability begins to look like an impossible task. Listen to the final episode of our podcast series to here how Humana’s ODL approach met the company’s data velocity requirements, and next steps for personalized healthcare and interoperability at Humana. Listen to the entire conversation below Your browser does not support the audio element. More related FHIR and healthcare resources [ White paper ] Bring the FHIR Inside: Digital Transformation Without the Rip and Replace [ On-demand webinar ] Building FHIR Applications with MongoDB
Drowning in Data: Why It's Time to End the Healthcare Data Lake
From digital check-ins, to connected devices and telehealth programs, patients expect the benefits of a more digitized healthcare experience. At the same time, they’re also demanding a more personalized approach from healthcare providers. This duality - the need to provide a more convenient experience with one that’s more tailored to the patient - is fueling a wave of technology modernization efforts and the replacement of monolithic legacy IT systems. With limited re-use outside of the context they were built for and a reliance on nightly batch processing, legacy IT systems fail to deliver the services healthcare IT teams need or provide the experiences patients demand. Modernization should come with a move to microservices that can be used by multiple applications, agile teams that embrace domain driven design principles, and event busses like Kafka to deliver real-time data and functionality to users. While this transformation is occurring, there’s an 800lb gorilla not being widely addressed. Analytics. What the healthcare industry doesn’t want to talk about, is how costly analytics has become; the people, the software, the infrastructure, and particularly how difficult it is to move data in and out of data lakes and warehouses. It's hindering the industry’s ability to deliver insights to patients and providers in a timely and efficient manner. And yet, so many organizations are modernizing their analytics data warehouses and data lakes with an approach that simply updates the underlying technology. It’s a lift-and-shift effort of tremendous scale and cost, but one that is not addressing the underlying issues preventing the speedy delivery of meaningful insights. Drowning in data: A 1980s model in the 2020s While the business application landscape has changed, healthcare is still clinging to the same 1980’s paradigm when it comes to analytics data. It started by physically moving all the data from transactional systems into a single data warehouse or data lake (or worse, both), so as not to disrupt the performance of business applications by executing analytics queries against the transactional database. Eventually, as data warehouses had enough relational tables and data in them, queries began to slow down, and even time-out before delivering results to end users. This gave rise to data marts, yet another database to copy the warehouse data into, using a star schema model to return query results more efficiently than in the relational warehouse. In the last and current iteration of analytics data platforms, warehouses and data marts became augmented, and were even replaced in some cases, with data lakes. Technologies like Hadoop promised a panacea where all sorts of structured and unstructured data could be stored, and where queries against massive datasets could be executed. In reality it turned out to be a costly distraction, and one that did not make an organization's data easier to work with, or provide real-time data insights. Hence why it earned the nickname “data jail”. It was hard to load data into, and even harder to get data out of. New technology, same challenges While Hadoop and other technologies did not last long, they hung around just long enough to negatively alter the trajectory of many analytics shops, which are now investing heavily in migrating away from Hadoop, to cloud-based platforms. But, are these cloud alternatives solving the challenges of the Hadoop era? Can your organization rapidly experiment, innovate and serve up data insights from your data lake? Can you go from an idea to delivery in days? Or, is it weeks, months even? Despite the significant amounts of time, money and people required to load data into these behemoth cloud data stores, they still exhibit the same challenges as their Hadoop-era predecessors. They are difficult to load and even more difficult to make changes to. They can never realistically offer real-time or even near-real-time processing, the response time that patients and providers expect. Worse, they contain so much data, that making sense of it is a task often left to either a sophisticated add-on like AWS HealthLake, or specialized data engineering and data science teams. To add to this, the cloud based analytics systems are typically managed by a single team that’s responsible for collecting, understanding and storing data from all of the different domains within an organization. This is what we like to call a modernized monolith, the pairing of updated technology with a failure to fundamentally address or improve the overall limitations or constraints of a system or process. It’s an outdated and inefficient approach that’s simply been “lifted and shifted” from one technology to another. Many data lake implementations take a modernized monolithic approach which, like their predecessors, results in a bottleneck and difficulty in getting information out, once it goes in. In a world where data is at the center of every innovative business, and real-time analytics is top-of-mind for executives, product owners and architects alike, most data lakes don’t deliver. Transforming your organization into a data-driven enterprise requires a more agile approach to managing and working with ever-growing sums of data. The rise of the operational data layer — an ODS renaissance To provide meaningful insights to patients in a timely and efficient manner, two very important things need to happen. Healthcare organizations need to overcome the limitations of legacy systems, and they need to make sense of a lot of very complex data. A lift-and-shift approach migrating data into a data lake will not solve these problems. In addition, it’s not feasible or advisable to spend tens, or even hundreds of millions of dollars to replace legacy systems as a precursor to a digital engagement strategy. The competition will leap-frog you before your efforts are even half complete. So, what can be done? Can your organization make better sense of its data, and at the same time mitigate the issues legacy systems impose? Can this be done without a herculean effort? The answer is yes. The solution is an operational data layer (ODL) , formerly known as the operational data store. It’s a method that’s been tried and tested by major corporations, and is the underlying technology that powers many of the apps you interact with on your phone. An ODL lets you build new features without existing system limitations. It lets you summarize, analyze, and respond to data events, in real-time. It helps you migrate from legacy systems, without incurring the cost and complexity of replacing legacy systems. It can give your teams the speed and agility that working against a data lake will simply never have. Data lakes and warehouses have their place, and the kinds of long-term data insights and data science benefits that can be gleaned from them are significant. The challenge, however, is reacting in real-time, and serving those insights to patients, quickly. An ODL strategy offers the best, most cost and time efficient approach to mitigate legacy system issues, without the pain of replacing legacy systems. Investing in an ODL strategy will both solve your legacy modernization dilemma, and it will help you deliver real-time data and analytics at the speed of an agile software delivery team. MongoDB is an ideal ODL provider . Not only does it have the underlying, flexible document-based database, but it is also an application data platform, empowering your developers to focus on building features, not managing databases and data. If you’re interested in learning about how MongoDB has enabled organizations large and small to successfully implement ODL strategies and tackle other burning healthcare issues, click here .