Observations are a key aspect of healthcare. This resource is used to capture those that do not require more sophisticated mechanisms.

A unique identifier assigned to this observation.

Allows observations to be distinguished and referenced.

观测结果值的状态

A code that classifies the general type of observation being made.

如果Observation.component不为空，则需要将component元素中所有的值（编码-值对）结合此元素一起综合考虑，才能正确理解观测结果的含义。

Observations have no value if you don't know who or what they're about.

Some analytes vary greatly over age.

For a discussion on the ways Observations can be assembled in groups together see Notes below.

All code-value and component.code-component.value pairs need to be taken into account to correctly understand the meaning of the observation.

The information determined as a result of making the observation, if the information has a simple value.

An observation exists to have a value, though it might not if it is in error, or if it represents a group of observations.

Provides a reason why the expected value in the element Observation.component.value[x] is missing.

For many results it is necessary to handle exceptional values in measurements.

"Null" or exceptional values can be represented two ways in FHIR Observations. One way is to simply include them in the value set and represent the exceptions in the value. For example, measurement values for a serology test could be "detected", "not detected", "inconclusive", or "test not done".

The alternate way is to use the value element for actual observations and use the explicit dataAbsentReason element to record exceptional values. For example, the dataAbsentReason code "error" could be used when the measurement was not completed. Because of these options, use-case agreements are required to interpret general observations for exceptional values.

A categorical assessment of an observation value. For example, high, low, normal.

For some results, particularly numeric results, an interpretation is necessary to fully understand the significance of a result.

Historically used for laboratory results (known as 'abnormal flag' ), its use extends to other use cases where coded interpretations are relevant. Often reported as one or more simple compact codes this element is often placed adjacent to the result value in reports and flow sheets to signal the meaning/normalcy status of the result.

Guidance on how to interpret the value by comparison to a normal or recommended range.

Knowing what values are considered "normal" can help evaluate the significance of a particular result. Need to be able to provide multiple reference ranges for different contexts.

Most observations only have one generic reference range. Systems MAY choose to restrict to only supplying the relevant reference range based on knowledge about the patient (e.g., specific to the patient's age, gender, weight and other factors), but this might not be possible or appropriate. Whenever more than one reference range is supplied, the differences between them SHOULD be provided in the reference range and/or age properties.