|Submitted By: Daniel Vreeman / RTI International|
|Data Element Information|
|Rationale for Separate Consideration||Some existing data classes (e.g. Laboratory) have a similarly conceptualized data element (Laboratory.Values/Results), however others (e.g. Smoking Status, Vitals) do not. Here we propose a common approach in the spirit of https://bit.ly/2ThEB7p|
|Use Case Description(s)|
|Use Case Description||Clinical observations are an essential structure for recording many kinds of health information. A laboratory test result, vital sign measurement, blood type, pain scale rating, or recording of exercise activity type that a person engaged in (e.g. running, walking, swimming, etc.) are all considered observations.
Billions of clinical observations have been exchanged in the current health information exchange ecosystem. All of the prevailing exchange (e.g. messages, documents, APIs) and common data model (e.g. OMOP, PCORnet, Sentinel, etc) paradigms represent these core data elements (code, value, date/time, subject) pertaining to observations.
For many reasons, it would be ill-advised for USCDI to enumerate the tens of thousands of specific observation “variables” (eye color, gait speed, shoulder flexion range of motion, etc) as separate Data Elements. Rather, USCDI needs a common template structure for specifying observations and then mechanisms for creating subsets of key domains where there is value in doing. Here we have proposed the essential data elements. Using this common pattern of essential data elements, key domain subsets (like the existing labs and vital signs classes) can be defined using the shared template with further specified through bindings to appropriate terminology standards and/or additional data elements.
|Estimated number of stakeholders capturing, accessing using or exchanging||Most health IT systems with more than a few clinical variables use an entity-attribute-value (or name value pair) model similar to this proposed approach for storing and exchanging observations.|
|Maturity of Use and Technical Specifications for Data Element|
|Applicable Standard(s)||Vocabulary Standard:
No single vocabulary covers the content necessary for representing all aspects of observation reporting (observation identifiers, coded observation values, computable units of measure, etc). Yet, the collection of core vocabularies needed are sufficiently mature.
[Observation: Code] LOINC was specifically designed to fulfill the need for a freely available standard for identifying observations. More than 25 years later, it now contains a rich catalog of variables, answer lists, and the collections that contain them.
[Observation: Value.units] UCUM was specifically designed to fulfill the need for a freely available standard of computable units of measure.
• If the observation represents a validated assessment instrument (e.g. survey) item and the value is a CodeableConcept, then Observation.value.code SHALL be drawn from LOINC.
• If the observation pertains to clinical genetics, then other terminologies or syntaxes may be used as appropriate, e.g. HGVS, ClinVar, etc.
• If the observation represents a human phenotype, then the Observation.value MAY be drawn from the Human Phenotype Ontology
• If the observation has a nominal or ordinal scale value and the Observation.value exists in SNOMED CT, then SNOMED CT MAY be used
Exchange and Analytic CDM specifications:
HL7 Version 2: Chapter 7
C-CDA: Results Section (entries required): 2.16.840.1.1138184.108.40.206.2.3.1
OMOP: Measurement, Observation
PCORnet CDM: LAB_RESULT_CM, PRO_CM, OBS_CLIN, OBS_GEN
|Additional Specifications||There are several implementation guides (e.g. for HL7 v2 and CDA) and FHIR profiles on Observation. Examples include those in the main FHIR spec (vital signs, genetics) and in US Core (smoking status) etc.|
|Current Use||This data element has been used at scale between multiple different production environments to support the majority of anticipated stakeholders|
A few examples of documentation include:
Epic FHIR Documentation: https://fhir.epic.com/
Cerner FHIR Documentation: https://fhir.cerner.com/millennium/r4/diagnostic/observation/
Allscripts FHIR Documentation: https://developer.allscripts.com/FHIR?PageTitle=Resources
|Number of organizations/individuals with which this data element has been electronically exchanged||5 or more. This data element has been tested at scale between multiple different production environments to support the majority of anticipated stakeholders.|
Three examples among many:
• the INPC clinical repository contains billions of discrete observations from about 100 institutions. https://www.ihie.org/popcare/#careweb
• Apple Health uses the FHIR Observation resource for labs and vital signs to interface with many institutions: https://support.apple.com/en-us/HT208647
• participating PCORNet institutions store billions of discrete observations represented in the PCORnet CDM structure
|Restrictions on Standardization (e.g. proprietary code)||Some, but not all observations in operational health IT systems are linked to vocabulary standards. So local codes still persist. At present, mappings to vocabulary standards are much more common for observation identifiers than for coded observation values.|
|Restrictions on Use (e.g. licensing, user fees)||Licensing for some reference terminology standards is available at no cost worldwide: LOINC, UCUM, HPO.
SNOMED CT is available at no cost for use within the US via an affiliate license available from NLM (https://www.nlm.nih.gov/healthit/snomedct/snomed_licensing.html).
The FHIR exchange specification is available without copyright (i.e. it is declared to be Public Domain). Other HL7 exchange specifications are available under the terms described here http://www.hl7.org/legal/ippolicy.cfm?ref=nav.
|Privacy and Security Concerns||Some observations may carry sensitive information. Appropriate protections for addressing privacy and security concerns should be addressed in the surrounding technical infrastructure and policies.|
|Estimate of Overall Burden||Unknown.|
Information from the submission form
The information determined as a result of making the observation. The information carried by Observation.value may take several forms, depending on the nature of the observation. For example, it could be a quantitative result, and ordinal scale value, nominal or categorial value, etc. Data Type: variable. Possible data types include: Quantity, CodeableConcept, string, boolean, integer, Range, Ratio, SampledData, time, dateTime, Period Terminology Standards: The appropriate terminology depends on the observation. A few examples: • If the observation is quantitative, then Observation.value.units SHALL be drawn from UCUM. • If the observation represents a validated assessment instrument (e.g. survey) item and the value is a CodeableConcept, then Observation.value.code SHALL be drawn from LOINC. • If the observation pertains to clinical genetics, then other terminologies or syntaxes may be used as appropriate, e.g. HGVS, ClinVar, etc. • If the observation represents a human phenotype, then the Observation.value MAY be drawn from the Human Phenotype Ontology • If the observation has a nominal or ordinal scale value and the Observation.value exists in SNOMED CT, then SNOMED CT MAY be used.