[Progress News] [Progress OpenEdge ABL] FHIR: An Introduction and Why It’s Transforming Health Data Interchange

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Seth Meldon

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Fast Healthcare Interoperability Resources (FHIR) simplifies healthcare interoperability by adopting modern software design patterns for flexible, secure transmission of data.

In this blog series, we’re going to present a number of active projects already underway within the big tent of technologies that better enable patients and clinicians to make informed, data-driven healthcare decisions.

For readers not already working in the health IT domain, presumably you have nonetheless visited a medical practitioner of some kind at some point in your life. This blog series is focused on how the data logged by the clinician during your visit is made available securely to external parties and applications—whether to guide your own course of treatment, or to help researchers analyze and improve the health outcomes of similar populations. This is where a fundamental shift is taking place in health data exchange.

Fast Healthcare Interoperability Resources is a health data interoperability standard for the exchange of healthcare information electronically. FHIR is the logical outgrowth of the ongoing digitization of the healthcare industry and the need for patient records to be readily "available, discoverable, and understandable."

While FHIR encapsulates an immense breadth of clinical terminologies, it is organized into a few fundamental building blocks, the most elemental of which is called a “resource.” Resources are specific points of information, and can be understood as being similar to an entity in a Corticon Rule Vocabulary.

FHIR: An Introduction and Why It’s Transforming Health Data Interchange

Examples of FHIR Resources (Source: V. Nguyen, 2020)

As described in a paper published by Orion Health, resources are "kept small by design and hold only the most used elements for a given resource. For example, a patient resource has the patient's name, address, date of birth, and gender. However, a given implementation might also need to include religion or race, neither of which are included in the base resource."

Resources have clear interrelations with other resources— a patient could have an encounter with a practitioner in which the practitioner recorded clinical observations, such as a fever, and may have prescribed a medication as part of a treatment to be picked up at a pharmacy.

To contextualize groups of resources, FHIR leverages “profiles,” which provide control over and clinical context for patient data. Profiles are like a fully built Corticon Vocabulary—an abstracted data model of each piece of data that will be either an input or output of rules.

Example of a FHIR Resource as a Rule Vocabulary in Corticon

Example of a FHIR Resource as a Rule Vocabulary in Corticon

By structuring FHIR as an ontology of interrelated concepts within an increasingly complex healthcare ecosystem, data sharing is simplified, as all applications are shifting towards transmittal of healthcare data that conforms to this ontology.

The range of use cases for FHIR are immense, and growing. They’re far from limited to just an individual patient’s data. It likewise has evolved to accommodate data exchange scenarios for drug interaction checking, insurance claims, scheduling, ordering, and ePrescribing.

Moreover, FHIR transforms health software development by enabling clinicians to be a much larger part of the software’s design phase, without sacrificing interoperability.

With the proliferation of both wearable devices and mobile health apps, implementers are using new types of data, originating from new types of devices. FHIR has an answer for this as well—extensibility. FHIR is developed by a free, open source, and vendor-agnostic community, divided into working groups. These working groups, in addition to refining existing standards, are at work building new FHIR specifications.

The widespread adoption of FHIR is enabling faster implementations of solutions that are cost-effective, future-proof and, most importantly, improving patient outcomes. In forthcoming blogs, we’ll analyze some real world case studies and illustrate how Progress tooling is leveraged throughout.

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