Measurements and simple assertions made about a patient, device or other subject.

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

Used for simple observations such as device measurements, laboratory atomic results, vital signs, height, weight, smoking status, comments, etc. Other resources are used to provide context for observations such as lab reports, etc.

A unique identifier assigned to this observation.

Allows observations to be distinguished and referenced.

A plan, proposal or order that is fulfilled in whole or in part by this event.

Allows tracing of authorization for the event and tracking whether proposals/recommendations were acted upon.

The status of the result value.

Need to track the status of individual results. Some results are finalized before the whole report is finalized.

This element is labeled as a modifier because the status contains codes that mark the resource as not currently valid.

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

Used for filtering what observations are retrieved and displayed.

In addition to the required category valueset, this element allows various categorization schemes based on the owner’s definition of the category and effectively multiple categories can be used at once. The level of granularity is defined by the category concepts in the value set.

Describes what was observed. Sometimes this is called the observation "name".

Knowing what kind of observation is being made is essential to understanding the observation.

The patient, or group of patients, location, or device whose characteristics (direct or indirect) are described by the observation and into whose record the observation is placed. Comments: Indirect characteristics may be those of a specimen, fetus, donor, other observer (for example a relative or EMT), or any observation made about the subject.

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

One would expect this element to be a cardinality of 1..1. The only circumstance in which the subject can be missing is when the observation is made by a device that does not know the patient. In this case, the observation SHALL be matched to a patient through some context/channel matching technique, and at this point, the observation should be updated.

If the target of the observation is different than the subject, the general extension observation-focal-subject. may be used. However, the distinction between the patient's own value for an observation versus that of the fetus, or the donor or blood product unit, etc., are often specified in the observation code.

The healthcare event (e.g. a patient and healthcare provider interaction) during which this observation is made.

For some observations it may be important to know the link between an observation and a particular encounter.

This will typically be the encounter the event occurred within, but some events may be initiated prior to or after the official completion of an encounter or episode but still be tied to the context of the encounter or episode (e.g. pre-admission lab tests).

The time or time-period the observed value is asserted as being true. For biological subjects - e.g. human patients - this is usually called the "physiologically relevant time". This is usually either the time of the procedure or of specimen collection, but very often the source of the date/time is not known, only the date/time itself.

Knowing when an observation was deemed true is important to its relevance as well as determining trends.

At least a date should be present unless this observation is a historical report.

The date and time this observation was made available to providers, typically after the results have been reviewed and verified.

Updated when the result is updated.

Who was responsible for asserting the observed value as "true".

May give a degree of confidence in the observation and also indicates where follow-up questions should be directed.

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 may not if it is in error, or if it represents a group of observations.

Normally, an observation will have either a single value or a set of related observations. A few observations (e.g. Apgar score) may have both a value and related observations (for an Apgar score, the observations from which the measure is derived). If a value is present, the datatype for this element should be determined by Observation.code. This element has a variable name depending on the type as follows: valueQuantity, valueCodeableConcept, valueString, valueBoolean, valueRange, valueRatio, valueSampledData, valueAttachment, valueTime, valueDateTime, or valuePeriod. (The name format is "'value' + the type name" with a capital on the first letter of the type).

If the data element is usually coded or if the type associated with the Observation.value defines a coded value, use CodeableConcept instead of string datatype even if the value is uncoded text. A value set is bound to the ValueCodeableConcept element. For further discussion and examples see the notes section below.

Provides a reason why the expected value in the element Observation.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 "specimen unsatisfactory". 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. Note that an observation may only be reported if there are values to report. For example differential cell counts values may be reported only when > 0. Because of these options, use-case agreements are required to interpret general observations for null or exceptional values.

The assessment made based on the result of the observation. Intended as a simple compact code often placed adjacent to the result value in reports and flow sheets to signal the meaning/normalcy status of the result. Otherwise known as abnormal flag.

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

May include statements about significant, unexpected or unreliable values, or information about the source of the value where this may be relevant to the interpretation of the result.

Need to be able to provide free text additional information.

Indicates the site on the subject's body where the observation was made (i.e. the target site).

Only used if not implicit in code found in Observation.code. In many systems, this may be represented as a related observation instead of an inline component. If the use case requires BodySite to be handled as a separate resource (e.g. to identify and track separately) then use the standard extension body-site-instance.

Indicates the mechanism used to perform the observation.

In some cases, method can impact results and is thus used for determining whether results can be compared or determining significance of results.

Only used if not implicit in code for Observation.code.

The specimen that was used when this observation was made.

Should only be used if not implicit in code found in Observation.code. Observations are not made on specimens themselves; they are made on a subject, but in many cases by the means of a specimen. Note that although specimens are often involved, they are not always tracked and reported explicitly. Also note that observation resources may be used in contexts that track the specimen explicitly (e.g. Diagnostic Report).

The device used to generate the observation data.

An extension should be used if further typing of the device is needed. Devices used to support obtaining an observation can be represented using either an extension or through the Observation.related element.

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 may 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.

The value of the low bound of the reference range. The low bound of the reference range endpoint is inclusive of the value (e.g. reference range is >=5 - <=9). If the low bound is omitted, it is assumed to be meaningless (e.g. reference range is <=2.3).

The value of the high bound of the reference range. The high bound of the reference range endpoint is inclusive of the value (e.g. reference range is >=5 - <=9). If the high bound is omitted, it is assumed to be meaningless (e.g. reference range is >= 2.3).

Codes to indicate the what part of the targeted reference population it applies to. For example, the normal or therapeutic range.

Need to be able to say what kind of reference range this is - normal, recommended, therapeutic, etc, - for proper interpretation.

This SHOULD be populated if there is more than one range. If this element is not present then the normal range is assumed.

Codes to indicate the target population this reference range applies to. For example, a reference range may be based on the normal population or a particular sex or race.

Need to be able to identify the target population for proper interpretation.

This SHOULD be populated if there is more than one range. If this element is not present then the normal population is assumed.

The age at which this reference range is applicable. This is a neonatal age (e.g. number of weeks at term) if the meaning says so.

Some analytes vary greatly over age.

Text based reference range in an observation which may be used when a quantitative range is not appropriate for an observation. An example would be a reference value of "Negative" or a list or table of 'normals'.

A reference to another resource (usually another Observation) whose relationship is defined by the relationship type code.

Normally, an observation will have either a value or a set of related observations. A few observations (e.g. Apgar score) may have both a value and a set of related observations or sometimes a QuestionnaireResponse from which the measure is derived.

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

A code specifying the kind of relationship that exists with the target resource.

A relationship type SHOULD be provided.

The "derived-from" type is the only logical choice when referencing the QuestionnaireAnswer resource.

A reference to the observation or QuestionnaireResponse resource that is related to this observation.

Some observations have multiple component observations. These component observations are expressed as separate code value pairs that share the same attributes. Examples include systolic and diastolic component observations for blood pressure measurement and multiple component observations for genetics observations.

Component observations share the same attributes in the Observation resource as the primary observation and are always treated a part of a single observation (they are not separable). However, the reference range for the primary observation value is not inherited by the component values and is required when appropriate for each component observation.

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

Describes what was observed. Sometimes this is called the observation "code".

Knowing what kind of observation is being made is essential to understanding 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 may not if it is in error, or if it represents a group of observations.

Normally, an observation will have either a single value or a set of related observations. A few observations (e.g. Apgar score) may have both a value and related observations (for an Apgar score, the observations from which the measure is derived). If a value is present, the datatype for this element should be determined by Observation.code. A CodeableConcept with just a text would be used instead of a string if the field was usually coded, or if the type associated with the Observation.code defines a coded value. For boolean values use valueCodeableConcept and select codes from <http://hl7.org/fhir/ValueSet/v2-0136> (these "yes/no" concepts can be mapped to the display name "true/false" or other mutually exclusive terms that may be needed"). The element, Observation.value[x], has a variable name depending on the type as follows: valueQuantity, valueCodeableConcept, valueRatio, valueChoice, valuePeriod, valueSampleData, or valueString (the name format is "'value' + the type name" with a capital on the first letter of the type).

Provides a reason why the expected value in the element Observation.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.

The assessment made based on the result of the observation. Intended as a simple compact code often placed adjacent to the result value in reports and flow sheets to signal the meaning/normalcy status of the result. Otherwise known as abnormal flag.

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

The component interpretation applies only to the individual component value. For an overall interpretation all components together use thes Observation.interpretation element.

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 may 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.