CHAPTER 2

KNOWLEDGE COMPLEXITY AND CLINICAL REASONING: STANDARDIZED TERMINOLOGIES

In this chapter, the topics of knowledge complexity, nursing informatics, and standardized terminologies are discussed. Given the developments in nursing informatics over the years, nursing knowledge representation and knowledge complexity influence the way providers think and reason, as well as how they record and capture data with technology. Standardized terminologies provide clinicians with a clinical reasoning vocabulary. The value and importance of technology and developments in the field of nursing informatics are discussed. Levels of nursing practice data relevant to the clinical reasoning processes are described. Different levels of nursing practice data are used in making decisions, allocating resources, and contributing to development of professional nursing practice. With the advent of the electronic health record (EHR) and the accumulation of large databases, the opportunity to use evidence and data in a new and novel way is likely to influence thinking and reasoning into the future (Pesut, 2006).

The Health Care Information and Management Systems Society (HIMSS) and the American Medical Informatics Society provide leadership and background resources for understanding the role of technology and health information developments to support clinical care and reasoning. The Nursing Work Group of the American Medical Informatics Association (AMIA) has a long history of setting international and national standards that support the use of nursing knowledge in the world of health care. Health analytics (Burke, 2013) is likely to transform health care and the way that interprofessional teams reason about individual, family, community, and population health. Topol (2012) notes that the superconvergence of human data capture is likely to lead to the creative destruction of medicine as humans become digitalized. Ritt (2014) observes that advancements in analytics will improve patient outcomes, promote health and safety, and foster data-driven decision making, which will positively impact practice. Knowledge work is an essential element in advancing nursing science and practice. Advanced practice nurses need awareness and insight into the role that standardized terminologies play in building future nursing knowledge. Health analytics will alter the future of health care and the life sciences (McNeill, 2014).

LEARNING OUTCOMES

After completing this chapter, the reader should be able to:

KNOWLEDGE WORK: FILTERS, FRAMES, AND FOCUS

Clinical reasoning presupposes knowledge work. Understanding how knowledge is represented and how knowledge language systems evolve and develop supports insight into the world of knowledge management. Such insight and understanding are essential for a full appreciation of types of knowledge that support performance, action, and learning. Knowledge is complex.

Knowledge is represented in resources like the International Classification of Diseases (ICD; World Health Organization, 2015) and the Unified Medical Language System (UMLS) of the U.S. National Library of Medicine (2015). These nationally recognized resources are important because many EHRs are organized to capture the data in these knowledge-representation systems to support the identification and tracking of essential health information. Nursing terms are embedded in these systems and provide nurses with ways to capture essential nursing data and knowledge. Capturing this knowledge supports ongoing data mining and pattern recognition in regard to the epidemiology of nursing diagnoses, interventions, and outcomes and supports the evolution and development of health analytics (McNeill, 2014). A useful classification system has a specific purpose, well-defined criteria for inclusion of items in categories, criteria that consistently and reliably assign items to categories, and makes sense to informed users. When classification systems meet these criteria, they provide the clinical vocabulary for clinical reasoning and assist the nurse to name patient problems, communicate with peers concerning the patient, and communicate with other disciplines concerning the nature of nursing’s contribution to care (American Nurses Association [ANA], 2012).

The knowledge classification or standardized terminology that the advanced practice clinician uses in practice often serves as a filter for recording and ultimately thinking and reasoning about the management of problems, outcomes, and interventions. Filters help support framing and give specific meanings to a set of facts. Framing and “meaning making” then enable the advanced practice clinician to focus on a specific problem, which, in turn, can be transformed into a desired outcome and influenced by the choice of an intervention in service of a clinical judgment about the degree of outcome achievement.

Often the EHR is the vehicle through which nursing data and information are captured and stored. Sometimes medical framing of problems, outcomes, and interventions is necessary and these medical problems and issues have nursing care consequences. Medical framing and nursing framing of problems are complementary in nature. Framing from a nursing perspective is valuable in the care coordination process. What is important in terms of filtering, framing, and focus is that clinicians be conscious and intentional about the way they frame the facts associated with a client or a patient’s story. Nursing informatics scholars have spent many years representing and coding nursing languages to capture the nursing framing of client conditions. Such nursing knowledge work has helped nursing define its unique contribution to health care and also has built a foundation on which nursing research and data science can evolve. The distinctions, system rules, relationships, and perspectives are important to be able to fully appreciate the contributions of disciplines to care coordination efforts and to support a systems-thinking mind-set.

NURSING INFORMATICS: EVOLVING NURSING KNOWLEDGE WORK

In 2016, the American Medical Informatics Association (AMIA), the nursing special interest group, defines nursing informatics as the “science and practice [that] integrates nursing, its information and knowledge, with management of information and communication technologies to promote the health of people, families, and communities worldwide” (American Medical Informatics Association, 2016). The application of nursing informatics knowledge is empowering for all health care practitioners in achieving patient-centered care. Nurse informaticians work to advance health care as developers of communication and information technologies, educators, researchers, chief nursing officers, chief information officers, software engineers, implementation consultants, policy developers, and business owners. Core areas of work include:

The work of nurse informaticians and much of nursing informatics efforts have become a part of the U.S. National Library of Medicine Unified Medical Language System (UMLS), which categorizes many health and biomedical vocabularies to enable interoperability among computer systems. To learn more about UMLS visit the National Library of Medicine or review the quick start quide to UMLS (https://www.nlm.nih.gov/research/umls/quickstart.html; U.S. National Library of Medicine, 2015) about the mapping and tracking of nursing terminologies in the meta-thesaurus of unified medical language. Do you know what the seven standard nursing terminologies are that are included in the UMLS? If not, explore the explanation and description about the representation of nursing terminologies in the UMLS (www.ncbi.nlm.nih.gov/pmc/articles/PMC3243214).

In addition, there are other models for care coordination. Haas, Swan, and Haynes (2014) have identified essential competencies and the basics of a care coordination curriculum stemming from an ambulatory care model. The Care Coordination and Transition Model (CCTM) curriculum educates professional nurses in the areas of advocacy, education and the engagement of patients and families, coaching and counseling of patients and families, patient-centered care planning, support for self-management, nursing process as a proxy for monitoring and evaluation, teamwork and collaboration, cross-setting communications and care transitions, population health management, care coordination and transition management between acute care and ambulatory care, informatics nursing practice, and telehealth nursing practice (Haas et al., 2014). These competencies all require attention to knowledge archetypes and knowledge work.

THE KNOWLEDGE-COMPLEXITY ARCHETYPE

In the world of informatics there is the notion that data lead to information, information leads to knowledge, and knowledge evolves into wisdom. Allee (1997, 2003) suggests that there is more to the process and has proposed an archetype that makes explicit the complexity of knowledge. Table 2.1 illustrates the levels and categories of what Allee defines as the Knowledge Complexity Archetype (1997, 2003). Reflect on the dimensions of the Knowledge Complexity Archetype table and consider how each aspect or facet of knowledge and learning informs action and performance and clinical reasoning activities. Also give attention to the span of time and perspective that is required to appreciate and value how the knowledge elements fit together. How do you think this Knowledge Complexity Archetype relates to the development of clinical reasoning for care coordination?

In Allee’s model, knowledge gained through instinctual learning supports sensing and feedback for here-and-now moments. Gathered data lead to the development of information, which can be used to support learning and help define the most efficient way to accomplish a goal or a task. As people gain experience and reflect on the information they have acquired, knowledge grows and develops. Through self-conscious reflection the advanced practice clinician can discern how best to use knowledge in the most effective ways. The meaning that the advanced practice clinician attributes to knowledge gained supports understanding and productivity, and the effective use of resources. This type of meaning making requires sensitivity to time and communal learning. Communal learning coupled with a sense of past history and present circumstance lays the foundation for self-organization and the development of a philosophy of how things fit together in a system. Throughout time, the knowledge, learning, and action of communal learning leads to the development of wisdom about the importance of the ecology of communities and the world. Wisdom gained supports insight into the connections and dynamic relationships between and among people and things in the greater whole. In the end, the advanced practice clinician realizes that there is a unity of insight that is necessary if sustainability is to be achieved through actions, learning, and performance.

TABLE 2.1 Knowledge-Complexity Archetype

Source: Allee (2003).

A wise and effective health care provider is a knowledge worker who is conscious of his or her filtering, framing, and focusing in terms of knowledge representations. Such a person understands and appreciates the work that has gone into the development of nursing knowledge classification systems, standardized terminologies, as well as the creation of the UMLS and how they are related to each other. And although nursing terms may be embedded in these systems, future health analytic work (Burke, 2013; McNeill, 2014) will depend on the ways and means that the nursing terminology can be cross referenced and mapped to contribute to the creation of comparative, descriptive, prescriptive, and predictive analytics (Ritt, 2014). There are different levels of data that can be aggregated and used to develop nursing analytics. Understanding the role that data play at different levels of perspective in regard to care coordination clinical reasoning is an important value-clarification exercise. Organizing nursing knowledge for purposes of practice, education, and research is a professional responsibility that supports the optimization, integrity, and sustainability of the Knowledge-Complexity Archetype model.

LEVELS OF NURSING PRACTICE DATA

The Center for Clinical Effectiveness at the University of Iowa developed the model shown in Figure 2.1 to illustrate three levels of nursing practice data: the individual level, the unit/organization level, and the network/state/country level (Center for Nursing Classification and Clinical Effectiveness, 2016). It is essential that advanced nurses understand and value the importance and the interdependence of each level of practice data from a systems-thinking perspective. Students and clinicians are likely to resolve the individual level of practice data for clinical reasoning. Knowledge in these systems contributes to the filters, frames, and focus that nurses employ to reason about patient-care needs and clinical decision making. Represented knowledge is also then used for documentation of care delivered. Managers and administrators are most likely to value the unit-level data. Administrators and researchers are most interested in the network-, state-, and country-level data. All nurses need to be informed about these levels of practice data because these data, taken together, help describe and define nursing’s contribution to the health care enterprise. Such contributions are the foundation for future health-analytic work (McNeill, 2014). Clinical testing and evaluation of classification systems for practice is an ongoing professional responsibility and contributes to the analytics needed to monitor care, quality, and the effective use of resources to advance nursing science and knowledge work.

FIGURE 2.1 Nursing practice data: Three levels.

Reprinted with permission from Center for Nursing Classification and Clinical Effectiveness (2016).

Individual-Level Data

The level of immediate interest to most practicing nurses is the individual level. At this level, practice data are organized so that data are relevant and useful in explaining patient problems, nursing interventions, outcomes, and clinical choices and decisions that nurses can make. Information about the patient and the context is explained through the use of clinical knowledge that has been standardized in the form of classification systems or standardized terminology (ANA, 2012). If this information is collected and used according to a standardized system, it can be aggregated and used in a broader context at the unit or organizational level. Developments at this level are expanding, as groups and organizations continue to focus on development of nursing diagnoses, interventions, and outcomes.

Unit/Organization-Level Data

At the unit or organizational level, data about individual patients are combined into one system. This system can be linked to other information systems such as the medical care information system. At this level, analyses about common kinds of treatment can be performed according to four possible parameters: resources, costs, effectiveness, and education. Using data for resource allocation results in measures of productivity. Data related to costs provide information about charging and contracting. Using data used to support effectiveness research has consequences for practice innovations. Data about staff performance can be used for evaluation and planning. Each institution defines and specifies the type of information most useful for documenting patterns and trends for nursing service in the organization. If you have aspirations to become a nurse manager, unit/organizational-level data will be important.

Network/State/Country-Level Data

The network/state/country level represents the broadest scope of data about nursing activities. At this level, the Nursing Minimum Data Set (NMDS) provides an important contribution to the data-management needs of many systems. What do you think are essential pieces of nursing information? A group of nurse researchers believes the NMDS is a good place to start. The NMDS is a set of variables with uniform definitions and categories concerning the specific dimensions of nursing, which meets the information needs of multiple data users in the macro health care system (American Medical Informatics Association, 2016; Werley, Ryan, & Zorn, 1995). The purpose of the NMDS is to standardize information associated with nursing care that patients received in a variety of service settings. There are three elements in the data set: nursing care data, patient data, and service data. Nursing care data elements consist of (a) nursing diagnosis, (b) nursing intervention, (c) nursing outcome, and (d) intensity of nursing care. Patient data elements consist of (a) personal identification and (b) demographic characteristic such as date of birth, sex, ethnicity, and residence. Service data elements include (a) unique facility or service agency number, (b) unique health record of patient, (c) unique number of principal registered nurse providers, (d) episode admission or encounter date, (e) discharge date, (f) disposition of patient, and (g) expected payer.

Benefits of this kind of data set include uniform collection of data that can be compared across a variety of parameters, identification of trends related to patient problems and nursing care provided, and reliable data for quality-assurance evaluation and costing of nursing service. In addition, such a database promotes comparative research on nursing care, including research on nursing diagnoses, interventions, outcomes, and other clinical nursing research-based questions. Many efforts are being channeled to develop the NMDS. Many organizations and projects are devoted to developing the elements for the minimum data set. Consequences of this development include creation of a data bank to research projects about nursing care.

Most clinicians are most involved with the individual level of practice-relevant data. It is at this level that they make choices and decisions about the kind of patient problems or diagnoses identified, outcomes established, and the interventions chosen. Working in a team requires that the members of the team respect and value the filtering, framing, and perspective taking of the different disciplines. Nurses may collect data and cluster signs and symptoms inductively to build diagnoses using a nursing filter. However, the system in which the advanced practice clinician works may expect a different kind of filter or framing and require clinicians to use ICD-10 codes. Care coordination clinical reasoning challenges the advanced practice clinician to manage the competing values associated with both filters and frames to arrive at a focus. How does the advanced practice clinician negotiate and reconcile ICD-10 codes to gain insight into the nursing care issues and consequences associated with those medical diagnoses? As an advanced practice clinician, do you always filter and frame from a nursing perspective? Or perhaps you were taught to filter and frame information from a medical condition perspective? Filtering and framing are important aspects of the clinical reasoning process. Appreciating and valuing the long-term use and consequences of standardized data collection informs and influences one’ motivation and understanding of how data can be used to generate knowledge that leads to insights and health analytic discoveries at different levels of perspective: patients, systems, populations.

Table 2.2 represents an attempt to illustrate nursing domains of practice from the filtering and framing perspective. In this conceptualization, one sees the four domains of nursing practice and interest: functional, physiological, psychosocial, and environmental. Under these major domains are classes of diagnoses, outcomes, and interventions. Many of the major nursing diagnoses, interventions, and outcomes are mapped in the UMLS and meta-thesaurus of the National Library of Medicine and thus are embedded in a number of EHRs in order to capture nursing-sensitive data for analysis, evaluation, and use in health analytic computations and projections.

Expanding the standardized language issue further, Table 2.3 represents a crosswalk from nursing domains to ICD-10 codes and medical diagnoses used by advanced practice nurses for reimbursement categorization. Harmonizing nursing domains with reimbursement codes captures the professional language of nursing, medicine, and other health professionals from the UMLS and meta-thesaurus of the National Library of Medicine. When the languages are united in the EHRs, the different interprofessional disciplines are united in focus and are in the best position to communicate and move toward the same patient outcomes. The harmonization of language can only bridge the gaps that currently exist between providers and offer the best data for health analytic computations and projections. Care coordination clinical reasoning presents a challenge because of the need to integrate and combine all of the health care team providers’ disciplinary filters and frames to create and specify the focus of care and treatment. How the advanced practice clinician uses the knowledge stored in EHRs depends on the context of practice, knowledge, beliefs, values, and professional identity. As nursing continues to evolve, and data, information, and knowledge stored in EHRs become an important filter, frame, and focus, clinical scholarship will evolve to produce comparative, descriptive, prescriptive, and predictive health analytics (Burke, 2013, McNeill, 2014; Ritt, 2014).

TABLE 2.2 Harmonizing Nursing Language and Domains Taxonomy of Nursing Practice: A Common Unified Structure for Nursing Language

TABLE 2.3 Harmonizing Nursing Domains and Medical Diagnoses

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