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. For example, a MedicationRequest may require a patient to have laboratory test performed before it is dispensed.

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

References SHALL be a reference to an actual FHIR resource, and SHALL be resolveable (allowing for access control, temporary unavailability, etc.). Resolution can be either by retrieval from the URL, or, where applicable by resource type, by treating an absolute reference as a canonical URL and looking it up in a local registry/repository.

A larger event of which this particular Observation is a component or step. For example, an observation as part of a procedure.

To link an Observation to an Encounter use encounter. See the Notes below for guidance on referencing another Observation.

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.

A human language representation of the concept as seen/selected/uttered by the user who entered the data and/or which represents the intended meaning of the user.

The codes from the terminologies do not always capture the correct meaning with all the nuances of the human using them, or sometimes there is no appropriate code at all. In these cases, the text is used to capture the full meaning of the source.

Very often the text is the same as a displayName of one of the codings.

The patient, or group of patients, location, or device this observation is about and into whose record the observation is placed. If the actual focus of the observation is different from the subject (or a sample of, part, or region of the subject), the focus element or the code itself specifies the actual focus of the observation.

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.

The actual focus of an observation when it is not the patient of record representing something or someone associated with the patient such as a spouse, parent, fetus, or donor. For example, fetus observations in a mother's record. The focus of an observation could also be an existing condition, an intervention, the subject's diet, another observation of the subject, or a body structure such as tumor or implanted device. An example use case would be using the Observation resource to capture whether the mother is trained to change her child's tracheostomy tube. In this example, the child is the patient of record and the mother is the focus.

Typically, an observation is made about the subject - a patient, or group of patients, location, or device - and the distinction between the subject and what is directly measured for an observation is specified in the observation code itself ( e.g., "Blood Glucose") and does not need to be represented separately using this element. Use specimen if a reference to a specimen is required. If a code is required instead of a resource use either bodysite for bodysites or the standard extension focusCode.

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 but still be tied to the context of the encounter (e.g. pre-admission laboratory 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. For recording imprecise or "fuzzy" times (For example, a blood glucose measurement taken "after breakfast") use the Timing datatype which allow the measurement to be tied to regular life events.

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

For Observations that don’t require review and verification, it may be the same as the lastUpdated time of the resource itself. For Observations that do require review and verification for certain updates, it might not be the same as the lastUpdated time of the resource itself due to a non-clinically significant update that doesn’t require the new version to be reviewed and verified again.

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.

References SHALL be a reference to an actual FHIR resource, and SHALL be resolveable (allowing for access control, temporary unavailability, etc.). Resolution can be either by retrieval from the URL, or, where applicable by resource type, by treating an absolute reference as a canonical URL and looking it up in a local registry/repository.

The value of the measured amount. The value includes an implicit precision in the presentation of the value.

Precision is handled implicitly in almost all cases of measurement.

The implicit precision in the value should always be honored. Monetary values have their own rules for handling precision (refer to standard accounting text books).

How the value should be understood and represented - whether the actual value is greater or less than the stated value due to measurement issues; e.g. if the comparator is "<" , then the real value is < stated value.

Need a framework for handling measures where the value is <5ug/L or >400mg/L due to the limitations of measuring methodology.

Note that FHIR strings SHALL NOT exceed 1MB in size

A human-readable form of the unit.

There are many representations for units of measure and in many contexts, particular representations are fixed and required. I.e. mcg for micrograms.

Note that FHIR strings SHALL NOT exceed 1MB in size

The identification of the system that provides the coded form of the unit.

Need to know the system that defines the coded form of the unit.

see http://en.wikipedia.org/wiki/Uniform_resource_identifier

A computer processable form of the unit in some unit representation system.

Need a computable form of the unit that is fixed across all forms. UCUM provides this for quantities, but SNOMED CT provides many units of interest.

The preferred system is UCUM, but SNOMED CT can also be used (for customary units) or ISO 4217 for currency. The context of use may additionally require a code from a particular system.

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.

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.

Comments about the observation or the results.

Need to be able to provide free text additional information.

May include general statements about the observation, or statements about significant, unexpected or unreliable results values, or information about its source when relevant to its interpretation.

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 device used to generate the observation data.

Note that this is not meant to represent a device involved in the transmission of the result, e.g., a gateway. Such devices may be documented using the Provenance resource where relevant.

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 context of use may frequently define what kind of quantity this is and therefore what kind of units can be used. The context of use may also restrict the values for the comparator.

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).

The context of use may frequently define what kind of quantity this is and therefore what kind of units can be used. The context of use may also restrict the values for the comparator.

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. Multiple appliesTo are interpreted as an "AND" of the target populations. For example, to represent a target population of African American females, both a code of female and a code for African American would be used.

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.

The stated low and high value are assumed to have arbitrarily high precision when it comes to determining which values are in the range. I.e. 1.99 is not in the range 2 -> 3.

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".

Note that FHIR strings SHALL NOT exceed 1MB in size

This observation is a group observation (e.g. a battery, a panel of tests, a set of vital sign measurements) that includes the target as a member of the group.

When using this element, an observation will typically have either a value or a set of related resources, although both may be present in some cases. For a discussion on the ways Observations can assembled in groups together, see Notes below. Note that a system may calculate results from QuestionnaireResponse into a final score and represent the score as an Observation.

The target resource that represents a measurement from which this observation value is derived. For example, a calculated anion gap or a fetal measurement based on an ultrasound image.

All the reference choices that are listed in this element can represent clinical observations and other measurements that may be the source for a derived value. The most common reference will be another Observation. For a discussion on the ways Observations can 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.

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

The value of the measured amount. The value includes an implicit precision in the presentation of the value.

Precision is handled implicitly in almost all cases of measurement.

The implicit precision in the value should always be honored. Monetary values have their own rules for handling precision (refer to standard accounting text books).

How the value should be understood and represented - whether the actual value is greater or less than the stated value due to measurement issues; e.g. if the comparator is "<" , then the real value is < stated value.

Need a framework for handling measures where the value is <5ug/L or >400mg/L due to the limitations of measuring methodology.

Note that FHIR strings SHALL NOT exceed 1MB in size

A human-readable form of the unit.

There are many representations for units of measure and in many contexts, particular representations are fixed and required. I.e. mcg for micrograms.

Note that FHIR strings SHALL NOT exceed 1MB in size

The identification of the system that provides the coded form of the unit.

Need to know the system that defines the coded form of the unit.

see http://en.wikipedia.org/wiki/Uniform_resource_identifier

A computer processable form of the unit in some unit representation system.

Need a computable form of the unit that is fixed across all forms. UCUM provides this for quantities, but SNOMED CT provides many units of interest.

The preferred system is UCUM, but SNOMED CT can also be used (for customary units) or ISO 4217 for currency. The context of use may additionally require a code from a particular system.

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.

A human language representation of the concept as seen/selected/uttered by the user who entered the data and/or which represents the intended meaning of the user.

The codes from the terminologies do not always capture the correct meaning with all the nuances of the human using them, or sometimes there is no appropriate code at all. In these cases, the text is used to capture the full meaning of the source.

Very often the text is the same as a displayName of one of the codings.

The value of the measured amount. The value includes an implicit precision in the presentation of the value.

Precision is handled implicitly in almost all cases of measurement.

The implicit precision in the value should always be honored. Monetary values have their own rules for handling precision (refer to standard accounting text books).

How the value should be understood and represented - whether the actual value is greater or less than the stated value due to measurement issues; e.g. if the comparator is "<" , then the real value is < stated value.

Need a framework for handling measures where the value is <5ug/L or >400mg/L due to the limitations of measuring methodology.

Note that FHIR strings SHALL NOT exceed 1MB in size

A human-readable form of the unit.

There are many representations for units of measure and in many contexts, particular representations are fixed and required. I.e. mcg for micrograms.

Note that FHIR strings SHALL NOT exceed 1MB in size

The identification of the system that provides the coded form of the unit.

Need to know the system that defines the coded form of the unit.

see http://en.wikipedia.org/wiki/Uniform_resource_identifier

A computer processable form of the unit in some unit representation system.

Need a computable form of the unit that is fixed across all forms. UCUM provides this for quantities, but SNOMED CT provides many units of interest.

The preferred system is UCUM, but SNOMED CT can also be used (for customary units) or ISO 4217 for currency. The context of use may additionally require a code from a particular system.

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.

A human language representation of the concept as seen/selected/uttered by the user who entered the data and/or which represents the intended meaning of the user.

The codes from the terminologies do not always capture the correct meaning with all the nuances of the human using them, or sometimes there is no appropriate code at all. In these cases, the text is used to capture the full meaning of the source.

Very often the text is the same as a displayName of one of the codings.

The value of the measured amount. The value includes an implicit precision in the presentation of the value.

Precision is handled implicitly in almost all cases of measurement.

The implicit precision in the value should always be honored. Monetary values have their own rules for handling precision (refer to standard accounting text books).

How the value should be understood and represented - whether the actual value is greater or less than the stated value due to measurement issues; e.g. if the comparator is "<" , then the real value is < stated value.

Need a framework for handling measures where the value is <5ug/L or >400mg/L due to the limitations of measuring methodology.

Note that FHIR strings SHALL NOT exceed 1MB in size

A human-readable form of the unit.

There are many representations for units of measure and in many contexts, particular representations are fixed and required. I.e. mcg for micrograms.

Note that FHIR strings SHALL NOT exceed 1MB in size

The identification of the system that provides the coded form of the unit.

Need to know the system that defines the coded form of the unit.

see http://en.wikipedia.org/wiki/Uniform_resource_identifier

A computer processable form of the unit in some unit representation system.

Need a computable form of the unit that is fixed across all forms. UCUM provides this for quantities, but SNOMED CT provides many units of interest.

The preferred system is UCUM, but SNOMED CT can also be used (for customary units) or ISO 4217 for currency. The context of use may additionally require a code from a particular system.

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.

A human language representation of the concept as seen/selected/uttered by the user who entered the data and/or which represents the intended meaning of the user.

The codes from the terminologies do not always capture the correct meaning with all the nuances of the human using them, or sometimes there is no appropriate code at all. In these cases, the text is used to capture the full meaning of the source.

Very often the text is the same as a displayName of one of the codings.

The value of the measured amount. The value includes an implicit precision in the presentation of the value.

Precision is handled implicitly in almost all cases of measurement.

The implicit precision in the value should always be honored. Monetary values have their own rules for handling precision (refer to standard accounting text books).

How the value should be understood and represented - whether the actual value is greater or less than the stated value due to measurement issues; e.g. if the comparator is "<" , then the real value is < stated value.

Need a framework for handling measures where the value is <5ug/L or >400mg/L due to the limitations of measuring methodology.

Note that FHIR strings SHALL NOT exceed 1MB in size

A human-readable form of the unit.

There are many representations for units of measure and in many contexts, particular representations are fixed and required. I.e. mcg for micrograms.

Note that FHIR strings SHALL NOT exceed 1MB in size

The identification of the system that provides the coded form of the unit.

Need to know the system that defines the coded form of the unit.

see http://en.wikipedia.org/wiki/Uniform_resource_identifier

A computer processable form of the unit in some unit representation system.

Need a computable form of the unit that is fixed across all forms. UCUM provides this for quantities, but SNOMED CT provides many units of interest.

The preferred system is UCUM, but SNOMED CT can also be used (for customary units) or ISO 4217 for currency. The context of use may additionally require a code from a particular system.

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.