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Standardized Nursing Terminologies

Jane Englebright / Nicholas R. Hardiker / Tae Youn Kim

INTRODUCTION

The widespread adoption of electronic health records (EHRs) provides data from nursing practice that can be used to drive the continuous advancement of nursing science. The data generated by clinicians at the point of care can be harvested from the EHR, aggregated, combined with other data, and studied. The learnings from these analyses can then be incorporated into the documentation rubric within the EHR to facilitate practice change and generate more data.

Achieving this cycle from data to insights to improvement requires adopting a standardized terminology with coded concepts. These terminologies provide data elements in a standard format that can be combined with other data sources to evaluate care delivery and continuously improve practice. Standardized, coded data can be used as quality metrics and research metrics, and can facilitate analysis across different organizations (Englebright, 2014).

A standardized nursing terminology embodies nursing concepts that represent the domain of nursing. These essential building blocks for nursing practice can be integrated with the data of other healthcare disciplines to provide care to individual patients and can be aggregated to gain insights for the care of entire patient populations (McCormick et al., 1994).

This chapter provides the background necessary to understand standardized terminologies, concepts, and data elements. The chapter gives an overview of the characteristics of nursing terminologies, how they are developed, and how they are used in EHR systems. It considers the current “state of the science” of nursing terminology work and provides an example of how nursing terminologies can be used to solve real problems in nursing practice (Englebright, 2014).

Note that the word “terminology” is used throughout the chapter to refer also to “classification,” “vocabulary,” “taxonomy,” or “nomenclature.” Concepts and terms that are coded for computer processing are referred to as “data” or “data elements.”

HEALTH TERMINOLOGIES

Healthcare terminologies are widely used in administrative applications. Healthcare facilities have long used the International Classification of Diseases (ICD) to report mortality and morbidity statistics internationally (WHO, 1992). A number of administrations, including the U.S. federal government, have adopted this terminology for payment of healthcare services. Other specialty groups have created additional terminologies for payment of their specific services such as Current Procedural Terminology (CPT) for surgical procedures (AMA, 2014) and Logical Observation Identifiers Names and Codes (LOINC) for laboratory tests and assessments (Regenstrief Institute, 2014).

Administrative functions in healthcare, such as billing, were the first to be computerized. The widespread use of standardized, coded terminologies supported the rapid transformation of these functions. As these systems grew in sophistication, the industry and government regulators attempted to use these administrative data for measuring quality of care, patient outcomes, and resource consumption.

When computerization came to the clinical functions in healthcare, there was not a widely accepted set of standard, coded clinical terminologies to guide the development of EHRs (Elfrink, Bakken, Coenen, McNeil, & Bickford, 2001). In 2012, the Institute of Medicine (IOM) decried the deplorable state of clinical data, noting that patient care data is poorly captured and managed, and scientific evidence is poorly used. The report called for the capture of clinical care data in real time and at the point of care for better patient care coordination and management. The report also recommended that to be usable the data must be interoperable to support better care across the full continuum of patient care (IOM, 2012).

NURSING TERMINOLOGIES

Although the development of nursing terminologies preceded the IOM report, it was motivated by many of the same concerns. There was a need to quantify nursing resources, to effectively use the EHR systems that were entering the care environment, and to enable the application of a growing body of evidence-based nursing practice available in electronic knowledge bases (Saranto, Moss, & Jylha, 2010).

As early as 1859, Florence Nightingale named her six canons of care as “what nurses do” in her text Notes on Nursing (1859). She considered the six canons to be measures of “good standards” that are essential for the practice of nursing. It took another 80 years for her work to be expanded in the United States when Virginia Henderson published her Textbook of the Principles and Practices of Nursing (1939) in which she delineated her “14 patterns of daily living.” Her works were followed by the works of several nurse-theorists who presented their theories and standards of nursing practice such as King’s “Process of Nursing,” Roger’s “Four Building Blocks,” or Abdellah’s “21 Problems” (Fordyce, 1984). These models were all developed as approaches to patient care; however, none referred to or predicted the use of computers to support the implementation of nursing practice standards (Englebright, 2014).

In 1970 the American Nurses Association (ANA) approved the nursing process as the standard of professional nursing practice. The nursing process provides the framework for gathering patient care data, beginning with the assessment phase, through diagnosis, goal designation, planning, and evaluation (Yura & Walsh, 1983, pp. 152–155).

In 1989, the ANA’s Steering Committee on Databases to Support Nursing Practice created a process to recognize terminologies and vocabularies that support nursing practice (Table 8.1). ANA (2008) recognizes minimum data sets, interface terminologies, and reference terminologies that support nursing practice (Table 8.2).

Minimum Data Sets

The ANA recognizes two minimum data sets, the Nursing Minimum Data Set (NMDS) and the Nursing Minimum Management Data Set (NMMDS). Minimum data sets define an essential set of data elements for describing nursing practice or nursing management. Each data element has a standard definition and code that enables it to be used in a variety of settings and systems, maintaining the same meaning when moved from the originating system into a larger pool of data.

Nursing Minimum Data Set (NMDS)

The NMDS identifies essential, common, and core data elements to be collected for all patients/clients receiving nursing care (Werley & Lang, 1988). The NMDS generally includes three broad categories of elements: (a) nursing care, (b) patient or client demographics, and (c) service elements (Table 8.3). Many of the NMDS elements are consistently collected in the majority of patient/client records across healthcare settings, especially the patient and service elements. The NMDS is also being worked upon by a number of countries as the International Nursing Minimum Data Set (i-NMDS) (Westra, Matney, Subramanian, Hart, & Delaney, 2010).

Nursing Management Minimum Data Set (NMMDS)

Similar to the NMDS, the Nursing Management Minimum Data Set (NMMDS) defines 18 elements that are essential to support the management and delivery of nursing care across all types of settings (Kunkle et al., 2012). The elements are organized into three categories: environment, nursing care resources, and financial resources (Table 8.4) (Werley, Devine, Zorn, Ryan, & Westra, 1991). The NMMDS supports numerous constructed variables as well as aggregation of data, for example, unit level, institution level, and network level. This NMMDS provides the structure for the collection of uniform information that influences quality of patient care, directly and indirectly. The Environment and Nursing Care categories for the NMMDS have been reviewed, normalized to national data definition standards, and incorporated into LOINC (Regenstrief Institute, 2014); whereas the financial categories are excluded.

Interface Terminologies

Interface terminologies are designed for use at the point of care. They use terms and concepts that are familiar to practicing nurses. Interface terminologies vary in scope, structure, and content. They were developed by different organizations, with different funding sources, for different purposes, with different foci, and with different copyright privileges. Most of the early terminologies were initially developed for paper-based documentation systems. However, over time and with the advancement of technology, all of the nursing terminologies have been adapted for automated data processing and aggregation (ONC, 2017).

Some of the interface terminologies are very broad and have applicability to a variety of care settings; others are narrower. For example, CCC started as a home health care system, but has expanded to address both acute and community settings. Omaha began as a rehabilitation system and has expanded to additional settings. The Perioperative Nursing Data Set (PNDS) was developed for procedural areas and has maintained that specialty focus.

Reference Terminologies

The ANA also recognizes two reference terminologies, LOINC and SNOMED-CT (Nursing Resources for Standards and Interoperability, 2017). A reference terminology acts as a common reference point that can facilitate cross-mapping between interface terminologies.

SNOMED-CT SNOMED-CT was developed collaboratively by the College of American Pathologists (CAP) and the UK National Health Service (Wang, Sable, & Spackman, 2002). It now falls under the responsibility of SNOMED International. SNOMED-CT possesses both reference properties and user interface terms. SNOMED-CT is considered to be the most comprehensive, multilingual healthcare terminology in the world and integrates concepts from many nursing terminologies.

Before SNOMED International acquired SNOMEDCT from CAP, many of the ANA-recognized Interface Terminologies for Nursing were integrated into SNOMEDCT. SNOMED-CT is distributed at no cost in member countries by their national coordinating center such as the NLM in the United States. SNOMED-CT is one of a suite of designated standards for use for the electronic exchange of health information, and also is a required standard in interoperability specifications of the U.S. Health Information Technology Standards Panel (HITSP) (National Library of Medicine, 2019).

LOINC Logical Observation Identifiers Names and Codes (LOINC) was initiated in 1994 by Regenstrief Institute, a non-profit medical research organization associated with Indiana University. LOINC is a universal standard that is comprised of more than 71,000 observation terms primarily used to represent laboratory tests, measurements, and observations. It is also a clinical terminology for laboratory test orders and results, clinical measures such as vital signs, and other patient observations (LOINC, 2015). In 1999, LOINC was identified by the Health Level Seven (HL7) Standard Development Organization (SDO) as a preferred code set for laboratory test names in transactions between healthcare facilities, laboratories, laboratory testing devices, and public health authorities. In 2002, LOINC established a Clinical LOINC Nursing Subcommittee to provide LOINC codes primarily for patient assessments. LOINC is available at no cost and is also one of the suites of designated standards for use in U.S. Federal government systems for the electronic exchange of clinical health information (Nursing Resources for Standards and Interoperability, 2015).

Nursing Terminology Challenges

There is a movement to harmonize nursing and multidisciplinary terminologies. However, there are two major challenges. First, the existence of multiple, specialized terminologies has resulted in areas of overlapping content, areas for which there was no content, and large numbers of different codes and terms for the same concepts (Chute, Cohn, & Campbell, 1998; Cimino, 1998a). Second, existing terminologies most often were developed to provide sets of terms and definitions of concepts for human interpretation, with computer interpretation only as a secondary goal (Rossi Mori, Consorti, & Galeazzi, 1998). The latter is particularly true for the majority of nursing terminologies that have been designed primarily for direct use by nurses in the course of clinical care (Association of Operating Room Nurses [AORN], 2007; Martin, 2005; Saba & Taylor, 2007). However, EHR systems that support functionality such as decision support may require more granular (i.e., less abstract) data than may be found in today’s interface terminologies (Campbell et al., 1997; Chute, Cohn, Campbell, Oliver, & Campbell, 1996; Cimino, 1998b; Cimino, Hripcsak, Johnson, & Clayton, 1989); mapping interface terminologies to Reference terminologies may provide a solution.

Advanced Terminologies for Nursing

Healthcare terminologies suitable for implementation in EHR systems have been studied by numerous experts who have provided an evolving framework that enumerates a number of desirable characteristics. The characteristics apply to any terminology being used in the healthcare industry, including nursing. Advanced terminologies must be concept-oriented (with explicit semantics), rather than based on surface linguistics (Chute et al., 1998; Cimino, 1998b; Cimino et al., 1989). Other recommended criteria include domain completeness and a polyhierarchical organization. Additional criteria applying to concepts themselves include being atomic level (a single concept coded as a single data element), nonredundancy (unique identifier), nonambiguity (explicit definition), concept permanence (cannot be duplicated), compositionality (ability to combine concepts to form new unique concepts), and synonymy (a single concept supports multiple terms with same meanings) (de Keizer & Abu Hanna, 2000; Henry & Mead, 1997; Whittenburg, 2011; Zielstorff, 1998).

In order to appreciate the significance of concept orientation, it is important to understand the definitions of and relationships among things (objects) in the world, our thoughts (concepts) about things in the world, the labels (terms) we use to represent and communicate our thoughts about things in the world, and the coded data elements needed to represent and be processed by computer (Bakken et al., 2000; Moss, Damtongsak, & Gallichio, 2005). The terminology relationships are depicted by a descriptive model commonly called the semiotic triangle (Fig. 8.1) (Ingenerf, 1995; Ogden & Richards, 1923). The International Organization for Standardization (ISO), International Standard ISO: 1087-1:2000 provides definitions for elements that correspond to each vertex of the triangle (ISO, 2000).

• FIGURE 8.1. The Semiotic Triangle Depicts the Relationships among Objects in the Perceivable or Conceivable World (Referent), Thoughts about Things in the World, and the Labels (Symbols or Terms) Used to Represent Thoughts about Things in the World.

Concept (i.e., thought or reference): Unit of knowledge created by a unique combination of characteristics—a characteristic is an abstraction of a property of an object or of a set of objects.

Object (i.e., referent): Anything perceivable or conceivable.

Term (i.e., symbol): Verbal designation of a general concept in a specific subject field—a general concept corresponds to two or more objects which form a group by reason of common properties (ISO, 2000).

As specified by the criteria in Table 8.5 and illustrated in Fig. 8.2, a single concept may be associated with multiple terms (synonym).

• FIGURE 8.2. A Simple Graphical Example of a Formal Representation of the Nursing Activity Concept “Bladder Irrigation.”

Ontologies

Terminology models may be formulated and elucidated in an ontology language that represents classes (also referred to as concepts, categories, or types) and their properties (also referred to as relations, slots, roles, or attributes) such as Web Ontology Language (OWL) (Rector, 2004). In this way, ontology languages or terminologies are able to support, through explicit semantics, the formal definition of concepts and their relationships with other concepts (Fig. 8.2); they also facilitate reasoning about those concepts, for example, whether two concepts are equivalent or whether one concept, such as “vital sign,” subsumes (is a generalization of) another, such as “temperature, pulse, and respirations (TPR)” (Hardiker, Hoy, & Casey, 2000). Ontology languages are often used to support advanced terminologies. One example is the use of OWL to represent International Classification for Nursing Practice (ICNP).

Web Ontology Language (OWL)

Outside the health domain, work in relation to the Semantic Web has resulted in the recognition of OWL as an emerging standard (i.e., a W3C recommendation) (McGuiness & van Harmelen, 2004). OWL is intended for use where applications, rather than humans, process data. OWL builds on existing recommendations such as Extensible Markup Language (XML) (surface syntax for structured documents), Resource Description Framework (RDF) (a data model for resources), and RDF Schema (a vocabulary for describing the properties and classes of resources) by providing additional vocabulary and a formal semantics. Software, both proprietary and open source, is available for (a) managing terminology models or ontologies developed in OWL (e.g., Protégé, 2010) and (b) reasoning on the terminology model (e.g., FaCT++) (Tsarkov, 2009). ICNP is maintained in OWL—it is a compositional standards-based terminology for nursing practice (Hardiker & Coenen, 2007). An OWL representation (in XML) of the nursing activity concept “Bladder Irrigation” is provided in Table 8.6.

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