In my role on the 2017 Interoperability Standards Advisory Task Force of the U.S. Health IT Standards Committee (HITSC), we are making recommendations about revisions and enhancements that the Office of the National Coordinator for Health IT (ONC) could make for the 2017 Interoperability Standards Advisory (ISA). I’ve written previously about some of those recommendations. The ONC has been listening, and in the latest draft of the 2017 ISA they have implemented many of those suggestions.

One of the major structural changes we recommended was that the ISA should use a consistent format to separate vocabulary standards for observations from those for observation values. To help ISA readers who aren’t familiar with these concepts, ONC is looking for some narrative to explain it.

Well, here goes.


photo via Jasleen Kaur

Modeling health data as observations

Many kinds of health data are represented as observations. A laboratory test result, a vital sign measurement, or recording the kind of exercise activity that a patient engaged in (e.g. running, walking, swimming, etc.) can all be considered observations.

In this context, we use observation as a generic term. But, depending on the domain of interest, you might call these tests, variables, or data elements, etc.

Within and among health IT systems, observations are communicated with a structure that has two key structural elements. The first element identifies what the observation is, e.g. diastolic blood pressure, hematocrit, tobacco smoking status. The second element carries the result value of the observation, e.g. 80 (mmHg), 40 (%), or “current every day smoker”.

When used together, these two elements carry the instance of specific test result for a given patient.

Another way to think about this model is as questions and answers. This first element of the structure (the observation) is like the question, and the second element (the observation value) is like the answer.

For example, the question might be: What is this patient’s blood type? The answer then, might be O Positive.

When identifying Vocabulary/Code Sets/Terminology Standards for health data represented as observations, the ISA lists separately the different standards for the observation and the observation value because they fulfill different roles.

Quantitative results don’t need a standard code for the value: the observation value is simply a number (with its associated units of measure). On the other hand, nominal or ordinal results benefit from having a standard code.

A common pairing is to use LOINC as the standard code for the observation (that’s what the O in LOINC stands for), and SNOMED CT as the standard code for the observation value when needed. This approach is endorsed by the developers of both terminologies and fits their design purpose.

There are some situations where the structure of health IT system removes the need for the two-part observation structure. If an EHR has a “Problem” table where the records are instances or assertions of patient problems (conditions), then there is no need for an observation code. On the other hand, if the structure of the health IT system or the exchange format lacks such a structural element, the patient’s problem list could be represented using the observation and observation value pattern.

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