The Pillars of Health IT Standards – Part 1

The Pillars of Health IT Standards – Part 1

Health IT standards can be broken down into what I call pillars of interoperability. These pillars are Content, Transport, and Workflow. Content standards aid in clearly communicating content shared between various applications. They provide a medium between disparate health IT systems to speak the same language. They provide clues, sometimes very specific, sometimes vague, as to what data lives inside of various content structures. Transport standards describe ways that content will be sent to, received from, or otherwise made available for consumption between two or more applications. Workflow standards stitch together the web of interaction points across the health IT ecosystem, leveraging Content and Transport standards to describe what an “end-to-end” flow of healthcare data looks like for any given use case.

This will be a 3-part blog post series breaking down each of these concepts. This post will focus in on Content, followed by Transport, and finallly Workflow.


Is very important when it comes to one system needing to communicate to another system relevant information to do something. Understanding the content is vital for the system or end user to be able to take some action. If the information is improperly understood then catastrophe could follow, and rather easily. Let’s take the example of units of measure on a medication. Milligrams is a common unit of measure, micrograms is perhaps not (at least in my non-clinical experience). 200 milligrams is a common dosage of ibuprofen, but 200,000 micrograms is not. The astute reader will note these are equivalent values. Suppose that a health IT system creating content to be shared with another system uses the unit of measure of micrograms for ibuprofen and documents a dosage of 200,000 (this could be entered as 200 milligrams by the end user, but perhaps stored as micrograms in the database). A health IT system consuming content created from this source system could accidentally misinterpret the value to be 200,000 milligrams, potentially resulting in a fatal situation for the patient.

While the above example may seem far-fetched, this is a reality that happens all too often and there has been much analysis and research done in the area of accidental medication overdose. The proper creation and consumption of content is vitally important (quite literally!) to a positive health outcome for a patient. Content creators must ensure they properly represent the data to be shared, and content consumers must ensure they properly understand the data to be consumed.

Content can be broken down into several different components: structures, state, reference data, and data mapping. Let’s take a look a each of these areas.


The structures used in content interoperability vary from base-level standards to implementations of those standard structures to meet a specific use case. The base-level standards are at the “schema” level, which defines the valid data types, how many times a particular element may be repeated (or not), etc. The implementation of those standards to meet a given use case is at the “schematron” level. ISO Schematron is a standard developed by ISO that has been in use for the past several years to validate conformance of an XML implementations against any given specification.

This idea of base structure versus what goes inside of that structure is important as it allows for multiple levels of standards development, and enables profiling standards to create specific implementation guidance for specific use cases. Through this approach the exchange of health IT information is able to be effectively exchanged in a changing market of available standards and systems.


Content may exist as transient or as persistant. Sometimes the lines are blurred here where transient data may later be repurposed as persistant, or vice versa! Workflow (discussed in a forthcoming post) helps to address this issue. State, in this context, is not quite the same as status although they share some characteristics. State is more distinct. A set of content is “in” a state, whereas it “has” a status. So content that is in a document state may be in an approved status. The difference is subtle, but very relevant. Health IT standards rely on the use of states to provide some sense of stability around what to expect of the content standardized within.

Reference Data

Reference data is a special kind of data used to classify other data. It helps to describe the purpose and meaning of the data. Reference data is found in the form of standardized vocabularies such as SNOMED and LOINC. Reference data is commonly required in master data management solutions to link the data across the spectrum, whether that data be patients, providers, clinical concepts, financial transactions, or any number of other master data concepts that are able to be leveraged to tell a story about the business that brings value to the organization in terms of decisions they need to make. Reference data can also be used in inferencing solutions where probable conclusions are developed based on the presence of a certain number of other specific data that is available. Reference data is an extension of schematron – if schematron defines what general type of data shall exist in a given content structure, then reference data allows for flexibility in terms of what the options are for specific content within those assertions.

Data Mapping

Data mapping is the act of identifying the likeness of one data concept to another, and providing a persistant and resuable link to that data. This is the glue that enables systems to exchange data. Data mapping leverages standard structures and reference data to figure out what needs to be mapped where. A particular set of inbound source content may be represented by one indsutry standard and needs to be mapped to an internal data model. If the source data is already linked to an industry standard reference data set (i.e., Vocabulary), then both the structure and the specific implementation and codification of the data elements within that structure can be mapped into the internal data model with relative ease given the internal system has tooling in place to support such content and reference data standards. That is a long-winded way of saying that content standards and terminology standards go a long way to solving interoperability problems when implemented properly.


Content is vitallly important – I would argue the most important aspect of health IT interoperability. If the content is not understood by any system in a health exchange of data, a number of different problems present themselves. And sometimes ill-communication is worse than no commmunication if it is misunderstood in a way that will bring harm to the patient. As the Hippocratic Oath states: “First do no harm.” A content creator must take extreme care to ensure content is properly represented, and a content consumer must equally take care that the content is consumed in the way it was intended for consumption by the creator.

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