Microorganisms Linked to Health Care–Associated Infections: Modernization of Terminology Resources for Reporting to the National Healthcare Safety Network

The National Healthcare Safety Network (NHSN) of the Centers for Disease Control and Prevention (CDC) needed a modernized approach to manage resources containing standardized terminology that specify microorganism data submitted electronically for legacy reporting. Health care-associated infections (HAIs) reported to NHSN require the submission of data regarding specific microorganisms attributed to the patient's condition. Data on microorganisms submitted to the NHSN electronically must use the SNOMED CT terminology standard. Terminology artifacts that guide submission of microorganism data have been maintained in spreadsheets that have become increasingly challenging to manage. This case report details the initial use case for the implementation of off-the-shelf software within the NHSN to modernize the maintenance of terminology assets. Resources that guide reporting microorganisms for HAIs were used as a prototype to demonstrate how a software application can be practically implemented to streamline the maintenance of complex terminology assets. Mission-critical artifacts have been reconciled and consolidated into a single source of truth knowledgebase using an off-the-shelf software solution. This report shares progress and lessons learned regarding the modernization of NHSN's Pathogen Codes resource and its derivative artifacts. A model is now available that can be replicated across other NHSN legacy artifacts. Our experience can be applied to other public health use cases and information systems facing similar challenges with attachments to legacy terminology resources and systems.

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Publication Details

Journal
JMIR Public Health and Surveillance
Published
2024-12-04
DOI
https://doi.org/10.2196/66088
Primary Topic
Biomedical Text Mining and Ontologies
Type
article
Field-Weighted Citation Impact
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article

Microorganisms Linked to Health Care–Associated Infections: Modernization of Terminology Resources for Reporting to the National Healthcare Safety Network

Jonathan R. Edwards, Caroline L Macumber, Sheila Abner, Andrea L. Benin et al.
JMIR Public Health and Surveillance
Biomedical Text Mining and Ontologies
article

Microorganisms Linked to Health Care–Associated Infections: Modernization of Terminology Resources for Reporting to the National Healthcare Safety Network

Jonathan R. Edwards, Caroline L Macumber, Sheila Abner, Andrea L. Benin, Shuai Zheng, Shaun C Shakib
article en

Abstract

The National Healthcare Safety Network (NHSN) of the Centers for Disease Control and Prevention (CDC) needed a modernized approach to manage resources containing standardized terminology that specify microorganism data submitted electronically for legacy reporting. Health care-associated infections (HAIs) reported to NHSN require the submission of data regarding specific microorganisms attributed to the patient's condition. Data on microorganisms submitted to the NHSN electronically must use the SNOMED CT terminology standard. Terminology artifacts that guide submission of microorganism data have been maintained in spreadsheets that have become increasingly challenging to manage. This case report details the initial use case for the implementation of off-the-shelf software within the NHSN to modernize the maintenance of terminology assets. Resources that guide reporting microorganisms for HAIs were used as a prototype to demonstrate how a software application can be practically implemented to streamline the maintenance of complex terminology assets. Mission-critical artifacts have been reconciled and consolidated into a single source of truth knowledgebase using an off-the-shelf software solution. This report shares progress and lessons learned regarding the modernization of NHSN's Pathogen Codes resource and its derivative artifacts. A model is now available that can be replicated across other NHSN legacy artifacts. Our experience can be applied to other public health use cases and information systems facing similar challenges with attachments to legacy terminology resources and systems.

JMIR Public Health and SurveillanceVol. 11
Mount Carmel Health (US), National Center for Emerging and Zoonotic Infectious Diseases (US)
Openalex Percentile: Top 80%
Biomedical Text Mining and Ontologies
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