National Healthcare Safety Network (NHSN) Digital Quality Measure (dQM) Reporting Implementation Guide, published by HL7 International / Public Health. This guide is not an authorized publication; it is the continuous build for version 1.0.0-ballot built by the FHIR (HL7® FHIR® Standard) CI Build. This version is based on the current content of https://github.com/HL7/nhsn-dqm/ and changes regularly. See the Directory of published versions
Official URL: http://hl7.org/fhir/us/nhsn-dqm/ImplementationGuide/hl7.fhir.us.nhsn-dqm | Version: 1.0.0-ballot | |||
IG Standards status: Trial-use | Maturity Level: 1 | Computable Name: NHSNdQM | ||
Other Identifiers: OID:2.25.316204395913842452684237438142819890580 |
This implementation guide (IG) specifies standards for electronic submission of surveillance data to National Healthcare Safety Network (NHSN) of the Centers for Disease Control and Prevention (CDC). This is part of CDC’s efforts to modernize public health reporting by using Health Level Seven International® (HL7) Fast Healthcare Interoperability Resources® (FHIR) data-exchange standards. This project builds on existing work such as the Quality Measure Implementation Guide and Data Exchange for Quality Measures Implementation Guide efforts. This IG contains a library of FHIR profiles and example digital quality measures (dQMs) for reporting to NHSN. It is expected that production dQMs from NHSN will eventually be hosted here (though no content is there at the time of this ballot).
Note that reporting dQM data to NHSN requires enrollment in NHSN, signing of NHSN data-use agreements, and completion of the dQM reporting plans, which are part of the NHSN participation process and are not defined by this specification. For an overview of NHSN and full information on NHSN participation requirements, see here. Provisions of the Public Health Service Act protect all data reported to NHSN from discovery through the Freedom of Information Act (FOIA).
This specification defines the general requirements for submissions to NHSN directly from a facility or through an external dQM-evaluation engine.
In alignment with CDC’s Data Modernization Initiative, NHSN is implementing automated measures for public health surveillance via digital quality measures (dQMs). These dQMs were created to minimize the burden of reporting; improve the accuracy, quality, and validity of data collected by NHSN; and increase speed and efficiency of public health surveillance.
Specifications in this IG are a transition from static IGs for specific purposes, and onto a quality measure-driven approach. Specifications in this IG define the overarching framework based on the existing Quality Measure (QM) and Data Exchange for Quality Measures (DEQM) implementation guides and some baseline profiles, and value sets for NHSN. Once established, NSHN can then create dQMs with related value sets, Clinical Quality Language (CQL) libraries, and other artifacts that comply with this framework for specific use cases.
The dQMs are dynamic, executable artifacts that are not a balloted part of this specification (though this IG does contain example dQMs). Thus, the criteria in compliant measures can be adjusted as needed while minimizing ballot/publish/implement cycle. A separate IG will define the NHSN dQMs data requirements and will be hosted by NHSN.
Also, this project focuses on leveraging data compliant with existing EHR FHIR Application Programming Interfaces (APIs) wherever possible, such as US Core. While it is hoped that QI Core compatible content will become widely available from EHR FHIR APIs, this project does not currently require QI Core as a minimum baseline for data capture.
This project will require coordination between NHSN and other branches of CDC, along with CMS.
The audience for this work is all developers who want to enable their software systems to report surveillance data to the NHSN via FHIR dQMs.
This IG defines the standard framework for reporting data to NHSN using a dQM with an initial population which may include all inpatient, emergency department, and observation encounters and line-level data that could be used for stratification, benchmarking and/or risk adjustment. This IG is not intended to define how to operationalize implementing reporting a specific NHSN dQM.
However, two example measures are provided in this IG: an acute care hospital (ACH) dQM and an automated bed capacity data collection measure. The ACH dQM defines the population of interest as all encounters with an inpatient, ED, or Observation status or an inpatient, ED, or Observation location. The bed-capacity data collection dQM defines the fields necessary for reporting such as AllBedsOccupied, AdultTotalOccupied, etc.
The framework for the HL7 FHIR dQMs reported to NHSN has the following dependencies:
Package hl7.fhir.uv.extensions.r4#1.0.0 This IG defines the global extensions - the ones defined for everyone. These extensions are always in scope wherever FHIR is being used (built Sun, Mar 26, 2023 08:46+1100+11:00) |
Package hl7.fhir.uv.bulkdata#2.0.0 FHIR based approach for exporting large data sets from a FHIR server to a client application (built Fri, Nov 26, 2021 05:56+1100+11:00) |
Package hl7.fhir.r4.examples#4.0.1 Example resources in the R4 version of the FHIR standard |
Package hl7.fhir.uv.sdc#3.0.0 The SDC specification provides an infrastructure to standardize the capture and expanded use of patient-level data collected within an EHR. |
Package ihe.formatcode.fhir#1.1.0 Implementation Guide for IHE defined FormatCode vocabulary. (built Thu, Feb 24, 2022 16:55-0600-06:00) |
Package hl7.fhir.us.core#6.1.0 The US Core Implementation Guide is based on FHIR Version R4 and defines the minimum conformance requirements for accessing patient data. The Argonaut pilot implementations, ONC 2015 Edition Common Clinical Data Set (CCDS), and ONC U.S. Core Data for Interoperability (USCDI) v1 provided the requirements for this guide. The prior Argonaut search and vocabulary requirements, based on FHIR DSTU2, are updated in this guide to support FHIR Version R4. This guide was used as the basis for further testing and guidance by the Argonaut Project Team to provide additional content and guidance specific to Data Query Access for purpose of ONC Certification testing. These profiles are the foundation for future US Realm FHIR implementation guides. In addition to Argonaut, they are used by DAF-Research, QI-Core, and CIMI. Under the guidance of HL7 and the HL7 US Realm Steering Committee, the content will expand in future versions to meet the needs specific to the US Realm. These requirements were originally developed, balloted, and published in FHIR DSTU2 as part of the Office of the National Coordinator for Health Information Technology (ONC) sponsored Data Access Framework (DAF) project. For more information on how DAF became US Core see the US Core change notes. (built Fri, Jun 30, 2023 14:02+0000+00:00) |
Package hl7.fhir.uv.cpg#1.0.0 Implementation guidance for creating Clinical Practice Guidelines with formal artifacts to facilitate sharing and implementation of the guideline (built Thu, Feb 11, 2021 20:29+0000+00:00) |
Package fhir.cqf.common#4.0.1 This implementation guide contains common FHIR assets for use in CQFramework content IGs, including FHIRHelpers and the FHIR-ModelInfo libraries. (built Fri, Nov 12, 2021 16:25+1100+11:00) |
Package hl7.fhir.us.core#5.0.1 The US Core Implementation Guide is based on FHIR Version R4 and defines the minimum conformance requirements for accessing patient data. The Argonaut pilot implementations, ONC 2015 Edition Common Clinical Data Set (CCDS), and ONC U.S. Core Data for Interoperability (USCDI) v1 provided the requirements for this guide. The prior Argonaut search and vocabulary requirements, based on FHIR DSTU2, are updated in this guide to support FHIR Version R4. This guide was used as the basis for further testing and guidance by the Argonaut Project Team to provide additional content and guidance specific to Data Query Access for purpose of ONC Certification testing. These profiles are the foundation for future US Realm FHIR implementation guides. In addition to Argonaut, they are used by DAF-Research, QI-Core, and CIMI. Under the guidance of HL7 and the HL7 US Realm Steering Committee, the content will expand in future versions to meet the needs specific to the US Realm. These requirements were originally developed, balloted, and published in FHIR DSTU2 as part of the Office of the National Coordinator for Health Information Technology (ONC) sponsored Data Access Framework (DAF) project. For more information on how DAF became US Core see the US Core change notes. (built Wed, Jun 22, 2022 19:44+0000+00:00) |
Package hl7.fhir.us.qicore#5.0.0 The QICore 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. (built Tue, Apr 4, 2023 13:39+0000+00:00) |
Package hl7.fhir.us.cqfmeasures#4.0.0 The Fast Healthcare Interoperability Resource (FHIR) Quality Measure Implementation Guide (this IG) describes an approach to representing Quality Measures (QMs) using the FHIR Clinical Reasoning Module and Clinical Quality Language (CQL) in the US Realm. However, this Implementation Guide can be usable for multiple use cases across domains, and much of the content is likely to be usable outside the US Realm. (built Mon, Aug 28, 2023 20:07+0000+00:00) |
Package hl7.fhir.us.qicore#6.0.0 The QICore 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. (built Fri, Mar 1, 2024 18:46+0000+00:00) |
Package hl7.fhir.uv.extensions#5.1.0 This IG defines the global extensions - the ones defined for everyone. These extensions are always in scope wherever FHIR is being used (built Sat, Apr 27, 2024 18:39+1000+10:00) |
Package hl7.fhir.uv.saner#1.0.0 The Situational Awareness for Novel Epidemic Response Implementation Guide enables transmission of high level situational awareness information from healthcare facilities to centralized data repositories to support the treatment of the novel coronavirus illness. (built Tue, Sep 7, 2021 19:01+0000+00:00) |
This implementation guide is a product of the HL7 International–Public Health Work Group.
Content in this implementation guide was produced and developed by Lantana Consulting Group under contract to the Division of Healthcare Quality Promotion (DHQP) in the National Center for Emerging and Zoonotic Infectious Diseases (NCEZID) at CDC.
Primary Editor | Rick Geimer | Lantana Consulting Group | rick.geimer@lantanagroup.com |
Primary Editor | Corey Spears | Lantana Consulting Group | corey.spears@lantanagroup.com |
Co-Editor | Andrea Benin | Chief, Surveillance Branch, DHQP, CDC | aqb4@cdc.gov |
Co-Editor | Kristina Betz | NHSN Measure Development and Validation Unit Lead, CDC | rly7@cdc.gov |
Co-Editor | Amrit Kerr | Project Manager, Lantana Consulting Group | amrit.kerr@lantanangroup.com |
Co-Editor | Sheila Abner | CDC | sha8@cdc.gov |
Co-Editor | Nadine Shehab | Lantana Consulting Group | ftn0@cdc.gov |
Co-Editor | Jennifer Watkins | CACI | nub7@cdc.gov |
Co-Editor | Raymond Dantes | CDC | vic5@cdc.gov |
Co-Editor | David deRoode | Lantana Consulting Group | david.deroode@lantanagroup.com |
Co-Editor | Zabrina Gonzaga | Lantana Consulting Group | zabrina.gonzaga@lantanagroup.com |
Co-Editor | Sean McIlvenna | Lantana Consulting Group | sean.mcilvenna@lantanagroup.com |
Co-Editor | Shanai Thornton | Lantana Consulting Group | shanai.thornton@lantanagroup.com |