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  1. FHIR Specification Feedback
  2. FHIR-27114

Provide example of Alignment

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    • Type: Change Request
    • Status: Applied (View Workflow)
    • Priority: Medium
    • Resolution: Persuasive with Modification
    • Specification:
      US FHIR Guidance - Quality Reporting (FHIR)
    • Raised in Version:
      0.1
    • Work Group:
      Clinical Quality Information
    • Related Page(s):
      Home
    • Resolution Description:
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      Add example based on CMS127 comparing QDM and FHIR for two data elements in CMS127 to the Alignment section. (text present in attachment to this section).  AND add a sentence: 
      HIT implementers which use a proprietary internal data model are mapping their representations to FHIR for a variety of use cases (e.g., communicating among practitioners and registries).  Using FHIR for eCQMs can leverage this FHIR representation, whereas using QDM (or any model tailored specifically for quality measurement) for eCQMs requires an extra step of conversion.

      Also update the Fitness section to provide improved examples for overfitting, underfitting and misfitting:

      At the end of the intro paragraph - 

      The following examples represent misalignment between the source and target of representations of information across use cases and/or from domain entity/concept to the expression of information.

      • Overfitting occurs when there is more extensive expressivity in the data model than is required to accurately represent domain entities and concepts within the information carrier (data model) resulting in ambiguity and leading to misinterpretation. For example, QDM differentiates Procedure from Intervention based on conceptual distinctions yet both map to the FHIR Procedure resource.
      • Underfitting occurs when a data model does not or cannot represent ample concepts to carry the information required to sufficiently describe the domain of interest. For example, QDM defines entities (Patient, Care Partner, Organization, and Practitioner) to allow expressions to reference the performer of one activity, e.g., Encounter, should be the same performer of another activity, e.g., Physical Exam. However, the entities are not fully specified to allow the Encounter participant to be an organization and the Physical Exam performer to be a practitioner who is a member of that referenced organization. FHIR enables such expressivity using PractitionerRole.organization. In addition, QDM’s approach to underfitting requires a new version to incorporate any changes. FHIR allows extensions to add required elements in advance of new version availability.
      • Mis-fitting occurs when there is misalignment between the source and target representations of a mapping. For example, a misfit occurs when a concept in one domain, such as Physical Exam, can be interpreted as describing actions such as dilated retinal examination (addressed with the FHIR Procedure resource), or as findings, i.e., the result of the dilated retinal examination (addressed with the FHIR Observation resource). QDM requires interpretation by expression authors and implementers the ambiguity can lead to incorrect data retrievals. FHIR enables the expression authors and implementers to more clearly understand intent, improving the certainty of expected data retrieves.

       

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      Add example based on CMS127 comparing QDM and FHIR for two data elements in CMS127 to the Alignment section. (text present in attachment to this section).  AND add a sentence:  HIT implementers which use a proprietary internal data model are mapping their representations to FHIR for a variety of use cases (e.g., communicating among practitioners and registries).  Using FHIR for eCQMs can leverage this FHIR representation, whereas using QDM (or any model tailored specifically for quality measurement) for eCQMs requires an extra step of conversion. Also update the Fitness section to provide improved examples for overfitting, underfitting and misfitting: At the end of the intro paragraph -  The following examples represent misalignment between the source and target of representations of information across use cases and/or from domain entity/concept to the expression of information. Overfitting occurs when there is more extensive expressivity in the data model than is required to accurately represent domain entities and concepts within the information carrier (data model) resulting in ambiguity and leading to misinterpretation. For example, QDM differentiates Procedure from Intervention based on conceptual distinctions yet both map to the FHIR Procedure resource. Underfitting occurs when a data model does not or cannot represent ample concepts to carry the information required to sufficiently describe the domain of interest. For example, QDM defines entities (Patient, Care Partner, Organization, and Practitioner) to allow expressions to reference the performer of one activity, e.g., Encounter, should be the same performer of another activity, e.g., Physical Exam. However, the entities are not fully specified to allow the Encounter participant to be an organization and the Physical Exam performer to be a practitioner who is a member of that referenced organization. FHIR enables such expressivity using PractitionerRole.organization. In addition, QDM’s approach to underfitting requires a new version to incorporate any changes. FHIR allows extensions to add required elements in advance of new version availability. Mis-fitting occurs when there is misalignment between the source and target representations of a mapping. For example, a misfit occurs when a concept in one domain, such as Physical Exam, can be interpreted as describing actions such as dilated retinal examination (addressed with the FHIR Procedure resource), or as findings, i.e., the result of the dilated retinal examination (addressed with the FHIR Observation resource). QDM requires interpretation by expression authors and implementers the ambiguity can lead to incorrect data retrievals. FHIR enables the expression authors and implementers to more clearly understand intent, improving the certainty of expected data retrieves.  
    • Resolution Vote:
      Paul Denning/Floyd Eisenberg:19-0-2
    • Change Category:
      Clarification
    • Change Impact:
      Non-substantive

      Description

      The guidance would be more convincing with one or more good examples showing how FHIR is better aligned with clinical workflow in comparison to QDM.  Add convincing examples of alignment.

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              Assignee:
              Unassigned Unassigned
              Reporter:
              mitrep9g Paul Denning
              Request in-person:
              Paul Denning
              Watchers:
              4 Start watching this issue

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