ISiKGCS

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.

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 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 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.

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.

If there is no comparator, then there is no modification of the value

How the Quantity should be understood and represented.

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.

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.

Codes specifying why the result (Observation.value[x]) is missing.

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.

Codes identifying interpretations of observations.

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.

Methods for simple observations.

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 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.

Code for the meaning of a reference range.

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.

Codes identifying the population the reference range applies to.

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".

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.

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.

If there is no comparator, then there is no modification of the value

How the Quantity should be understood and represented.

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.

Codes specifying why the result (Observation.value[x]) is missing.

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.

Codes identifying interpretations of observations.

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.

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.

If there is no comparator, then there is no modification of the value

How the Quantity should be understood and represented.

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.

Codes specifying why the result (Observation.value[x]) is missing.

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.

Codes identifying interpretations of observations.

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.

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.

If there is no comparator, then there is no modification of the value

How the Quantity should be understood and represented.

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.

Codes specifying why the result (Observation.value[x]) is missing.

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.

Codes identifying interpretations of observations.

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.

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.

If there is no comparator, then there is no modification of the value

How the Quantity should be understood and represented.

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.

Codes specifying why the result (Observation.value[x]) is missing.

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.

Codes identifying interpretations of observations.

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.