Organize your clinical documents with the LOINC Document Ontology
Inside many EHRs and clinical data warehouses it is a bit like your clothes closet — not as tidy as you'd like.
This is especially true if you're aggregating data across several sites. There's often a mishmash of note titles like Dr. Smith's Tuesday Pain Clinic, Chronic Pain Clinic, and Outpatient Pain Note.
Here's where the LOINC Document Ontology comes in
The LOINC Document Ontology is a special set of LOINC codes that are built on a framework for naming and classifying the key attributes of clinical documents. They provide consistent semantics for documents exchanged between systems for many uses.
When you link your local note titles to LOINC codes, instead of cryptic and idiosyncratic note titles, you'll have a principled set of document name attributes. With these systematic attributes, it's easy to create a logical navigation tree in your document viewer or write hyper-efficient queries that pull back all the cardiology notes or discharge summaries.
The LOINC Document Ontology provides the framework you've been missing
Get the world's leading comprehensive document naming framework
The LOINC Document Ontology represents the five key attributes of clinical documents that can be understood across systems.
Subject Matter Domain
e.g. Cardiology, Pediatric Cardiology, Physical Therapy
e.g. Physician, Nurse, Case Manager, Therapist, Patient
e.g. Hospital, Outpatient, Emergency Department
Type of Service
e.g. Consultation, History and Physical, Discharge Summary
Kind of Document
e.g. Note, Letter, Consent
Support for multiple levels of granularity
Some institutions like to have distinct documents for different provider roles (nurse, case manager, etc.). Others don't.
Some institutions like to distinguish among care settings in the document name. Others don't.
With the power of the LOINC Document Ontology, you can effectively group and organize documents coming from disparate sites and different preferences into a systematic framework. Organize the chaos, but retain the underlying detail.
Used worldwide in HL7's CDA standard
The HL7 Clinical Document Architecture (CDA) standard specifies that the clinicalDocument.code for any CDA document should come from LOINC. Implementation guides like the Consolidated CDA Templates for Clinical Notes that are part of the Meaningful Use regulations require LOINC codes to identify the document types. LOINC codes from the Document Ontology are required in the C-CDA value sets for documents such as consult notes, discharge summaries, progress notes, procedure notes, op notes, and more.
Browse the current version of the LOINC Document Ontology
See the attribute values and organization of each axis.
Articles and resources
A detailed guide for implementing the LOINC Document Ontology within systems as a foundation for powerful archiving, data mining, and information exchange based upon document types.
- Normalizing Clinical Document Titles to LOINC Document Ontology: an Initial Study.
AMIA Annu Symp Proc, 2021-01-25
Xu Zuo, Jianfu Li, Bo Zhao, Yujia Zhou, Xiao Dong, Jon Duke, Karthik Natarajan, George Hripcsak, Nigam Shah, Juan M Banda, Ruth Reeves, Timothy Miller, Hua Xu
- Assessing the adequacy of the HL7/LOINC Document Ontology Role axis.
J Am Med Inform Assoc, 2014-10-28
Sripriya Rajamani, Elizabeth S Chen, Mari E Akre, Yan Wang, Genevieve B Melton
- Standardizing Clinical Document Names Using the HL7/LOINC Document Ontology and LOINC Codes.
AMIA Annu Symp Proc, 2010-11-13
Elizabeth S Chen, Genevieve B Melton, Mark E Engelstad, Indra Neil Sarkar
- Iterative evaluation of the Health Level 7--Logical Observation Identifiers Names and Codes Clinical Document Ontology for representing clinical document names: a case report.
J Am Med Inform Assoc, 2009-03-04
Sookyung Hyun, Jason S Shapiro, Genevieve Melton, Cara Schlegel, Peter D Stetson, Stephen B Johnson, Suzanne Bakken
- Biomedical ontologies in action: role in knowledge management, data integration and decision support.
Yearb Med Inform, 2008
- Toward the creation of an ontology for nursing document sections: mapping section names to the LOINC semantic model.
AMIA Annu Symp Proc, 2006
Sookyung Hyun, Suzanne Bakken
- Document ontology: supporting narrative documents in electronic health records.
AMIA Annu Symp Proc, 2005
Jason S Shapiro, Suzanne Bakken, Sookyung Hyun, Genevieve B Melton, Cara Schlegel, Stephen B Johnson