Why FHIR offers a gateway to healthcare transformation
Current work on the standard to enable data exchange among industry participants can lay the groundwork to improve many aspects of care.
The healthcare sector is experiencing a remarkable transformation, driven by an explosion in data generation.
With a compound annual growth rate of nearly 36 percent, healthcare data is amassing at a zettabyte scale, encompassing a wide array of sources, from mobile apps and wearable devices to cloud computing and AI technologies. This vast reservoir of data, which includes both clinical and claims information, represents a digital rendering of global health trends and lifestyles.
At the heart of this transformation is the Fast Healthcare Interoperability Resources (FHIR) standard, which has emerged as a pivotal framework for exchanging health data within the cloud that has been largely untapped. Developed by Health Level Seven International (HL7), FHIR leverages open standards, such as JSON, OAuth and REST, making it an extensible and widely applicable tool.
FHIR’s adoption is supported by various stakeholders, including bipartisan federal legislation, international health communities and venture capital-backed tech companies. FHIR network effects are poised to provide critical data interoperability between payers, providers and researchers, enabling the sharing of clinical and claims data intelligence that will power transformative applications, such as prior authorization, care management and quality measures.
What FHIR enables
FHIR facilitates data interoperability at scale. It can have a positive impact on the evolving patient-centric approach in medicine aimed at enhancing longevity and effectively managing chronic diseases, such as cancer, metabolic disorders, cardiovascular diseases and neurodegenerative diseases, that contribute the most to the astronomical cost of healthcare and shorten lifespans.
By integrating data from clinical and administrative domains, FHIR helps uncover correlations that can inform preventative treatments and optimize healthcare delivery. Augmentation with other domains, like lifestyle data (such as exercise, sleep, and nutrition) is anticipated to provide a deeper understanding of the relationship between patient behaviors and tactics, risk factors and health outcomes, which is essential for advancing personalized medicine and value-based care models.
FHIR defines the ontology for an emerging patient data intelligence graph, identifying the resource types and how they are related to each other. FHIR resources describe facets of a patient across domains and time, and can be connected to social graphs, describing relations between patients, caregivers and other trusted parties; and control graphs, defining temporal access controls to traversable paths in the FHIR graph.
For example, Primary Record is an app designed for families and their local community of trusted caregivers to document, collaborate on and share medical information in the cloud. Families retrieve their patient history from different sources in a FHIR data platform. This “crowdsourcing” approach enables participants to collectively complete information gaps in the timelines that otherwise would not be captured by clinical EHR systems. They govern who has access to it, and what’s being written back to the patient record in the FHIR platform.
The power of collective data
Enabling health domains to coalesce around a collective emerging FHIR-native patient data intelligence graph with positive learning feedback loops is a compelling proposition. It describes the blueprint for a consumer-centric data architecture we have seen applied repeatedly in other industries, such as the “Amazonification” of commerce, where customer experience and service customization are priorities.
Similarly, a patient-centric healthcare model could utilize a FHIR-native data architecture that, starting from a personal token uniquely authenticating clinical and financial health history, proactively delivers personalized care plans and streamlines insurance processes, improving overall healthcare efficiency and patient satisfaction.
FHIR is designed to be a modular and open-source standard, encouraging global adoption and continual refinement. It enables local customization to meet specific needs, which is critical given the diverse regulatory and operational needs in healthcare. The ongoing development of FHIR includes the creation of new resource types and the establishment of implementation guides by communities like DaVinci and CARIN, which advocate for the standard’s extension.
Not all is smooth sailing
However, the rapid development of FHIR and its expanding scope present challenges. The complexity of integrating and managing an increasing variety of health data requires the FHIR specification to be continuously updated. For instance, in the FHIR R4 specifications, out of the 145 resources, only 13 have a "normative" maturity status. Furthermore, none of the Implementation Guides used for regulatory purposes in the US (US Core, Carin BB, Da Vinci PDEX, etc). have achieved “normative” status.
As FHIR becomes core to business use cases, and, accordingly, companies invest significant amounts of dollars into a FHIR-native data infrastructure, it will be instrumental for the community to agree on binding specifications that are fully backwards compatible, while, at the same time, maintaining the dynamic exploration into new territories that make it so great.
The importance of data governance
As the industry is preparing to harness data types beyond those traditionally covered by HIPAA regulations, it necessitates a robust data governance operating model to ensure that FHIR specifications remain both comprehensive and compliant with existing and future regulations.
To remain relevant and effective, the FHIR standard must evolve to address emerging issues in data privacy and consumer protection, such as those outlined by regulations like the California Consumer Privacy Act and future AI regulations and ethical guidelines. It is essential for FHIR APIs to operate on modern data platform capabilities such as full data transparency, not just in function SQL programming on tabular data, but also for vector stores used by machine learning and generative AI. Data governance enforces robust data quality and access controls at the FHIR resource level.
Finally, with very large data volumes ingested at near real-time velocity, the underlying data architecture must handle the significant increase in analytical complexity and scale while bending the cost curve. To move data at such high volumes, REST-based APIs or NDJSON bulk export will not suffice in the long run, and the community should explore how FHIR will support new serving approaches consummate with these modern data architectures.
We already have seen the beginnings of this with NCQA’s containerized quality measures which follow the concept of “bring your own algorithm.” After the platform granted a user access, it wouldn’t serve the data for it to be then physically transferred to another analytical environment. Instead, it would load the user’s analytics code, and run and compute as close to the data as possible, serving only the results.
Robust data is the elixir for businesses to innovate and sustain. As FHIR continues to develop, it stands as a cornerstone for the next generation of healthcare innovations, promising to enhance data interoperability, support personalized medicine and optimize value-based care. Its success will depend on the collaborative efforts of the global healthcare community to refine and expand the standard, ensuring it can meet the complex demands of AI computing and multi-policy regulatory environments. Ultimately the trust in analytical insights and AI models relies on the robustness of data and the underlying infrastructure.
Pieter De Leenheer, PhD, is chief technology officer of 1upHealth.