1. Matney, Susan A. PhD, RNC-OB, FAAN, FACMI, FHIMSS, FAMIA, FHL7
  2. Anderson, Lisa MSN, RN-BC

Article Content

The Logical Observation Identifiers, Names, and Codes (LOINC) Nursing Subcommittee is a sub-group of the Clinical LOINC Committee. The Clinical LOINC Committee is responsible for the development and maintenance of the clinical LOINC codes and reviews potential terms for inclusion in the LOINC database. The purpose of the Nursing Subcommittee is to facilitate the development and use of LOINC codes for observations used during key stages of the nursing process, including assessments, goals, and outcomes and also to meet the needs for administrative, research, and quality measurement initiatives related to nursing care.1 The Subcommittee focuses on the development of LOINC terms and education regarding interoperability standards. This article provides an update on the recent nursing LOINC content development and description of the education provided to the committee.



The Subcommittee is closely aligned with the Nursing Knowledge Big Data Initiative (NKBDI).2 The NKBDI is comprised of many workgroups including the Nursing Knowledge Encoding and Modeling Workgroup. The Nursing Knowledge Encoding and Modeling Workgroup develops nursing knowledge models, such as pain assessment, which are standardly encoded by the Encoding and Modeling Workgroup.3 The Encoding and Modeling Workgroup maps the data in the knowledge model to LOINC and SNOMED and requests new terms when none is found.4 The most recent models published in LOINC are multiple pain panels. The LOINC Pain Assessment Panel (see Table 1) includes all questions related to pain. In addition, several different pain assessment scales have been curated into LOINC including the Neonatal Pain, Agitation, and Sedation Scale, the Neonatal Infant Pain Scale, and the Checklist of Nonverbal Pain Indicators.5-7 Additional pain scales are being investigated. The Nursing Physiologic Assessment Panel was also developed by the NKBDI and contains panels for different body systems such as the Respiratory Assessment Panel and the Cardiovascular Assessment Panel.4 The NKBDI is developing genitourinary and falls risk knowledge models.

Table 1 - Click to enlarge in new windowTable 1 Pain Assessment Panel Example Members (LOINC Code 38212-7)

Future nursing LOINC development includes the Neonatal Pain, Agitation, and Sedation Scale; genitourinary assessment and falls, in collaboration with NKBDI; and the perioperative nursing data set outcomes, in collaboration with the Association of Operating Room Nurses. The committee is also advising on the development of a LOINC content request process with the NKBDI Nursing Knowledge Encoding and Modeling Workgroup.



The education sessions have focused on LOINC and additional interoperability efforts. Below is a list of recent education:


* LOINC Document Ontology Tutorial - David Baorto (Regenstrief)


* RELMA Tutorial - Susan Matney (Intermountain Healthcare)


* COVID Interoperability Alliance - Carol Macumber (Clinical Architecture)


* Nursing Knowledge Big Data Science Modeling Group


LOINC Copyright Process - Tess Settergren and Stephanie Hartleben


Mapping Heuristics Document - Tess Settergren and Stephanie Hartleben


Nursing Knowledge Modeling



The slides from the education sessions as well as meeting minutes are freely available on the Nursing Clinical LOINC Subcommittee page (available from


We welcome prospective new committee members who have an interest in LOINC (ie, you are a LOINC user), are willing to participate actively, and have demonstrated commitment to LOINC as indicated by prior attendance (in-person or virtual). Information as to how to join can be found in the link above. We look forward to enabling the interoperability of nursing data.




1. Regenstrief Institute Inc. Nursing subcommittee. 2021. [Context Link]


2. Delaney CW, Weaver C. The 7th nursing knowledge: big data conference brings remarkable accomplishments and shows staying power on key fronts. Computers, Informatics, Nursing. 2019;37(9): 444-445. [Context Link]


3. Westra BL, Johnson SG, Ali S, et al. Validation and refinement of a pain information model from EHR flowsheet data. Applied Clinical Informatics. 2018;9(1): 185-198. [Context Link]


4. Matney SA, Settergren TT, Carrington JM, Richesson RL, Sheide A, Westra BL. Standardizing physiologic assessment data to enable big data analytics. West J Nurs Res. 2017;39(1): 63-77. [Context Link]


5. Ersek M, Polissar N, Neradilek MB. Development of a composite pain measure for persons with advanced dementia: exploratory analyses in self-reporting nursing home residents. Journal of Pain and Symptom Management. 2011;41(3): 566-579. [Context Link]


6. Hudson-Barr D, Capper-Michel B, Lambert S, Palermo TM, Morbeto K, Lombardo S. Validation of the pain assessment in neonates (PAIN) scale with the neonatal infant pain scale (NIPS). Neonatal Network. 2002;21(6): 15-21. [Context Link]


7. Hummel P, Puchalski M, Creech SD, Weiss MG. Clinical reliability and validity of the N-PASS: neonatal pain, agitation and sedation scale with prolonged pain. Journal of Perinatology. 2008;28(1): 55-60. [Context Link]