QI-Core Implementation Guide
5.0.0-ballot - STU Ballot
US
QI-Core Implementation Guide
5.0.0-ballot - STU Ballot
US
This page is part of the Quality Improvement Core Framework (v5.0.0-ballot: STU 5 Ballot 1) based on FHIR R4. The current version which supercedes this version is 4.1.1. For a full list of available versions, see the Directory of published versions 
| Official URL: http://hl7.org/fhir/us/qicore/ImplementationGuide/hl7.fhir.us.qicore | Version: 5.0.0-ballot | |||
| Draft as of 2022-08-08 | Computable Name: QICore | |||
This STU 5.0 update to the QI-Core profiles updates to US-Core STU v5. See the version history for a complete listing of changes to this version.
1.QI-Core has used the Encounter.diagnosis element to define diagnoses addressed as part of the encounter, and used Must Support elements to request specific data:
FHIR supports the concept of Claim.diagnosis.onAdmission, Claim.diagnosis.sequence, and Claim.diagnosis.type to address concepts of principal diagnosis and present on admission. However, feedback has suggested that use of Encounter.diagnosis is preferred as designed in QI-Core. Some have advised use of the FHIR Account resource but that does not specify diagnoses. Please advise if the current QI-Core approach is preferred or, if not, which path should be taken instead for the information desired.
2.Given the US Core approach to determining encounter diagnoses using Encounter.reasonReference with reference to Condtion Encounter Diagnosis Profile, which of the following approaches are most appropriate to capture all conditions managed during an encounter:
Where possible, new and updated content will be highlighted with green text and background
The following resolutions have been voted on by the members of the sponsoring work group HL7 International Clinical Quality Information.
Comments and their resolutions
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Status: Summary:(Jira Issue)
The QI-Core Implementation Guide defines a set of FHIR profiles with extensions and bindings needed to create interoperable, quality-focused applications. The profiles in this implementation guide derive from and extend the US Core profiles to provide a common foundation for building, sharing, and evaluating knowledge artifacts across quality improvement efforts in the US Realm.
As an HL7 FHIR Implementation Guide, changes to this specification are managed by the sponsoring workgroup, Clinical Quality Information, and incorporated as part of the standard balloting process. The current roadmap follows closely behind the base FHIR roadmap, and the US Core Implementation Guide.
This guide is divided into pages which are listed at the top each page in the menu bar:
This Implementation Guide originated as a U.S. Realm Specification with support from the Clinical Quality Framework (CQF) initiative (that concluded in 2017), which was a public-private partnership sponsored by the Centers for Medicare & Medicaid Services (CMS) and the U.S. Office of the National Coordinator (ONC) to harmonize standards for clinical decision support and electronic clinical quality measurement. The Clinical Quality Framework effort transitioned to HL7’s Clinical Quality Information (CQI) and Clinical Decision Support (CDS) Work Groups in 2016. The HL7 CQI Work Group maintains this Implementation Guide, co-sponsored by the Clinical Decision Support (CDS) HL7 Work Group to inform electronic clinical quality improvement (i.e., measurement and decision support). This Quality Improvement Core (QI-Core) Implementation Guide is intended to be usable for multiple use cases across domains, and much of the content is likely to be usable outside the U.S. Realm.
Understanding QI-Core and how it is used in quality applications requires an understanding of the role of common reference models. Electronic Health Records (EHRs) are stored in many different local formats. Exchanging data between EHRs requires mapping between local data formats. It is well understood that standards can reduce the number of data maps each data provider must create. In a similar manner, to share quality measures and clinical decision support artifacts, the measures and artifacts must refer to data in a standardized way.
In the U.S. Realm, the common reference model for electronic clinical quality measures (eCQMs) is the Quality Data Model (QDM). For clinical decision support, a common reference model is the HL7 Virtual Medical Record for Clinical Decision Support(vMR). Decision support and quality measures are closely related, and can be viewed as “two sides of the same coin”. Specifically, decision support provides guidance for clinical best practices, and quality measures assess whether clinical best practices have been followed. It therefore makes intuitive sense to use the same common reference model for both types of applications.
This initiative began in 2013 with the creation of the Quality Improvement Domain Analysis Model (QIDAM), which drew on the vMR and QDM as sources of requirements. The goal was to align on a unified logical model, Quality Information and Clinical Knowledge (QUICK), consisting of objects, attributes, and relationships such that the QUICK model could reference specific Quality Improvement Core (QI-Core) profiles aligned with specific versions of FHIR. The first QUICK model representations included a logical view derived from the corresponding FHIR profiles for the respective version of FHIR upon which QI-Core profiles are based. Recognizing the broader community focus on FHIR, QUICK logical view was aligned, structurally and semantically, as closely as possible to FHIR. While this alignment creates a common model for quality and interoperability that more easily leverages future FHIR-related efforts including Clinical Document Architecture (CDA) on FHIR. However, we recognize that defining a different conceptual/logical model for quality improvement capability splits focus of the community. The appropriate place for the mindshare and consensus development of the exchange semantics for quality improvement use cases is the QI-Core profiles directly. The QI-Core versions have evolved with FHIR-specific tooling to include views showing differential from base FHIR resources or US Core profiles, and a Must Support view indicating all Must Support elements for each respective QI-Core profile.
QI-Core classes and attributes are the most relevant to the broader QI community, lying in the intersection of clinical quality measures (CQM) and CDS, thus providing a common foundation for reusability. To the extent possible, QI-Core derives content from USCore profiles and extensions. The expectation is that QI-Core will continue to grow over time as USCore grows, and by incorporating needed extensions with broad applicability. There may also be further extensions and coordinated profiles in specific domains beyond QI-Core, e.g., radiology-specific profiles. The CQI and CDS Work Groups coordinate with HL7 Work Groups that manage specific FHIR resources to align definitions and value sets that include concepts required for CDS and retrospective CQM use cases. When additional classes and attributes are needed for specific quality applications, they can be added through FHIR’s extension mechanism. These extensions, however, would not automatically result in shareable artifacts without additional coordinating agreements between interested parties. It is expected that QI-Core will evolve over time to include some of the extensional content when the community identifies a common need and the additional content has been validated.
QI-Core profile authoring will provide a more facile method for creating CQM and CDS artifacts with CQL that expand to full FHIR representation for implementation through CQL-to-ELM conversion.
The QI-Core FHIR Implementation Guide provides requirements and guidance on the use of FHIR in quality measurement and decision support. The profiles in this implementation guide will be used to meet QI-Core project objectives of:
This IG is focused on representation of clinical data, and is limited in breadth to the profiles currently included in QI-Core. Not all FHIR resources are profiled, especially those that do not have clinical value in the context of quality improvement, or do not map to QIDAM. Additional extensions may be added to the current set of profiles, and additional profiles may be added at a later time. In particular, QI-Core represents a subset of the semantics covered in QIDAM, vMR, and QDM. The parts of the latter specifications that are not in the QI-Core profiles could be handled with additional profiles, if the DSTU period reveals the need for such additions. Keeping the QI-Core profiles in line with FHIR and FHIR’s “80%” rule is one way to make sure that the quality artifacts produced from QI-Core are computable, based on commonly-collected clinical data. The current set of profiles will evolve to reflect changes to the underlying FHIR resources.
The following topics are explicitly out of scope for this implementation guide:
Some of the above topics are under active investigation and will be topics of future standards efforts. Specifically, the FHIR Clinical Reasoning module provides resources and guidance for how to represent and evaluate quality improvement artifacts within FHIR.
Quality applications may make use of patient-specific information. For this reason, all transactions must be appropriately secured, limiting access to authorized individuals and protecting data while in transit (as laid out in the FHIR Implementer’s Safety Check List). These are the same considerations that would relate to any FHIR implementation, and include authentication, authorization, access control consistent with patient consent, transaction logging, and following best practices. For the purposes of QI-Core, security conformance rules are as follows:
It is the responsibility of the server (data provider) to ensure that any necessary consent records exist and are reviewed prior to each exchange of patient-identifiable healthcare information. This verification should be logged in the same manner as other transactions, as discussed above under General Security Considerations.
QI-Core addresses provenance at a data element level. We address data element provenance as defined with the individual FHIR resource. Each FHIR resource has its own way to address provenance (author, performer, author or issued date, occurrence date, etc.). Therefore, we assure QI-Core can handle provenance based on the resource modeling. The US domain Quality Data Model handles provenance in the same way and the mapping tables from QDM attributes to QI-Core/FHIR resource elements occurs at that level. There are some instances for which QI-Core creates extensions to ensure it captures the resource-specific data provenance. Decisions to create such extensions are intentionally consistent with each resource owner’s future FHIR version direction and with discussions with the HL7 Work Groups responsible for the respective resource. QI-Core closely follows US Core and will address future US Core versions that enhance its approach to provenance.
QI-Core has been harmonized with certain other FHIR-based initiatives, in particular, the Data Access Framework (DAF). US Core is a U.S. Realm Implementation Guide, developed under the DAF initiative, that maps ONC Common Clinical Data Set elements to FHIR resources. The data elements in US Core are also in QI-Core, and whenever possible, profiles defined in QI-Core are derived from the profiles in US Core. As a result, conforming to US Core automatically satisfies a significant subset of the conformance requirements of QI-Core. QI-Core conformance involves supporting certain additional data elements not required by US Core, because they are needed for quality measures or clinical decision support.
Because QI-Core profiles derive from US Core profiles where possible, wherever US Core defines a binding, the QI-Core profiles inherit that binding. QI-Core may specify additional constraints, such as requiring a binding that is only preferred in the USCore base profile, but in general, the QI-Core profiles use the same bindings as US Core. This means that QI-Core is currently a U.S. Realm specification. To support applications outside the U.S. Realm, additional binding analysis and effort would be required.
QI-Core’s extensions have also been reviewed by HL7 Work Groups and other initiatives to validate that QI-Core extensions will not create future conflicts. Other initiatives that the QI-Core effort is aligning with include the Clinical Information Modeling Initiative (CIMI) and Graphite Health.
For the Occupational Data Health (ODH) effort, QI Core would have profiled directly based upon the ODH IG but because the current version of ODH has a dependency on US Core STU3 (v3.1.1) not STU5, we could not do this. Even so, the QI Core profile directly aligns with the ODH profile content and is now a profile on US Core Observation SDOH profile. The following is an example how occupational data can be added to a stratified measure (e.g. breast cancer screening, colorectal cancer screening) by high risk occupations. It can provide a way to reference ODH Usual Work observation using QICore Observation profile (i.e., a single observation). If one were representing an evaluation tool that includes multiple ODH items, the QICore ObservationSurvey profile would be appropriate.
In addition, the QI-Core effort continues to update the mapping from QDM to QI-Core such that a CQL-based artifact written with QDM as the model would be executable against a QI-Core compliant FHIR endpoint.
QI-Core profiles are indicated by the prefix “QICore”. For example, the QI-Core profile of Patient is named QICorePatient.
QI-Core adds a variety of extensions to core FHIR classes. These extensions derive from two primary sources: the Quality Improvement Domain Analysis Model (QIDAM), and the Quality Data Model (QDM). Profile pages contain definitions of extensions and mappings to QDM as an aid for current users of QDM.
QI Core derives from US Core and so the requirements on “MustSupport” defined in US Core must be respected.
In addition to the requirements defined in the US Core base, QI Core further describes and constrains the “MustSupport” functionality.
Certain elements in the QI-Core profiles have a “MustSupport” flag. In the QI-Core quality profiles, the MustSupport flag is used to indicate whether the element must be supported in QI implementations. More specifically, labelling an element as MustSupport means that quality improvement implementations SHALL understand and process the element.
In addition, only elements where MustSupport is true can be used in quality measure criteria or decision support condition and triggering logic. This is because if the logic references an element, the conclusion is not valid unless the exchanging system supports the elements being referenced by the logic.
Although support is not guaranteed, references to elements where MustSupport is false (or does not appear) in the QI-Core profile would be useful and should be provided. All elements in the QI-Core profiles, including those that are not MustSupport, can be used in CDS actions (i.e. right-hand side or consequents of CDS rules). For example, vaccination protocol in ImmunizationRecommendation is not a MustSupport element, so it cannot be used in a quality measure or as a criteria for triggering a CDS action. However, it can be filled in as part of the recommendation of a CDS application.
Although the element may be returned in a resource when the resource is retrieved from an EHR, no logical processing involving that data element can be expected. Note that the MustSupport flag does not imply that the element will always have a value, if the lower cardinality is zero. The only assurance associated with MustSupport is that the quality improvement application will try to retrieve the data and process it if the data allows.
Specific applications can modify the profiles and set MustSupport flags to true if they will process additional elements, but setting a MustSupport flag from true to false is noncompliant.
A number of QI-Core profiles inherit directly from US Core profiles, if any, or other FHIR resources (i.e. USCoreImplantableDeviceProfile, USCore Pediatric BMI for Age, USCore Smoking Status etc.) and the underlying Reference elements can address the US Core or FHIR profiles for the items referenced. For any other references to base FHIR resources or those not formally defined in a QI-Core Profile, the referenced resource SHALL be a QI-Core Profile if a QI-Core Profile exists for the resource type. For example, USCore Smoking Status references US Core Patient profile, the reference to Patient SHALL be a valid QI-Core Patient.
In summary, MustSupport elements represent the minimal set of data elements that must be supported in quality applications, defined as follows:
Throughout the QI-Core profiles elements that are marked as required, meaning they have a minimum cardinality of 1, will also be marked as MustSupport. In the case of complex elements if the top level element is marked as MustSupport then any required sub-elements will be marked as MustSupport as well.
Within FHIR resources, some elements are considered Modifying Elements, indicating that the value of that element may change the interpretation of the resource. Examples of modifying elements include status (in many resources), negations (e.g. Immunization.wasNotGiven), and certainty qualifications (e.g. Observation.reliability). Decision support and quality implementations MUST always check the values of modifying elements. For example, in processing an Immunization resource, the application must inspect the “wasNotGiven” element to determine whether the immunization was given or was not given to the patient. For this reason, modifying elements SHALL be treated as MustSupport, even if not declared.
Two commonly used patterns for negation in quality measurement and decision support are:
For the purposes of quality measurement, when looking for documentation that a particular event did not occur, it must be documented with a reason in order to meet the intent. If a reason is not part of the intent, then the absence of evidence pattern should be used, rather than documentation of an event not occurring.
In particular, QI-Core defines several profiles that support explicit documentation of the fact that an activity or event did not occur. For these cases, the profiles define at least the following information:
doNotPerform or notDone)Note that although these aspects are all present within each negation profile defined by QI-Core, they manifest differently in different resources. As a result, each negation profile uses a combination of constraints and extensions to provide consistent representation of negated actions or events within QI-Core.
The following examples differentiate methods to indicate (a) presence of evidence of an action, (b) absence of evidence of an action, and (c) negation rationale for not performing an action. In each case, the “action” is an administration of medication included within a value set for “Antithrombotic Therapy”.
Evidence that “Antithrombotic Therapy” (defined by a medication-specific value set) was administered:
define "Antithrombotic Administered":
["MedicationAdministration": "Antithrombotic Therapy"] AntithromboticTherapy
where AntithromboticTherapy.status = 'completed'
and AntithromboticTherapy.category ~ "Inpatient Setting"
No evidence that “Antithrombotic Therapy” medication was administered:
define "No Antithrombotic Therapy":
not exists (
["MedicationAdministration": "Antithrombotic Therapy"] AntithromboticTherapy
where AntithromboticTherapy.status = 'completed'
and AntithromboticTherapy.category ~ "Inpatient Setting"
)
Evidence that “Antithrombotic Therapy” medication administration did not occur for an acceptable medical reason as defined by a particular value set (i.e., negation rationale):
define "Antithrombotic Not Administered":
["MedicationAdministration": "Antithrombotic Therapy"] NotAdministered
where NotAdministered.status = 'not-done'
and NotAdministered.statusReason in "Medical Reason"
In this example for negation rationale, the logic looks for a member of the value set “Medical Reason” as the rationale for not administering any of the anticoagulant and antiplatelet medications specified in the “Antithrombotic Therapy” value set. To report Antithrombotic Therapy Not Administered, this is done by referencing the canonical url of the “Antithrombotic Therapy” value set using the notDoneValueSet extension to indicate providers did not administer any of the medications in the “Antithrombotic Therapy” value set. By referencing the value set canonical url to negate the entire value set rather than reporting a specific member code from the value set, clinicians are not forced to arbitrarily select a specific medication from the “Antithrombotic Therapy” value set that they did not administer in order to negate.
Similarly, to report “Procedure, Not Performed”: “Cardiac Surgery” with a reason, the canonical url of “Cardiac Surgery” value set is referenced by using the value set extension to indicate providers did not perform any of the cardiac surgery specified in the “Cardiac Surgery” value set.
Note that the negation profiles can be used to make two different types of negative statements:
Each of the negation profiles provides an example illustrating both types of negative statement.
QI-Core defines the following profiles specifically for representing negation rationale:
The QICore ObservationNotDone profile SHOULD be used for all specific observation profile content including:
Quality Measure and Clinical Decision Support authors and implementers should be cautious to prevent a reason for not performing a single item from a value set as indication that the reason applies to all valueset members. This may become more problematic as automated data extraction progresses and directly impacts EHR implementation. Clinicians require a rapid way to document that none of the members of the negation set could be selected. Caution is required to prevent a single member selection from being interpreted as if all valueset members were selected.
This would be the most common use case. A less frequent need is to indicate that they did not do ONE of the members of the valueset. Stakeholders should understand that either a reason for not acting on a valueset or a single member from that value set meet criteria for the notDone expression.
Response to a query for a reason will result in fulfilling the criteria that meet the not-performed extension as long as two criteria have been met:
The reason the profile indicates the .code as qicore-notDoneValueSet is to allow a clinician to indicate “I did none of these” with the respective statusReason or doNotPerformReason. Implementer feedback suggests that clinicians prefer the “none of these” approach rather than a requirement to select a single element from a list. However, there are clinical situations in which a clinician will indicate a reason for not performing a specific activity that represents one of the members of a value set bound to a specific data element in a measure or a CDS.
Two examples of such a scenario:
Artifact developers should consider these facts when evaluating data retrieved as it pertains to measure intent and value set development. Implementers should consider these facts to consider providing data capture opportunities that limit practitioner burden.
Uniformity in vocabularies and value sets enhances the interoperability of knowledge artifacts, but also forces data owners to translate local data into the required vocabulary. As a US Realm product, QI-Core requires value sets and vocabularies referenced in the ONC Common Clinical Data Set (CCDS) and the US Core Data for Interoperability. Because QI-Core is expected to be applied outside the U.S. Realm, and also in clinical settings where local terminologies exist, U.S. Realm bindings could be accompanied by alternative codes as translation codes in the QI-Core profiles. In the case that the US Core Data for Interoperability adopts QI-Core and CQL, policy should be created to mandate the preferred bindings given in the standard.
Note that quality improvement artifact authors should pay close attention to binding parameters specified in the profiles to determine whether the value set defined in the binding is exemplar or should be constrained to a specific value set when used. For example, the code element of the MedicationRequest profile is bound to the complete value set for the RxNorm code system, indicating that all MedicationRequest instances shall use codes from the RxNorm code system, but within any given artifact, instances will typically use a restricted value set.
FHIR resources frequently contain references (pointers) to other FHIR resources. For example, Encounter.patient is a reference to a Patient resource. In QI-Core, most references are constrained to QICore-profiled resources. For example, QICore-Encounter.patient must point to a Patient resource that conforms to the QICore-Patient profile. Consequently, any extensions or bindings expected to exist in QICore-Patient are also present in the resource pointed to by Encounter.patient. References to QI-Core extensions accessed through references are guaranteed to be valid. References to resources that do not currently have QI-Core profiles are not constrained, and as such, only the core FHIR properties and bindings are guaranteed to exist.
A particular problem occurs when a resource reference permits any type of resource, such as Encounter.indication. When dealing with “Any” references, the current method of specifying profiles does not allow the profile author to specify something to the effect of “a QI-Core resource when there is one, and a FHIR core resource if there isn’t.” In QI-Core, the resources in “Any” references SHALL conform to QI-Core profiles if the base resource has been profiled.
Conformance to this QI-Core Implementation Guide requires the following (in addition to adherence to core FHIR requirements):
| Author Name | Affiliation | Role |
|---|---|---|
| Anne Smith | NCQA | Contributor |
| Ben Hamlin | NCQA | Contributor |
| Bryn Rhodes | Alphora | Editor |
| Chris Moesel | The MITRE Corporation | Contributor |
| Claude Nanjo | Originator | |
| Claudia Hall | Contributor | |
| Floyd Eisenberg | iParsimony, LLC | Primary |
| James Bradley | The MITRE Corporation | Contributor |
| Jason Walonoski | The MITRE Corporation | Contributor |
| Juliet Rubini | Mathematica | Contributor |
| Linda Michaelsen | Optum | Contributor |
| Mark Kramer | The MITRE Corporation | Originator |
| Jason Mathews | The MITRE Corporation | Originator |
| Lisa Anderson | Mathematica | Contributor |
| Lloyd McKenzie | Gevity Consulting | Contributor |
| Marc Hadley | The MITRE Corporation | Contributor |
| Paul Denning | The MITRE Corporation | Contributor |
| Peter Muir | ICF, Inc. | Contributor |
| Raman Srinivasan | IBM Watson Health | Contributor |
| Robert Samples | Contributor | |
| Sam Sayer | The MITRE Corporation | Contributor |
| Stan Rankins | Telligen | Contributor |
| Yan Heras | Optimum eHealth, LLC | Contributor |
| Yanyan Hu | The Joint Commission | Contributor |
IG © 2019+ http://www.hl7.org/Special/committees/cqi/index.cfm. Package hl7.fhir.us.qicore#5.0.0-ballot based on FHIR 4.0.1. Generated 2022-08-08
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