ObservationsSubmitted by maria.michaels… on 2020-11-23
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.
| 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. |
| Healthcare Aims |
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| 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. [Observation: Value.code] • 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): 2.16.840.1.113883.10.20.22.2.3.1 FHIR: Observation 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 |
| Supporting Artifacts |
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. |
| Supporting Artifacts |
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 |
| Potential Challenges | |
| 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. |
Submitted by maria.michaels… on 2020-11-23
MedMorph's Number of Prenatal Visits
In agreement of a common template structure for specifying observations and the mechanisms for creating subsets of key domains where there is value in doing so. Additionally, USCDI could also provide a way for defining the concepts within the key domains (e.g. concepts associated with pregnancy, vital signs, labs). While the proposed data class ‘Observation’ may capture elements that identify a value that support Number of Prenatal Visits, it is important to distinguish and define 'Number of Prenatal Visits' as those visits recorded in the most current records available. It should not include visits for laboratory and other testing in which a physician or health care professional did not examine or counsel the pregnant woman. It should not include classes, such as childbirth classes, where the physician or health care professional did not provide individual care to the pregnant woman. These types of nuances in reporting values are defined differently among other natality values reported such as Plurality and placing them within a general value in observation may lose the integrity of the information intended to capture. While the proposed data class ‘Observation’ captures data elements for the specified pregnancy it is important to recognize that birth, fetal death and delivery events may be captured within specific areas of a healthcare system such as labor and delivery summaries and prenatal care records. To help lessen the burden of implementations and queries of these events we propose a new Delivery and Pregnancy Information class where data elements like the Number of Prenatal Visits may be appropriately captured in this specific life event. National worksheet for reporting of live birth and US standard birth certificate: https://www.cdc.gov/nchs/data/dvs/facility-worksheet-2016-508.pdf, https://www.cdc.gov/nchs/data/dvs/birth11-03final-ACC.pdf Additional relevant specification for this data element includes the following HL7 and IHE standards: HL7 Version 2.6 Implementation Guide: Vital Records Birth and Fetal Death Reporting, Release 1 STU Release 2 - US Realm: https://www.hl7.org/implement/standards/product_brief.cfm?product_id=320 IHE Quality, Research and Public Health Technical Framework Supplement – Birth and Fetal Death Reporting-Enhanced (BFDR-E) Revision 3.1: https://www.ihe.net/uploadedFiles/Documents/QRPH/IHE_QRPH_Suppl_BFDR-E.pdf Vital Records Common Profiles Library FHIR IG: http://build.fhir.org/ig/HL7/fhir-vr-common-ig/branches/master/index.html Vital Records Birth and Fetal Death Reporting: https://build.fhir.org/ig/HL7/fhir-bfdr/index.html Birth Defect Reporting FHIR IG: https://build.fhir.org/ig/HL7/fhir-birthdefectsreporting-ig/index.html HL7 CDA® R2 Implementation Guide: Ambulatory and Hospital Healthcare Provider Reporting to Birth Defect Registries Release 1 , STU 2 -US Realm: https://www.hl7.org/implement/standards/product_brief.cfm?product_id=428 Below lists examples of artifacts of where this data element is collected. EPIC stork module (obstetrics) for birth reporting: https://www.epic.com/software#PatientEngagement EPIC FHIR APIs for patient, vitals, obstetric details: https://fhir.epic.com/Specifications?api=932 https://fhir.epic.com/Specifications?api=968 https://fhir.epic.com/Specifications?api=966 BFDR-E in ISA: https://www.healthit.gov/isa/reporting-birth-and-fetal-death-public-health-agencies