What If We're Overcomplicating Healthcare IT?
I make the case that SQL could be the solution to simplifying the layers of complexity in health data architecture. This is a written version of my talk at EHRCON 25.
Have you struggled with sharing your medical information between hospital visits? openEHR might hold the answers to healthcare’s ongoing struggle with data reusability.
The healthcare industry presents unique challenges when it comes to data modelling—new information, details and relationships are constantly being discovered. This means that an information system built in conventional approaches - where both information and knowledge, or data and clinical information about the data are built into a single data model quickly becomes obsolete.
openEHR’s two-level information model design
openEHR is designed keeping this in mind—understanding that the domain of medical information is a changing place, and that changes to requirements aren’t exceptional. In openEHR, information and knowledge are separated into two layers,
- Information level: this defines the structure of data, the reference model (RM)
- Knowledge level: this defines clinical concepts and their representation, the archetype model (AM) Only the reference level is implemented in software, significantly reducing dependency on changing content definitions. This separation allows domain experts, i.e. clinicians, to update medical knowledge without requiring modifications to the core data system.
A helpful analogy is to think of it as a library, the information level are the shelves that provide structure and the knowledge level are the books, the collection of which can change and grow over time. In openEHR, the reference model (RM) defines a stable way to store patient data, and the knowledge level or archetype model (AM) defines medical concepts that give the data meaning. The knowledge level in openEHR consists of archetypes and templates that we will now explore in detail.
What is an openEHR Archetype?
An openEHR Archetype is a reusable, domain-specific building block that structures clinical data. They define content on the basis of topic or theme e.g. blood pressure, physical examination, report. These are designed by domain experts or clinicians and are vendor neutral.
Archetypes have a machine readable GUID that is assigned to it when it is created. They also have Human Readable IDs (HRIDs) as represented in the below example.
The openEHR Clinical Knowledge Manager(CKM) is a health data management application and repository of clinical concepts developed with the goal to create a coherent health data ecosystem that promotes sharing and reusing of clinical concepts across multiple clinical scenarios.
Here is a blog that explores this further.
Using the CKM, let’s explore what an Archetype looks like, with the example of a blood pressure archetype that is accessible as the Blood Pressure Archetype (v2) with HRID org.openehr::openEHR-EHR-OBSERVATION.blood_pressure.v2
The blood pressure Archetype defines the data and other contextual information about a blood pressure reading. This structured approach enables consistency in health data capture and storage and therefore ensures reusability.
It defines the kind of data that can be captured, including systolic, diastolic, mean arterial pressure, pulse pressure, and clinical interpretations and comments. You can also look at the mind map representation to visually understand the structure of an archetype.
What is an openEHR Template?
An openEHR Template allows the use of Archetypes for specific clinical scenarios such as a patient admission or discharge. They are usually customized for practical use in a particular healthcare setting and are the “real-world” versions of the Archetype. It caters to the actual clinical workflow and usually corresponds to clinical forms, even indicating which information is mandatory and optional.
For example, a blood pressure measurement template might use only the systolic and diastolic fields from the archetype, ignoring the ‘mean arterial pressure’ if it is not a priority. It could also make some fields mandatory such as the patient position field or method of measurement (manual/automatic).
The blood pressure template blood pressure template, for example, only includes systolic and diastolic values and excludes other data points.
This Vital Signs template uses the blood pressure archetype and other archetypes including body temperature, height, weight etc. to capture the most commonly used patient information.
To go ahead and create your own openEHR Template, you can follow this tutorial. We’ll be exploring this further in future blogs.
Benefits of openEHR’s two-level information model
openEHR’s two-level methodology of information system design results in several benefits,
- Future-proofing: Applications, databases and data don’t depend on constantly changing, expanding medical information. This significantly reduces development effort and cost.
- Interoperability: Clinical data can be structured and recorded consistently across different organizations and systems. This allows for simpler data exchange, reducing errors and improving collaboration.
- Flexibility: The availability of openEHR templates allows customization for local workflows, regulations and organizational requirements. This allows global standards to be adapted to local contexts without compromising on interoperability.
- Data Quality: Templates and Archetypes enforce structured data entry that reduces ambiguity and ensures completeness. This ensures high-quality, clinically meaningful data that supports better decision making and patient outcomes.
openEHR’s two-level data model is a powerful approach towards modernizing healthcare data management. It provides a standard data foundation for clinical concepts through Archetypes, and flexibility in their implementation through Templates. This ensures both semantically clear and reusable healthcare data that is interoperable, and adaptability in implementation.
In a world where accessing the right data at the right time is essential to improving patient health outcomes, openEHR is a timely solution to address fragmented healthcare data challenges. The key features of interoperability, flexibility, improved data quality and future proofing are critical towards building a scalable and sustainable foundation for the future of healthcare innovation.
Enroll in the free openEHR Fundamentals Course to learn more about openEHR, the open clinical data standard that’s transforming the healthcare data landscape. Register now!
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