Introduction

Misinterpretations that arise in every day personal and professional discussions highlight the critical role of vocabulary in communication generally, and in practice specifically. Without the common understanding that comes from vocabulary, these misinterpretations extend to affect care delivery, practice patterns, role differentiation, and, ultimately, patient and organizational outcomes, including quality and costs of care. Communicating information is necessary to objectively make health care decisions, yet information is useless without vocabulary.

The health care industry has a dearth of data upon which to base decisions. In fact, O'Connor (1998) cites "bad data" as the primary reason those decreased health expenditures cannot be attributed to managed care: existing data do not allow that linkage to be fully explored. Further, "bad data" may be one of the reasons that health care expenditures are expected to rise in the future: the costs of data collection and health information system development or redesign can be prohibitive if both direct and indirect costs are considered, and the burden of those costs likely will be passed on to individuals, communities, and society.

Data for making health care decisions are deficient, not simply because of an insufficient amount of data, but because of an insufficient amount of the right type of data. Existing data consists of large amounts of claims data, some administrative and clinical data, and minimal outcomes, quality, and comprehensive cost data. Healthcare environments are creating large databases and repositories, but various healthcare groups are struggling to collect data and build a case for support to collect data that document the contributions of different skill levels and types of healthcare professionals and nonprofessionals. Jacox (1992) addressed the relevance of this issue for nursing, and noted that databases are needed to clearly distinguish care delivered by individual and/or groups of nurses.

In the quest to gather data related to nursing, vocabulary is essential to communicate information and guide data collection. However, there is general disagreement about what the vocabulary should be or how the vocabulary or vocabularies should be developed. This paper will propose those multiple levels of nursing vocabulary, classifications, and taxonomies that will be needed in the future. A model will be presented to demonstrate that multiple vocabularies, classifications and taxonomies are needed, convergence is necessary at a certain level, and that a unicode or unified taxonomy is required if global, international, worldwide, or universal comparisons of nursing care are to be made. Given that knowledge development begets debate, this paper aims to provoke such discourse. Key terms relevant to this discussion are shown in Table 1.

Background

Nursing classification schema have been developed at varying levels of abstraction and offered as ways to organize and categorize nursing phenomena, such as the North American Nursing Diagnosis Association (NANDA) Nursing Diagnosis Taxonomy (Warren & Hoskins, 1995), the Omaha System (Martin & Scheet, 1995), the Nursing Interventions Classification (NIC) (Bulechek, McCloskey, & Donahue, 1995), and the Nursing Outcomes Classification (NOC) (Johnson & Maas, 1995). However, at a more basic level, the question arises, why does the discipline of nursing need a taxonomy or taxonomies?

Table 1.
Relevant Terms and Definitions
Term	Definition
Classification	A systematic arrangement of classes; a structural framework arranged according to similar groups (Lang, et al, 1995).
Database	A collection of interrelated files with records organized and stored together in a computer system (Lang, et al, 1995).
Language	A set of characters, conventions, and rules used to convey ideas and information (Lang, et al, 1995).
Taxonomy	Method of classifying a vocabulary of terms for a specific topic according to specific laws or principles (Lang, et al, 1995).
Terminology	Words and/or phrases used to describe a concept or phenomenon
Unified	Linked, affiliated, associated to make into a unit or coherent whole (McCormick, 1988a)
Uniform	Having the same or similar form with others; constant (McCormick, 1988a)
Vocabulary (Nomenclature)	The stock or repertoire of words from which to name or describe phenomena within a language or knowledge base (Lang, et al, 1995)

Several reasons can be identified to explain this need. First, employers, insurers, providers, payers, and policy-makers require objective, science-based information to make critical decisions about meeting health care demands, supplying health care services in the marketplace, providing access to services, allocating resources, and determining the costs and quality of care. Second, market forces, or competition, necessitates accountability, and documentation is required to substantiate accountability for processes and outcomes of care. Data on outcomes related to quality and costs will provide information to the public, and ultimately enable consumers and payers to determine health care-related value, and make tradeoffs in level of quality desired for a given cost. Finally, data are a requisite for conducting empirical research; hence, researchers need data to answer research questions related to practice, identify "best" practices, develop and test models of care, design decision-support models, and determine efficient and effective utilization of resources. While no one data source will meet all of these needs, a common vocabulary will enable linkages among data sources to be made.

A discussion of "taxonomy" of any kind naturally polarizes individuals and groups on a philosophical level, given that individuals and groups conceptualize and classify phenomena in different ways. In fact, the question of whether nursing needs one or more taxonomies or many conjures up thoughts of similar disciplinary debates about nursing theory and research methodology: does the discipline need one or many? Each side of the debate has certain advantages and disadvantages, and in some cases, a disadvantage of one side becomes the advantage of the other. The major advantage of advocating one taxonomy is simplicity - if there is one taxonomy, then there is the assumption that everyone is or will be made aware of it, understands the vocabulary and classifications, accepts it, and utilizes the known taxonomy. The existence of one taxonomy eliminates confusion over terminology and meaning, and necessitates disciplinary agreement about the vocabulary and classifications. For example, most nations use the International Classification of Diseases Version 9 or 10 (ICD-10) to describe country-level mortality and health care costs, which, in turn, facilitates the comparison of medical conditions across countries.

However, the opposing argument in a discussion of taxonomy is readily apparent in a national health care environment that supports many types of health care delivery, and that implies multiple service options, open discourses about those options, and the freedom to choose amongst options. For example, Henry (1997) emphasizes that existing nursing classification systems recognized by the American Nurses Association (e.g., NANDA, NIC, NOC) are not sufficient to reflect the entire scope of nursing practice. Additionally, the argument for one taxonomy assumes that one taxonomy would be learned, interpreted, and operationalized in a similar fashion across all individuals who practice nursing. This assumption is clearly not reasonable. For these reasons and others, nurse scholars have debated and opposed the advocacy of one taxonomy for nursing.

From a social policy and economic perspective, one taxonomy is inconsistent with the shift toward devolution of power and increasing autonomy to individual states. For example, certain funds are released to states in the form of block grants. Medicaid, the joint federal and state program aimed at providing health care coverage to the poor, provides shared reimbursement from federal and state governments, the largest proportion of which comes from state sources. In turn, many of the Medicaid regulations are established and implemented at the state level, within certain federal guidelines and limited federal oversight. This variability in funding and decision-making at the state level suggests that there will be variability in health care needs, resource allocation, health care service delivery, and monitoring mechanisms, including data collection, across states. Hence, variability across states and geographic areas necessitates variability of data elements that will be collected across states and regions.

Simultaneously, within health care, responsibility and accountability for decision-making is increasingly decentralized to the clinician closest to the point of care delivery '” and, ultimately, and/or to the extent possible, to the individual patient or family. This effort should, in theory, improve the quality of service delivered to the consumer, increase autonomy and job satisfaction for the provider, and decrease negative aspects of the practice environment. In both policy and management, however, the trend toward increasing autonomy, whether to states or individuals, can shift easily back toward a more centralized mode of decision-making whenever dramatic changes occur within political or management authority. These possible shifts necessitate that data are captured in a manner that allows integration across levels and sites of care delivery, providers, and realms of management and control.

The term taxonomy refers to a hierarchical system. As defined in Table 1, taxonomy comprises vocabulary and terms; in turn, vocabulary is made up of terms, or names at the most basic level. This hierarchical system is similar to that of a theoretical system, whereby theories comprise constructs, constructs consist of concepts, and so forth. An important assumption of these two systems is that there are relationships between the levels. While discussions of taxonomy within the context of informatics is not typically linked to theory, the analogy of the two systems is particularly relevant to this discussion, given that useful taxonomies are linked to significant theory (Benzon at www.newsavanna.com/wlb/CE/Arena/Arena07/shtml).

This paper proposes a model that demonstrates where multiple vocabularies, classifications, and taxonomies may be needed, and where they need to converge to fewer taxonomies, and finally at what level a unicode or unified taxonomy might be required if global (international, worldwide, or universal) comparisons are to be made of nursing care.

A Vocabulary Framework

Figure 1 depicts a framework that provides conceptual insight into vocabulary needs for policy and management practice decision-making in healthcare. This model builds on Eisenberg's (1998) discussion of healthcare as occurring at the levels of society, health systems, and clinical practice. Similarly, management decision-making occurs at the levels of population, group, and individual. For example, clinical management decisions focus on case and care management: at the individual level through assessment, planning, implementation, and evaluation of an individual's plan of care; at the group level as coordination and facilitation of care across a group of patients with similar care needs; and at the population level as integration of care across aggregates of similar and different groups with common health care needs, most notably morbidity, mortality, health promotion and disease prevention. This figure denotes the reciprocal relationship between management and policy decision-making, and the incorporation of vocabulary at all levels of decision-making.

This model assumes that there are three types of vocabularies needed in health care. The point of care is an "interface" vocabulary that occurs at the individual and practice level, and includes terms that are used between clinician and patient, and/or clinician and clinician to describe and convey related patient and clinical information. Vocabularies at the point of service level emphasize the settings where care is delivered, and are often discipline or specialty focused. Obviously, different vocabularies and classifications are needed to represent point of care vocabulary in nursing across the continuum of care (e.g., prevention to primary care to sub-acute care, to acute care, etc.). Examples of existing information systems that provide interface vocabulary to support decision-making at the patient care level are Oceania (www.oceania.com) and a Canadian information system called Purkinje (www.purkinje.com).

Network is represented by terms and phrases that serve as a "reference" vocabulary to link clinicians' documentation across horizontally or vertically integrated systems of care delivery (e.g., a hospital system or primary care clinic system, and health maintenance organization, respectively). Reference vocabularies are based on knowledge derived through interface, and reflect an integration and classification of knowledge. Thus, the individual practice encounter is used to build information and knowledge for decision-making at a group or network level. Vocabularies at this higher level of abstraction synthesize knowledge from multiple settings, disciplines and specialties interacting at the point of care, integrate and classify that knowledge, and build information and knowledge for decision-making to link group and system decisions.

Table 2.

Rationale for use of a reference health care vocabulary in measuring quality, outcomes,and evidence research in nursing

Provides the link between health care knowledge found in the literature and inquiring health professionals who need to keep up with the literature Facilitates examination of community nursing practice which requires uniformity of clinical data and concepts, or accurate mapping of vocabulary terms that have the same meaning or refer to the same concept Enables the evaluation of practice innovation impacts, such as practice guidelines, evidence, and other nursing knowledge, on the practitioner closest to the point of patient care, who requires a vocabulary infrastructure that is consistent with financial, administrative, and clinical databases Provides the ability to conduct research on patient outcomes by linking patient records and patient outcome data to examine the impact of patient conditions and clinical treatments on patients' health status, return-to-work, quality of life, and other outcomes. Facilitates the integration of new development tools used by purchasers and producers of health care with clinical, financial, and administrative vocabularies to fully examine quality in health care

There are currently no existing systems at the network level that link all health professional vocabularies within systems or groups of providers. However, this level of vocabulary is essential to measuring and monitoring quality, examining health outcomes, determining effectiveness of health care delivery, and developing an evidence base for practice. Table 2 provides further rationale for the network level of vocabulary. SNOMED International (Systematized Nomenclature of Human and Veterinary Medicine) is a complex but comprehensive classification system for "indexing the entire medical record, including signs and symptoms, diagnoses, and procedures. Its unique design will allow full integration of all medical information in the electronic medical record into a single data structure." (http://snomed.org). SNOMED is investing money to develop a reference or network level health care vocabulary for the United States. Other U.S. vocabularies that are considered beginning network vocabularies are MEDCIN, MEDICOMP, and Dr. Elmer Gabrieli's natural language processing (Gabrieli, 1993).

Universal or "administrative" vocabulary is the highest level of vocabulary, and links information on populations of people in a community, state, country, or globally (e.g., ICD- 9 or -10 coding system from the World Health Organization, or an International Classification of Nursing Practice (ICNP)). Universal vocabularies build on knowledge and information obtained at the point of service and network levels, and reflect the highest level of integration and synthesis of knowledge, and a combination of vocabularies for societal and population decision-making. For example, population statistics from a community, state or country are synthesized and analyzed to identify health care needs, and the numbers of people who die from certain disease conditions. At the international level the World Health Organization can determine the major conditions causing mortality within different countries by age groups and across the world. Universal vocabulary currently guiding decisions in global health care is the International Classification of Diseases (ICD) Versions 9 and 10 (CM is the Clinical Modification used in the U.S.). The ICD does not completely integrate population and society data, but reflects primarily medical diagnoses and phenomena. The ICD-9-CM is the classification used by the Health Care Financing Administration (HCFA) to reimburse for care delivered to Medicare and Medicaid recipients in the United States.

From Point of Care to Universal

Figure 2 further explicates nursing vocabulary needs from the point of care through universal levels. This figure points out that, at the point of care, nursing needs several vocabularies to communicate relevant information gathered during the patient encounter. These point of service-level vocabularies are used for documentation purposes to translate information into patient records; in turn, information from these records is extracted electronically and communicated to the network level. At this level, vocabularies are aggregated to higher level network classifications. This aggregation of terms into network classifications decreases the likelihood of errors in interpretation that may result from variability in vocabularies, terms, and definitions used at the point of service.

If all the nurses in a network such as Kaiser Permanente used different vocabularies and classifications, the costs and burden of linking information, while technically feasible, will be more time consuming and costly. The convergence of data elements or records from the network level into meaningful national or international data repositories at the universal level will require, for simplicity, a unicode or single taxonomy. Further, with comparisons between regions of a country, which are based on different definitions of terms, different vocabularies and different classifications, the likelihood of error in definition, interpretation, and aggregation would be more common.

As an example, to clarify these relationships, consider the nurse providing care in schools. This nurse uses terms and phrases that convey relevant patient level information unique to the population. Although some terms and phrases used obviously would cross over into other points of care, such as primary care, certain vocabulary would be necessary for understanding particular situations unique to the school. Information gathered by the nurse at the school point of service would be communicated through one or more systems to the network level. At this level, information would be classified into network classifications, and subsequently channeled to data repositories at the universal level. When, and if, nursing has a universal taxonomy, it is possible that even the point of care and network vocabularies and classifications could converge with the universal level taxonomy.

Table 3.
Examples of changes or consistency across levels of use.
Level of Use	Different	Similar
Point of care	ear pain	Stress incontinence
Network	ear infection, left ear	Stress incontinence
Universal	Otitis Media, without effusion	Stress incontinence

An example of a concept taken from the point of care to the reference and finally to the universal levels is in Table 3. The first example demonstrates that a vocabulary can change from point of care to network to universal levels for a condition such as ear pain. The second example shows a consistency or a unicode that can be and is being used at all three levels to describe stress incontinence.

The UMLS as Rosetta Stone

The Rosetta Stone as a metaphor for classification and vocabulary challenges in health care provides important insight into our current dilemma. The Stone, discovered by one of Napoleon's officers invading Egypt in 1799, unlocked the meanings behind the ancient language of Egypt (http://tqd.advanced.org/3011/egypt1.htm). Up until the Rosetta Stone was discovered, three distinct and untranslatable languages comprised the ancient Egyptian language: Egyptian Hieroglyphs, Demotic, and Greek. The Stone enabled the ancient language to be decoded because it contained three inscriptions of specific terms and concepts across the three different scripts.

In nursing and health care the development of terminology and vocabularies has occurred via disciplinary knowledge development, and without a "Rosetta Stone," or system to link various vocabularies. Vocabularies have evolved within disciplines, specialties, and settings; yet a system is needed to cross and link the terms used in health care.

The equivalent to the Rosetta Stone in the U.S. is the Unified Medical Language System (UMLS). The UMLS is a long-term research and development effort being conducted through and coordinated by the National Library of Medicine (NLM), designed to facilitate the retrieval and integration of vocabularies and information from multiple machine-readable biomedical sources. The UMLS retrieves information from numerous sources, including bibliographic material, clinical records, databanks, data repositories, knowledge-based systems, and directories. The major barrier to effective retrieval has been the use of multiple vocabularies and classifications used by different health professionals in the US.

The UMLS electronically links vocabularies and classification systems through a system of four knowledge sources (www.nlm.nih.gov/pubs/factsheets/umls.html): Metathesaurus, Semantic Network, Specialist Lexicon, and Information Sources Map. The Metathesaurus is organized by concept or meaning, and contains semantic information on approximately 476,322 biomedical and related concepts with 1,051,903 different names. The Metathesaurus contains vocabulary terms, classifications, coding systems, and thesauri developed and maintained by various professional organizations, such as the American Nurses Association, and identifies alternate names for the same concept and relationships between different concepts.

The Specialist Lexicon contains syntactic information about health care-related terms and concept names from the Metathesaurus, as well as other non-health related English words used in communication that are not necessarily included in the scope of the Metathesaurus. The Semantic Network is comprised of a network of general categories or classifications which consistently categorize all concepts from the Metathesaurus, and identifies allowable relationships between terms. The Information Sources Map contains information on the available sources of the machine-readable health related information. Each term or concept is defined and cross-mapped to terms or concepts within other classification systems or vocabularies.

All vocabularies and classifications for nursing that have been approved by the American Nurses Association are incorporated into the UMLS. These nursing vocabularies and classifications in UMLS can be extrapolated, resulting in what could be described as a Unified Nursing Language System (UNLS) (McCormick, et al, 1994; McCormick & Zielstorff, 1995). This UNLS, when extrapolated, could be tested against large scale nursing data repositories to determine if it is also representative of vocabularies such as acute care, primary care, long-term care, outpatient, community, school health nursing, occupational health, and the many realms where nursing care is delivered.

Computer Based Patient Records Require a Structured Vocabulary

One glaring issue related to vocabulary needs is that there are no computer systems currently available in the world that have the ability to integrate vocabulary, classifications, and language from the point of service to network to universal levels. Integrated medical centers and managed care industries are beginning to demonstrate that health care vocabularies can be merged through transcriptionists, scanners, and object-oriented open systems using Internet technology, although given the potential of computer systems, this manner of merging vocabulary is time-consuming and fragmented.

The use of information technology requires uniform, accurate, and automated patient care data to conduct analyses to improve the quality of care. While there are certainly confidentiality and other ethical concerns that are beyond the scope of this paper, these analyses nevertheless would facilitate the assessment of effectiveness and cost-effectiveness of care. The former Center for Information Technology (CIT) within the Agency for Health Care Policy and Research (AHCPR), has funded cooperative agreements with the NLM to examine applications of the Electronic Medical Record, and research on Computerized Decision Support Systems for Health Providers, both of which have stressed the need for developing, refining, and implementing the use of structured vocabulary.

AHCPR has participated in developing vocabulary standards and tools for improving research and policy utilization of content stored in the computer-based patient record. Between 1994 and 1997, the AHCPR and the NLM funded the only horizontal and vertical systems study to strengthen electronic medical record systems, by developing, updating, and maintaining terminology models. This collaborative study took place within the Mayo Foundation (led by Dr. Christopher Chute) and Kaiser Permanente (led by Dr. Simon Cohn). The study measured the relative merits of terminology additions and changes as they affect clinical practice guideline development and patient data retrieval (Chute, et al, 1996). This study also evaluated the impact of terminology variations on physician practice and satisfaction.

The development of an electronic toolkit for transmitting and linking laboratory data was also supported by AHCPR research funds. Under the direction of Dr. Clem McDonald, principal investigator, this study developed naming conventions and assigned a fully specified unique name and code for laboratory results reporting, and many clinical measurements (AHCPR, 1996). The system developed, Logical Observations Identifiers, Names and Codes system (LOINC), is available online to the public at: http://www.mcis.duke.edu/standards/termcode/loinclab/loinc.html [As of 10/15/03, this site is no longer available, but the LOINC codes can be retrieved from www.loinc.org/download].

Finally, AHCPR collaborated with the NLM in a large-scale vocabulary test of the use of controlled vocabularies in health care applications (Humphreys, 1996). This study analyzed the combination of vocabularies currently in the UMLS to determine the extent to which existing vocabularies serve as an accurate source of vocabulary for health data systems and their clinical applications.

New Technologies to Map, Merge, and Integrate Vocabularies and Different Classifications

The advancement of computer technologies has opened up a world of opportunities for nursing and health care. While computer or electronic servers cannot, by virtue of their construction and purpose, be used to aggregate vocabulary to higher and higher levels of abstraction, or from the point of care (interface), to network (reference), to universal (administrative) levels, they can be used, however, to store content created and retrieved by any number of access methods. These access methods involve the use of additional technologies known as chunkers, matchers, mappers, and routers, and are similar to UMLS mechanisms described earlier. For example, information entered manually or into the electronic patient record is sent (virtually) to a router, where information is tagged or categorized, and text and/or objects are channeled to either a chunker or mapper.

For example, information entered into the record for a patient who presents with vague, nonspecific symptoms would likely be routed to a chunker. The chunker electronically extracts units of information, such as terms and concepts, from computerized text. The chunker then sends information to a matcher, which collapses words and nominal phrases into lexical and semantic classes. On the other hand, more specific patient information is sent from the router to a mapper, which further specifies information by referencing routed terms with any and all possibly related terms; these related terms alert the clinician to information that should be considered in clinical decision-making. Finally, information from the matcher or mapper is channeled to the server, which houses information within a database for analysis and evaluation purposes (Tuttle, 1998).

Together servers, chunkers, matchers, mappers and routers can be used to

1. link nursing literature with a data repository of nursing information extracted, for example, from an academic medical center, a nursing home, and a community care clinic;
2. integrate into NIC, NOC, NANDA, Omaha, and/or the Home Health Classification system; and
3. include all CINAHL terms, and the Metathesaurus of UMLS to form a nursing knowledge server in the U.S.

This nursing knowledge server, in turn could be used in a network of nursing practice, for example, within a managed care enterprise network.

The Internet provides additional technological capabilities. Through the Internet, vocabularies and classifications from different health care organizations, institutions, or systems are being merged in different ways to create data repositories that provide the basis for measuring cost, quality, patient access to care, and outcomes of care. These new repositories incorporate servers containing various data elements which allow the convergence of data from a variety of sources. Techniques such as data mining and Knowledge Data Discovery (KDD) might be used to determine, with knowledge robots, intelligent clients, or administrative agents, where data and vocabulary similarities and discrepancies exist (Fayyad, U, et al, 1996). The use of KDD versus natural language processors or text readers is yet undetermined.

Extensible Markup Language (XML) is being used as an integrator of terms at the point of data convergence. XML is an extremely simple dialect of the Standard Generalized Markup Language (SGML), as is Hypertext Markup Language (HTML), all of which are languages of the Internet (www.w3.org/XML/). XML is intended for large-scale Web applications, vendor-neutral data exchange, and processing of Web documents by intelligent clients. The XML documents are made up of storage units called entities, which can contain either parsed or unparsed data. Parsed data is made up of characters, some of which form the character data in the document, and some of which form markup. Markup encodes a description of the document's storage layout and logical structure.

The use of these technologies is leading to quicker convergence of knowledge sources and the identification of terms used in one vocabulary, yet missing from another. The potential of these technologies is to create a feedback loop so that data extracted from computer-based records can be used to identify new terms when, for example, a nurse uses a term that has not been previously used but should be added to the vocabulary or classification scheme.

Object-oriented technology is also advancing the way that vocabularies and classifications are converging from different sources. At the Internet level, XML and HTML are being fit into a Document Object Model (DOM) (www.w3.org/DOM/). The DOM is a platform- and language-neutral interface what allows programs and scripts to dynamically access and update content, structure and style of all documents. The DOM provides a set of objects for representing HTML and XML documents, a standard model of how these objects can be combined, and a standard interface for accessing and manipulating them. Vendors can support the DOM as an interface with their proprietary data structures, thus increasing interoperability on the Web.

Collectively, XML and the Object Models render content on the Web a meta-data syntax that fits easily within the framework of the World Wide Web. XML has provided a mechanism for defining and documenting object classes. XML can be used for describing terms that are strictly syntactic, or those which indicate concepts and relations among concepts with relational databases. Therefore, all vocabularies and classifications used at the point of care can be converged within networks, and converged yet again at the universal level. The universal level of taxonomy can be a convergence of many into a unicode or single taxonomy or remain several taxonomies that are linked but not assembled at the global level.

For nursing, the simple solution is to have a single unicode taxonomy at the universal level. While it can be argued that the taxonomy may differ across countries, if convergence at a universal or global level of nursing is desired, different languages must be converged and translated into a single taxonomy. Several nursing taxonomies have already been translated into multiple languages. Through the years, they have served as the unifying concept of the nursing profession, and the use of these taxonomies across numerous countries testifies to their ability to capture nursing information for application in several countries. The International Council of Nursing may represent the logical avenue through which to begin examining these taxonomies and to develop an open universal taxonomy for nursing.

Summary

This paper has identified three levels of vocabulary needs for nursing that are required and the technological advances that make integration possible. At the point of care, there will predictably be many nursing vocabularies and classifications used. At the network level of care, there will be several nursing classifications used. Because of technology breakthroughs such as the Web, XML, object-oriented technology, and relational databases, multiple classifications can continue to be used at the site of patient care and within varying health care systems. However, at the universal level, a unicode of a single taxonomy of nursing, with a single classification scheme will make data entry more simple, meaningful, and useful, data retrieval easier, international comparisons possible, and lower costs for data retrieval. In the end, it is anticipated that these efforts will facilitate the measurement and delivery of continuously improving care. Existing nursing structures that are already universally accepted may be the taxonomy to consider adopting at the universal level.

Authors

Kathleen A. McCormick, PhD, RN, FAAN, FRCNA, FACMI

Dr. Kathleen A. McCormick is currently senior principal, Health Division Unit, SRA, International, Inc. Prior to that she was senior science advisor, Agency for Health Care Policy and Research. In that capacity she developed a new program in computerized decision support systems. Prior to her retirement, she held the rank of Captain (Navy rank) in the United States Public Health Service. She was the Surgeon-General alternate to the Board of Regents of the National Library of Medicine; and the Secretary's Alzheimers Task Force. She was a member of the former National Information Infrastructure Task Force, Health Information and Applications Work Group. She is on the ANSI-Healthcare Informatics Standards Board (HISB). She was currently project officer for the establishment of codes and a toolkit of information for laboratory and diagnostic imaging standards on national networks conducted by the Regenstrief Institute. Prior to this assignment, Dr. McCormick was Director, Office of the Forum for Quality and Effectiveness in Health Care responsible for developing the methodologies and process of Clinical Practice Guidelines. In that position, she released the first six guidelines, developed the policies and methodology for guideline development, and instituted the first contract guidelines for AHCPR. In addition, she co-authored one of the first guidelines on Urinary Incontinence in the Adult, and directed the development of an additional 12 completed guidelines that have been released. Prior to the AHCPR assignment, Dr. McCormick was an intramural co-director of a National Institute on Aging incontinence clinical research project and developed the database management system for the collection of data in the nursing home. She was also the Principal Investigator of many projects in a Pulmonary Laboratory of the National Institute on Aging. In addition to many publications on incontinence and pulmonary physiology, Dr. McCormick is also author of articles and books on the use of computers in health care. She is co-author of Essentials of Computers for Nurses, 2nd Ed. The first edition of this book was a book of the year award winner. Dr. McCormick graduated from Barry University in Nursing, Boston University in Clinical Specialty Nursing, and the University of Wisconsin in Physiology. Dr. McCormick is an elected member in the Institute of Medicine, National Academy of Sciences, and the Academy of Medicine for Washington, D.C. These honors are in addition to her being a Fellow in 4 specialty associations: she is a Fellow in the American College of Medical Informatics, The Gerontological Society of America, The American Academy of Nursing, and the Royal College of Nursing in Australia.
* Dr. McCormick was employed at the Agency for Health Care Policy and Research when this paper was written. The paper does not reflect the position of AHCPR or DHSS/PHS.

Cheryl B. Jones, PhD, RN

Cheryl B. Jones, R.N., Ph.D., on research leave from her assistant professor position at the University of Virginia School of Nursing, is currently serving a two-year expert appointment on the health care workforce at the Agency for Health Care Policy and Research in the Washington DC area. While at AHCPR, Dr. Jones will focus on issues related to the cost-effectiveness of health care workforce initiatives, and the impacts of changes in the workforce on the quality and costs of health care delivery, including patient, provider, and organizational outcomes. Her previous work in economics and health care has focused on such issues as the cost-effectiveness of nurse retention strategies, econometric modeling of nursing labor market behaviors, and nursing employment trends within the changing health care environment.

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