Expiration Date: December 31, 2001. No CE credit will be given after this date.
Table of Contents
The deadline for completion of this module is Dec. 31, 2001. AbstractNote: This independent study module encompasses two articles from one issue of the Online Journal of Issues in Nursing. General PurposesThe purposes of this two-part independent study module are to:
Part One: "Characteristics of a Good Nursing Nomenclature From an Informatics Perspective"by Rita D. Zielstorff, MS, RN, FAAN The purpose for which a nomenclature is designed dictates its characteristics. Very few clinical nomenclatures have been designed for use in automated record systems. For this reason, system designers have had to adapt existing nomenclatures and classification systems for use in the automated systems they develop. Researchers have delineated the characteristics of a "good" nomenclature for purposes of structured data capture, storage, analysis, and reporting. Some of these characteristics are:
Part Two: "Is One Taxonomy Needed for Health Care Vocabularies and Classifications?"by Kathleen A. McCormick, PhD, RN, FAAN, FRCNA, FACMI
The use of vocabulary in nursing is a relatively new phenomenon. It has only been in the past 20 years that we have developed and refined the nursing nomenclatures and classification systems. However, as we become more knowledgeable about the information system infrastructure of health care in this country, the linking of vocabularies from disparate sources becomes more critical. We are now part of an international network of health care, where the term we use in one country greatly influences the impressions of other countries of our health care system. This paper describes some of the challenges to the nursing profession, in allowing us to maintain our local vocabulary, as we integrate into network and universal vocabularies in the future. It discusses some of the new technologies that facilitate the linkages. It defines some of the endpoint of our vocabulary in identifying the outcomes and quality of care that is delivered by the nursing profession. A model is proposed that demonstrate the linking required to move classifications from the point of care, through networks, and into universal levels. Objectives
Part One: "Characteristics of a Good Nursing Nomenclature From an Informatics Perspective"by Rita D. Zielstorff, MS, RN, FAAN Article originally published Sept. 30, 1998 The reasons for developing a vocabulary or nomenclature usually dictate its characteristics (Ingernerf, 1995). For example, if a nomenclature is developed primarily for classifying nursing intensity, its terms will describe patient characteristics that impact resources needed for care. If a nomenclature is developed primarily for billing, then its terms will describe actions or procedures that can be billed to a third party. In nursing, as in most other health disciplines, there are no nomenclatures that have been developed primarily for use in automated clinical information systems. Therefore, designers of information systems that rely on capturing and using structured clinical information have had to make do with nomenclatures that were designed for other purposes. A great deal of work has been done in the past few years to examine existing nomenclatures for their suitability for automated clinical systems, and most have been found significantly lacking (Campbell, 1997; Henry, 1998). In this paper, we will examine how data are used in automated clinical systems, and review the resulting requirements of a "good" nomenclature from the perspective of a system designer. It is important to state at the outset that a great deal of excellent work has been done with respect to nursing nomenclatures in the past few decades. Among the earliest is the work done at the Omaha Visiting Nurses Association to classify the problems that nurses define in the home health setting, along with the expected outcomes, the interventions that nurses use, and the actual patient outcomes. This set of terms and the recommended methods of using them is known as the Omaha System (Martin & Scheet, 1992). Among the best known nomenclatures is the North American Nursing Diagnosis Association (NANDA) Approved List of diagnostic labels (North American Nursing Diagnosis Association, 1994). More recent work includes the Nursing Interventions Classification (NIC), developed at the University of Iowa (McCloskey & Bulechek, 1996), the Home Health Care Classification (HHCC) developed at Georgetown University (Saba, 1992), and the Nursing Outcomes Classification (NOC), also developed at the University of Iowa (Johnson & Maas, 1997). At the University of Virginia, Ozbolt and colleagues culled hundreds of terms from patient records to develop the Patient Care Data Set (PCDS) (Ozbolt, Fruchtnicht & Hayden, 1994; Ozbolt, 1996), which codifies patient problems and actions delivered by all caregivers during a patient's hospital stay. All of the aforementioned nomenclatures have been recognized by the American Nurses Association as nomenclatures that should be included in a Unified Nursing Language System (Lang, 1995). All have been or are in the process of being added to the Unified Medical Language System developed and supported by the National Library of Medicine (Lindberg, Humphreys & McCray, 1993). Today's health care environment demands that automated patient record systems deliver the following functions:
All of these functions depend on data. Each function places requirements on the nomenclature that is used to capture and store that data. As we will see, sometimes these requirements conflict with one another, which further confounds the effort to develop a single, comprehensive nomenclature for use in automated systems. Each function will be discussed in turn. 1. Provide the Legal Record of CareIn order to provide the legal record of care, the system must capture the clinician's expression of patient assessment, diagnosis, goals, the plan of care, the care actually delivered, the patient's responses to care, and the actual patient outcomes. A nomenclature that captures all of the enormous richness of this data set across the spectrum of patient care settings must have what is known as domain completeness. Existing nursing nomenclatures cover various aspects of the nursing process in varying depths in one setting or another, but none can claim domain completeness. Even if a nomenclature claimed to have terms that describe all of the aspects of care in all settings, it must still support the human tendency to local variation. So the nomenclature must support synonymy, the ability to express the same concept in different ways depending on local preference. At this time, none of our nomenclatures supports synonymy. In addition to representing the entire domain, the terms in the nomenclature must be able to describe care at the clinical level, not at an administrative or epidemiological level; therefore the nomenclature's terms must have sufficient granularity to describe, for example, not only that a wound exists, but what the precise characteristics of the wound are, including size, location, nature and amount of drainage, etc. Because our patients are complex beings, the description of their conditions is also complex, thus the need for the ability to qualify the description of their conditions with modifiers such "mild," "moderate," and "severe." Because nursing is not a hard science, it must be possible to represent the degree of certainty of a finding (such as "possible xxx" or "probable yyy") and it must also be possible to record a negative finding (such as "no evidence of…" or "patient denies…"). Some of our nomenclatures do have modifiers that can be attached to terms. For example, modifiers such as "potential," "actual," "family," and "individual" can be attached to problem terms in the Omaha System, and NANDA terms can be qualified with such descriptors as "acute," "chronic," "impaired," and so on. Because human beings operate on their own perceptions of the world, the same term will have different meanings to different people, thus the need for a definition of each term in the nomenclature to insure non-ambiguity. Most of our nomenclatures do contain definitions of their terms, which assists with both understanding the meaning of a particular term, and also helps to assure consistency in use of the term. In fact, definitions for terms is one of the requirements for recognition of a nomenclature by the American Nurses Association, as is demonstrated clinical utility (McCormick, Lang, Zielstorff, Milholland, et al, 1994). There are other types of attributes that contribute to the description of conditions, actions and patient states that, when combined with core concepts, result in complex phrases such as "Stage 2 pressure ulcer at the right lateral malleolus." A nomenclature that had such a phrase in it would have to have many variants including whether it was stage 1, 2, 3 or 4, the anatomic location, whether it was right or left, lateral or medial, etc. In our example, the entire phrase has been "pre-combined" to include all of the qualifiers. But experience with systems that use pre-combined phrases has shown that as new knowledge and new circumstances arise, the need for new phrases mushrooms; the vocabulary quickly becomes unwieldy, and lacks parsimony. From an informatics perspective, it would be better if the nomenclature were more "atomic," with all qualifiers supplied from separate "axes" such as laterality (right, left, medial, lateral, etc.), anatomic location, stage or degree, and so on. Such a nomenclature would then be multi-axial and combinatorial, providing not only maximum parsimony, but maximum flexibility and extensibility. A few of our nomenclatures are somewhat combinatorial. The Omaha System, for example, allows combination of problem labels with modifiers, and allows action terms to be combined with "targets" to describe planned actions, but it is not accurate to say at this point that any of them is multi-axial. When nomenclatures are combinatorial, it is helpful to supply rules for how the different axes can be combined, so that nonsensical phrases such as "left social isolation" do not occur. For example, the Omaha System states that its coded signs and symptoms should not be used when the prefix "Potential" is attached to a problem term. By definition, a problem that is "potential" does not have signs or symptoms. Rules such as this make up the syntax and grammar of a nomenclature. While a nomenclature that is multi-axial and combinatorial and highly granular is desirable for many reasons, it can also be difficult to use by the clinician. Imagine having to make four clicks to select from four different lists of terms the words that make up the phrase as "Stage 2 pressure ulcer at the right lateral malleolus." One thing that clinicians abhor is an automated system that takes more time to use than the manual system they are used to. The technical challenge in developing a system that is both acceptable to clinicians and also captures data at a granular level in a form that can be manipulated by the computer for several different purposes is enormous. In fact, what we mostly see is compromise: we may ask the clinician to select a core concept from a list of terms (like "Stage 2 Pressure Ulcer") and allow the rest of the detail to be described in narrative text. Of course, it is then not possible to advise the nurse to consider infection when the drainage is described as odorous and purulent if that information is recorded in narrative text rather than in coded terms. To summarize, a nomenclature that is useful for recording clinical care must have domain completeness, it must support synonymy, it must have sufficient granularity, it must be parsimonious, its terms must be able to be qualified with modifiers (including certainty and negation), and its terms must be non-ambiguous. At the same time, it must be easy to use in the clinical setting. 2. Support Clinical Decision MakingThe ability of an automated system to support clinical decision making depends largely on how well the data available to it are structured. The nomenclature used to record information is one aspect of that structure. Consider, for example, the desire to have the system advise the nurse when a particular patient is at high risk for falling, or to propose appropriate measures to prevent pressure ulcers, or to recommend the most cost-effective wound treatment given a description of the wound. None of this can be done without assessment data that are recorded using a nomenclature that is quite granular. Furthermore, the data must be coded in such a way that they are easily retrievable and able to be manipulated by the computer. This requires that each term in the nomenclature have a unique identifier that can be used for coding. Experience with the maintenance of large nomenclatures has shown that the unique identifiers must be context-free, that is, the code should not indicate that the term belongs in one section of the taxonomy or another. This is because knowledge evolves, and using context-dependent codes creates serious problems when a code has to be moved to a different section of the taxonomy, or when the same term can logically belong in more than one section of a taxonomy (that is, when it can have multiple parents). It's extremely difficult to design decision support systems when the data required for a decision can exist under multiple codes. Of course, the quality of the data used for decision support is paramount, so the attributes of clarity and non-redundancy in the nomenclature will be key, along with the need to have clear definitions of each term so that clinicians use the terms accurately and consistently. 3. Billing/Costing/AccountingIt has long been advocated that atomic-level data captured in the course of clinical care should be able to be used for multiple purposes, including billing, costing, and/or accounting (Dick & Steen, 1991; Zielstorff, Hudgings & Grobe, 1993). In order to accomplish this, it must be possible to map the terms used in the clinical nomenclature to other nomenclatures that are used for billing, such as Current Procedural Terminology (CPT) (American Medical Association, 1993), or HCFA Common Procedure Coding System (HCPCS). Medical diagnoses may also be required for billing purposes, so terms for recording diagnoses must be able to be mapped to such nomenclatures as International Classification of Diseases — Clinical Modification (ICD9-CM) (National Center for Health Statistics, 1980). 4. Accumulate a Structured Data Base for Administrative Queries, Quality Assurance and ResearchAs with decision support, an automated system that provides the capability to store and retrieve data from a structured data base is highly dependent on the nature of the nomenclature used to capture the data. The same characteristics apply: The terms must have unique identifiers to allow coding; data quality must be supported through the attributes of clarity and non-redundancy in terms, and definitions of terms should be available to support accurate, consistent use. Since the purposes of these databases are wide-ranging, domain completeness as well as granularity are key requirements. The research system may require atomic-level data for certain purposes, while the administrative system may require that atomic-level data be rolled up into broader categories. When the nomenclatures is designed to be hierarchical, it is much easier to roll up the more granular data into groupings that make sense at the broader level. The ability to map the clinical terms to other standard classifications may be required as well. 5. Exchange Data with Internal and External SystemsHealth care agencies seldom have the luxury of a single, monolithic automated system. It is far more common that an agency will have multiple computers using different software platforms to accommodate their information processing requirements for clinical systems, administrative systems, financial systems, research systems, etc. When clinical data are needed by other systems, it must be possible to supply that data without re-entering it. Standards for packaging data and transporting them to "foreign" systems are evolving; to the extent that a nomenclature is structured to conform to those standards, then exchanging data will be made easier (Board of Directors of the American Medical Informatics Association, 1994). A major effort is underway to develop a clinical nursing classification scheme expressly for use in automated systems. The International Council of Nurses sponsors development of the International Classification of Nursing Practice (ICNP) (International Council of Nurses, 1996; Neilson & Mortensen, 1996). Still in its early phases, the nomenclature is intended to provide a common language for describing all of nursing practice across all settings and geographic locations. It includes a framework for mapping to existing nomenclatures and classifications. The developers encourage feedback and suggestions for additions and changes funneled through the American Nurses Association (Warren & Coenen, 1998).
The characteristics of a "good" nursing nomenclature from an informatics perspective are summarized in Table 1. Most of them have been listed by others as required attributes in any classification scheme that will be implemented in a computer-based patient record (Campbell, Carpenter, Sneiderman, Cohn et al, 1997; Henry, Warren, Lang & Button, 1998). Much of that work has foundations in the work of the Canon group, a gathering of researchers whose aim was to synthesize existing efforts at medical concept representation (Evans & Cimino, 1994). The topic has taken on more urgency in the past few years because of frustration with the slow pace of implementation of automated systems to support clinical care (United States General Accounting Office, 1993), and because of federal initiatives such as the Health Insurance Portability and Accountability Act of 1996 (HIPAA) that will require standards for coding and transmitting claims and other medical record data. The work of the nomenclature developers who are cited here, as well as the work of informaticians who examine, compare and evaluate existing nomenclatures for applicability in automated systems, is absolutely fundamental to achieving automated clinical systems that support both efficiency and effectiveness of care. Part Two: "Is One Taxonomy Needed for Health Care Vocabularies and Classifications?"by Kathleen A. McCormick, PhD, RN, FAAN, FRCNA, FACMI
Article originally published Sept. 30, 1998 IntroductionMisinterpretations 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. BackgroundNursing 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?
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 http://www.newsavanna.com/wlb/CE/Arena/Arena07/shtml; retrieved 1998, document no longer available online). 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 [click here for larger version] 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 (http://www.oceania.com) and a Canadian information system called Purkinje (http://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.
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 [click here for larger version] 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.
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 StoneThe 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 (http://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 VocabularyOne 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. 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 ClassificationsThe 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
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 (http://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) (http://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. SummaryThis 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. ReferencesPart One: "Characteristics of a Good Nursing Nomenclature From an Informatics Perspective"by Rita D. Zielstorff, MS, RN, FAAN American Medical Association. 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RoAnne Dahlen-Hartfield, DNSc, RN
ReviewersRosalie BenchotMary Campbell, MS, RN, CS Betty Freund, MSN, RN C Shirley Hemminger, MSN, RN, CCRN Betty Miller, MSN, RNC Nancy Panthofer Carol Sedak, PhD, RN, ONC |
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