Introduction
In the ever-evolving landscape of healthcare, the mandate for cost-effective, safe, and high-quality patient care is paramount. This demand is deeply embedded within contemporary health reform policies, underscoring the critical need for healthcare professionals to communicate their practices effectively and efficiently. For the nursing profession, standardized nursing languages (SNLs) have emerged as a vital strategy to articulate professional practice, ensuring that the contributions of nurses are clearly understood by other nurses, healthcare providers, and the broader healthcare team.
The advent and widespread adoption of the electronic health record (EHR) have further amplified the necessity for nursing to communicate its practice within a structured electronic format. Integrating SNLs into patient records offers nurses a significant opportunity to delineate their focus, specifically through the identification of nursing diagnoses, interventions, and outcomes. This structured approach not only enhances communication but also facilitates data-driven decision-making in patient care. The ongoing development, rigorous testing, and continuous refinement of SNLs are crucial for nursing to establish an accurate and reliable method for utilizing data elements across diverse populations and healthcare settings. This capability is essential for communicating nursing practice, enabling nurse administrators and healthcare leaders to effectively allocate resources, precisely determine the costs associated with nursing care, and innovate new care models grounded in data-driven insights regarding nurse-patient ratios and patient acuity.
The sustained utilization of nursing languages, coupled with the acceleration of nursing research leveraging this data, holds the key to providing the evidence necessary to link nursing knowledge with evidence-driven, cost-effective, and quality outcomes. Such outcomes more accurately reflect the profound impact of nursing on patient care and the broader healthcare system. Evaluating research that supports the development, application, and continuous improvement of nursing language is therefore critical for advancing research and transforming patient care delivered by nurses on a global scale. This commitment to standardized language and evidence-based practice is essential for ensuring that nursing continues to play a pivotal role in enhancing patient care outcomes in an increasingly complex healthcare environment, and aligns with models of patient care such as those potentially associated with figures like McFarland, emphasizing comprehensive and structured approaches to nursing practice.
Standardized Nursing Languages: A Cornerstone for Nursing Practice
The development and implementation of standardized nursing languages (SNLs) are not merely beneficial but fundamentally essential for the nursing workforce. These discipline-focused languages, encompassing data sets, nomenclatures, classifications, taxonomies, and terminologies, serve as the nomenclature for clinical phenomena that are central to the nursing profession. These standardized terms are indispensable for effective communication and collaboration among nurses themselves, as well as with patients, families, and all stakeholders within the broader healthcare system. As highlighted by communication theorists and nursing scholars, precise and consistent language is the bedrock of clear understanding and effective teamwork in any professional domain.
The systematic process of naming clinical phenomena—such as nursing diagnoses, nursing interventions, and patient outcomes—is pivotal in propelling the nursing profession forward as both a discipline and a science. This advancement is achieved through rigorous research and the continuous evolution of nursing knowledge. A defining characteristic of any profession, by sociological and academic consensus, is its unique and well-defined body of knowledge. This specialized knowledge base forms the very foundation of practice for its members, distinguishing it from other fields and technical roles.
| Term | Definition |
|---|---|
| ANA Core Criteria (nursing language criteria and definitions) | Support practice, clinically useful and unambiguous; systematic method of development, documented testing, and continued refinement, maintained on a regular basis. These criteria need to be present in all data sets, terminologies, or classifications |
| Class | A group, division, category, or set used to categorize or classify information |
| Classification | A way to arrange items (e.g., defining characteristics) based on relationships and assignment of names (e.g., interventions and outcomes) to groups of items |
| Data Set | Grouping of identified elements of particular interest within a context |
| Domain | The most abstract term in a taxonomy (e.g., functional domain) |
| Nomenclature | Terms that can be combined to represent more complex concepts; informed by preestablished rules |
| Taxonomy | Organization of concepts based on similarities into a conceptual framework |
| Terminology | Words for a concept or the vocabulary used to communicate a concept |
Source: Adapted from Dochterman and Jones, 2003.
According to leading nursing scholars Smith and McCarthy (2010), a professional discipline’s unique body of knowledge not only defines its practice but also differentiates it from technical practice. This knowledge base is composed of the discipline’s philosophies, ethics, theories, research, and art. The ongoing progress of the nursing discipline hinges on its ability to consistently link the contribution of nursing knowledge to the delivery of high-quality, cost-effective nursing care. The central aim of SNLs is to accurately name the phenomena of nursing concern. This standardization ensures that nurses, whether working locally, nationally, or internationally, have ready access to consistent labels, definitions, and descriptions of clinical phenomena. Such consistency is crucial for effective communication with patients and across the interdisciplinary healthcare team.
Employing SNLs to consistently communicate the substance of nursing science yields several critical benefits. Firstly, it significantly enhances patient safety and supports other quality-based healthcare objectives. Secondly, it fulfills the essential requirements for active participation in electronic health record (EHR) systems, which increasingly rely on standardized terminologies for data integration and interoperability. Thirdly, SNLs foster greater autonomy and control within nursing practice, empowering nurses to define and manage their scope of practice more effectively. Lastly, they provide essential clinical data that nurse administrators can utilize to achieve a multitude of workforce goals, from optimizing staffing patterns to accurately costing nursing care.
Patient safety and quality-based goals are directly enhanced through the aggregation, analysis, and interpretation of clinical data. By using the standardized labels of nursing diagnoses, interventions, and outcomes, healthcare systems can establish comprehensive databases. These databases are invaluable for identifying prevalent nursing phenomena of concern and for determining the most effective interventions needed to assist patients in achieving specific outcomes. This data-driven approach allows for the isolation of interventions that are most effective in addressing particular patient problems and achieving desired outcomes. Consequently, evidence-based and cost-effective quality care can be meticulously planned and implemented, aligning with initiatives aimed at faster and more effective healthcare solutions.
The global expansion of electronic health records (EHRs) has further accentuated the need for SNLs in healthcare communication. The successful implementation of EHRs necessitates the use of standardized terms, which ideally should be universally accepted at national and international levels. This standardization is crucial for developing benchmarks and for facilitating quality comparisons across different localities and healthcare systems. Integrating SNLs into patient documentation not only supports nursing autonomy but also enhances control over clinical phenomena that are of primary concern to nurses. These concerns are then clearly articulated and communicated to the public, other healthcare disciplines, and across healthcare systems. Moreover, nurse administrators gain access to essential clinical data, which is vital for optimizing staffing patterns, addressing patient acuity issues, costing nursing care more accurately, achieving desired patient outcomes efficiently, and effectively describing professional nursing practice to all stakeholders.
The objectives of this discussion are to identify the specific SNLs relevant to clinical practice, to explore the existing body of research that supports their use, to describe the methodologies for ongoing development and refinement of these languages, to connect current and future research to evidence-based nursing practices, and to elucidate the critical importance of nursing languages for the nursing workforce.
SNLs for Clinical Practice: A Historical and Contemporary Overview
Standardized Nursing Languages (SNLs) for clinical practice have been under continuous development since 1973, marked by the inception of the organization now known globally as NANDA International (NANDA-I). This section will detail the evolution of existing SNLs, review the research that underpins their validity and utility, and explain how SNLs are intrinsically linked to evidence-based nursing, thereby enhancing the quality and consistency of patient care.
The Landscape of Existing SNLs
The initial impetus for developing SNLs arose from the First Task Force to Name and Classify Nursing Diagnosis, convened in St. Louis, MO, in 1973. At this seminal meeting, nursing pioneers Gebbie and Lavin tasked 100 invited nurse experts from the United States and Canada with the critical mission of developing and classifying health problems that fall within the domain of nursing. This initiative was driven by the pressing need to enhance the visibility of nursing in patient care, establish standardized names for computer files to record and organize nursing data, accurately assign costs to nursing care, and link nurses’ clinical judgments and decisions directly to patient care actions and outcomes.
Following the inaugural conference, the organization continued its work with a second conference in 1975, and a third in 1978, transitioning to biannual meetings thereafter. In 1982, the organization adopted the name North American Nursing Diagnosis Association (NANDA), reflecting its geographical focus at the time. However, by 2002, recognizing the expanding international engagement of nurses from numerous countries, the organization’s name was updated to NANDA International (NANDA-I), more accurately representing its global reach and impact. The most recent NANDA-I meeting, and the first held outside of the United States, took place in Madrid, Spain, in May 2010, underscoring its international presence.
Since 1973, a variety of data sets, terminologies, and classifications have emerged through different developmental pathways. NANDA-I operates as a membership-driven organization with a structured committee framework, including the Taxonomy and Diagnosis Development Committees, which are crucial for maintaining and evolving the NANDA-I taxonomy. Concurrently, the Nursing Interventions Classification (NIC) and the Nursing Outcomes Classification (NOC) were developed by NANDA-I members through research funding from the National Institutes of Health. These projects involved extensive research teams based at the University of Iowa, leading to comprehensive classifications of nursing interventions and patient outcomes. The University of Iowa continues to support the maintenance of NIC and NOC through the Center for Nursing Classification and Clinical Effectiveness (CNCCE).
The Omaha System and the Clinical Care Classification (CCC) originated from the efforts of community health nurses who identified a specific need for SNLs in home healthcare settings. Initially designed for community-based care, both classifications have broadened their application to include various settings such as academic nurse-managed centers and nursing education programs. These languages are continuously evolving; for example, the Omaha System hosts regular meetings and conferences to further develop and refine its model. Recently, the CCC became freely accessible within the comprehensive reference database, SNOMED CT, enhancing its availability and integration within broader healthcare information systems. The Perioperative Nursing Data Set (PNDS) was developed and is actively promoted by the Association of periOperative Registered Nurses (AORN) specifically for use in perioperative nursing environments. Initially developed and disseminated in the United States and Canada, these SNLs are now utilized internationally in countries including Japan, Spain, and France, reflecting their global applicability and recognition.
The International Classification of Nursing Practice (ICNP)
The International Classification of Nursing Practice (ICNP) stands as a unified language system designed to describe nursing practice within health information systems. It is an information tool that articulates the specifics of nursing care in a standardized format, facilitating better global communication and data sharing. Under development since 1990, the ICNP has undergone extensive testing and translation to ensure its applicability and relevance across diverse cultural and linguistic contexts.
The International Council of Nurses (ICN) initiated the development of ICNP in 1990, recognizing the need for a universally understood nursing language. Over the years, multiple versions of ICNP have been developed and rigorously tested in various settings worldwide. The data generated through ICNP can be effectively used by clinicians, researchers, and administrators to accurately describe nursing practice and quantify the contributions of nursing to patient care outcomes. The current version of ICNP is recognized as a valuable resource for measuring the quality of nursing care and is increasingly utilized in nursing research.
To support its ongoing refinement and global adoption, ICNP has established several centers internationally, including Deutschsprachiege ICNP for German-speaking users, the Research Center for Nursing Practice in Australia (a collaboration between Australian Capital Territory and the University of Canberra), and the Chilean Center for ICNP Research and Development. These centers contribute to the continuous improvement of ICNP through research, translation, and adaptation to local healthcare contexts. Numerous cross-cultural research studies have been conducted using ICNP, encompassing cross-mapping studies, validation studies, and computer database analyses, demonstrating its versatility and robustness in diverse settings.
American Nurses Association—Nursing Practice Information and Infrastructure
In 2006, the American Nurses Association’s (ANA) Committee on Nursing Practice Information and Infrastructure launched a dedicated website to provide nurses with up-to-date information on SNLs and documentation standards. Currently, the ANA recognizes 13 nursing data sets, nomenclatures, and classification systems as approved for use in EHRs. Among these, five terminology sets specifically include nursing diagnoses, interventions, and outcomes. These ANA-approved sets are: (a) Clinical Care Classification (CCC), (b) International Classification for Nursing Practice (ICNP), (c) the combined NANDA-I, Nursing Interventions Classification (NIC), and Nursing Outcomes Classification (NOC), often referred to as NNN, (d) Omaha System, and (e) Perioperative Nursing Data Set (PNDS).
| • North American Nursing Diagnosis Association–Taxonomy II—classifies nursingdiagnosis (NANDA-I) |
|---|
| • Nursing Intervention Classification (NIC)–Taxonomy, 4th ed.—classifies nursing interventions |
| • Nursing Outcomes Classification (NOC)–Taxonomy, 4th ed.—classifies patient outcomes |
| • Gordon’s Eleven Function Health Patterns |
| • Home Health Care Classification’s (HCCC) classifies specific nursing diagnosis,interventions and outcomes (Homecare) |
| • Omaha system’s structure classifies specific nursing diagnosis, interventions, andoutcomes |
| • Patient Care Data Set (PCDS; Ozbolt) |
| • Perioperative Data Set (PNDS) classifies specific nursing diagnosis, interventions andoutcomes (Perioperative care) |
| • International Classification of Nursing Practice (ICNP) classifies specific nursingdiagnosis, interventions and outcomes (ICN) |
| • SNOMED RT (Systemized Nomenclature of Medicine clinical terms) referenceterminology to cross map multiple Classifications, etc. |
| • Clinical LOINC—Logical Observation Identifiers Names and Codes |
| • Nursing Minimum data Sets—NMDS Delaney, C. 2006 |
| • NMMDS—Nursing Management Minimum Data Sets |
Each of these five classification systems addresses core concepts pertinent to professional nursing practice. These include, for instance, self-care, anxiety, fear, mobility, sleep, nutrition, constipation, skin breakdown, stress, coping mechanisms, and self-management of chronic illnesses. The latest versions of the NANDA-I, NIC, and NOC (NNN) taxonomies collectively comprise 1,147 research-based labels, definitions, and descriptions, representing a substantial body of standardized nursing terminology. Extensive research and numerous position papers supporting these concepts can be found in the proceedings of conferences hosted by organizations like NANDA-I since 1973. The development of SNOMED CT (Systematized Nomenclature of Medicine—Clinical Terms) is aimed at creating a comprehensive reference terminology that facilitates the use of multiple languages in a standardized format within EHRs. SNOMED CT allows for mapping all ANA-approved languages, including NNN and other terminologies, into a unified system, enhancing interoperability and data exchange across different healthcare information systems.
NANDA-I, NIC, and NOC: Integrated Nursing Languages
Over the years, numerous national conferences have focused on the integrated application of NANDA-I, NIC, and NOC (NNN). NNN represents the largest group of language developers in North America dedicated to expanding the classifications of nursing diagnoses, nursing interventions, and nursing outcomes, respectively. These meetings aim to further terminology development, particularly towards establishing a common structure across the three languages and advancing research methodologies in their application. A unified structure is deemed critical for increasing the adoption of SNLs, clearly articulating the content and focus of nursing practice, developing robust databases for advanced research, supporting the prediction of staffing patterns and workload, and isolating costs associated with patient acuity and complexity.
In 2001, leaders from NNN secured funding from the National Library of Medicine to undertake a project focused on developing, implementing, and evaluating the foundational assumptions of nursing diagnosis, intervention, and outcome languages. This project aimed to examine the existing taxonomic structures within NNN and to create a preliminary draft of a common structure that would integrate diagnoses, interventions, and outcomes. A Desiderata framework was developed to guide the creation of this new organizing structure, emphasizing principles such as simplicity, theory neutrality, parsimony of categories, clarity of language, distinct definitions for diagnoses, interventions, and outcomes, and utility for interdisciplinary communication.
The Proposed NNN Classification Structure
The proposed NNN Classification structure consists of four overarching domains and 28 classes, designed to meet the established guidelines for a desired unified structure. This structure facilitates the consistent placement of the three languages within the same domains and classes, promoting a more integrated and cohesive approach to nursing documentation and practice. Currently, NNN continues to refine this common structure, with the long-term goal of achieving a unified framework for organizing nursing diagnoses, interventions, and outcomes. Research has explored the impact of using NNN in nursing education. A study by Kautz, Kuiper, Pesut, and Williams (2006) investigated the use of NNN in a Bachelor of Science in Nursing (BSN) program and identified inconsistencies in terminology use among faculty and students. The study recommended the integration of NNN throughout the nursing curriculum to ensure consistency in communicating and documenting nursing practice, thereby better preparing nurses for the demands of 21st-century healthcare environments.
Impact of SNLs on Nursing Knowledge and Practice
From their inception, the development of SNLs has been driven by the goal of reflecting and building upon the established body of nursing knowledge. Nursing has cultivated a substantial knowledge base, which has grown exponentially over the past 45 years. This knowledge development, encompassing grand, mid-range, and practice-based theories, reflects the philosophical underpinnings of the discipline. At the core of nursing knowledge are relationships—specifically, the nurse-patient, nurse-family, and nurse-community relationships. SNLs play a crucial role in capturing these complex interactions by articulating the phenomena of concern that embody the knowledge and focus of the nursing discipline.
The evolution of existing SNLs reflects the social contract between nurses and society, as articulated in the Social Policy Statement by the ANA and Nurse Practice Acts across numerous states. These acts, such as the New York State Nurse Practice Act of 1972, recognize nurses’ roles in diagnosing and treating human responses to health conditions. Contemporary SNLs align with this societal expectation by addressing health promotion, risks to health, and the responses of individuals and groups to illness.
SNLs bridge the gap between disciplinary knowledge and the practical delivery of care, offering nurses standardized approaches to describe their practice. The labels for nursing diagnoses, interventions, and patient outcomes are rigorously defined and described to ensure clarity and consistency in meaning for all users. The organizational structures of SNLs are designed to create systems that are easily communicable and practically applicable. Frameworks such as Gordon’s Functional Health Patterns have been effectively used to organize nursing diagnoses, providing a structured approach to patient assessment and care planning.
Research and SNLs: Building an Evidence Base for Nursing Practice
The robust body of research supporting the use of Standardized Nursing Languages (SNLs) provides compelling evidence for nurse leaders to advocate for and implement SNLs in clinical practice. This section reviews the significant research that underpins the validity and effectiveness of SNLs, emphasizing their crucial role in advancing evidence-based nursing.
In a comprehensive bibliometric study of the CINAHL database, Anderson et al. (2009) mapped the existing knowledge base of SNLs. This study examined a wide range of literature sources, including books, book chapters, journal articles, dissertations, brief reports, and abstracts, published between 1982 and 2006, focusing on the five terminology sets approved by the ANA. The analysis identified 1,140 unique items, classified according to terminology set. The findings revealed that the NNN terminology set (NANDA-I, NIC, NOC) had the most extensive research literature support, accounting for 879 of the 1,140 sources. This substantial research base underscores the significant scholarly attention and empirical validation that NNN has received. The following sections will further explore the research support for standardized nursing diagnoses, nursing interventions, nursing-sensitive patient outcomes, and the integrated use of NNN.
Research Support for Standardized Nursing Diagnoses
Since the inception of NANDA-I in 1973, rigorous research has been a cornerstone for the acceptance of new diagnoses and modifications to existing ones. The NANDA-I Diagnosis Development Committee mandates that all diagnosis submissions be supported by robust research and literature evidence. A diagnosis is included in the approved list only when sufficient empirical and scholarly support validates its clinical relevance and accuracy.
In 1989, NANDA-I convened a conference specifically to explore the research methodologies used in nursing diagnosis and to propose methods for effectively integrating SNLs into EHR systems. Key topics discussed included methods for validation, qualitative research approaches, quantitative methods, and integrative research designs. Early studies in this field were predominantly descriptive, encompassing concept analyses, diagnostic content and construct validity assessments, frequency studies, and inter-rater reliability studies. Predictive validity and advanced statistical methodologies, such as regression analyses, were less frequently addressed. A recognized need emerged for the development of instruments that accurately reflect the content and concepts of nursing to facilitate more rigorous and sophisticated research.
The types of studies aimed at developing nursing diagnosis knowledge are diverse, with descriptive studies still being prevalent. However, a growing trend towards more experimental designs is evident, as seen in presentations at the 2010 AENTDE/NANDA-I conference in Madrid. A PubMed literature search covering the decade from 2000 to 2010 identified 162 published research studies that focused on or included nursing diagnoses from NANDA-I and other terminologies like ICNP or PNDS. From the outset of nursing diagnosis development, a strong emphasis has been placed on validating the existence of specific nursing diagnoses, their defining characteristics, and associated risk factors across various patient populations.
The content and construct validity of individual nursing diagnoses for use with specific populations have been rigorously established by nurses globally. The sheer volume of these studies is extensive, particularly when considering the numerous nursing organizations, beyond NANDA-I, that are dedicated to advancing nursing diagnosis knowledge. These organizations, such as the Japan Society of Nursing Diagnosis, the Association of Common European Nursing Diagnoses, Interventions, and Outcomes (ACENDIO), the Brazilian Nursing Diagnosis Association, and the Spanish Nursing Diagnosis Society (AENTDE), regularly host conferences where nursing diagnosis research is presented, often leading to publications in international literature. For example, the 2010 AENTDE-NANDA-I conference in Madrid featured 674 papers and posters, the majority of which were research-based.
Descriptive studies have been instrumental in establishing the content and construct validity of concepts that represent nurses’ diagnoses—that is, the responses or experiences of individuals to health problems and life processes. An illustrative example is a clinical study conducted in two hospitals involving 76 patients experiencing one or more of three respiratory diagnoses: Ineffective Breathing Pattern, Ineffective Airway Clearance, and Impaired Gas Exchange. The data collection instrument used in this study demonstrated good validity and reliability, including both interrater and intrarater reliability. The findings and conclusions from this study were presented to the NANDA Diagnosis Review Committee and contributed to the refinement of these three diagnoses.
In a comprehensive review of validation studies reported in PubMed and CINAHL databases, Berger (2008) noted that the majority of identified studies were quantitative, employing nurse validation and clinical validation methods. Berger concluded that further validation studies are needed for many diagnoses. The review indicated that at least one validation study was available for 72 diagnoses, while 59 diagnoses had between one and four studies. For 84 diagnoses, no studies were found in the reviewed databases, although research may exist that was not published or indexed in the databases reviewed. Clinical methodologies, considered particularly valuable for diagnosis validation, were used in 50 studies analyzed by Berger. The review highlighted the need for additional studies utilizing multivariate methods such as magnitude estimation scaling, Q sorting, factor analysis, and discriminant analysis to further strengthen the evidence base for nursing diagnoses.
Naming Interventions: Research and Classification
A robust classification system focused on nursing interventions is essential to ensure consistent terminology and definitions for the treatments nurses provide. The Nursing Interventions Classification (NIC) offers guidelines for nurses in selecting appropriate nursing interventions. This selection process is a critical component of clinical reasoning, guiding nurses in choosing diagnoses, interventions, and outcomes that are most effective for patient care. Six key factors are pivotal in the selection of an intervention.
Firstly, the choice of a nursing intervention for a specific nursing diagnosis is significantly influenced by the desired patient outcome. This necessitates effective communication between the nurse, patient, and family members, along with consideration of the timeframe for care delivery. Patient outcomes serve as the benchmarks to evaluate whether a nursing intervention is successfully improving the patient’s condition. Secondly, nurses select outcomes and interventions based on the characteristics of the nursing diagnosis itself. Ideally, the intervention should target the underlying etiological factors to eliminate or mitigate the problem. When this is not feasible, the intervention should aim to alleviate the patient’s symptoms. The third factor is the research base supporting the intervention. Evidence-based interventions are preferred as they provide insight into the intervention’s effectiveness in similar situations and with comparable patient populations. The fourth consideration is the feasibility of implementing the intervention, encompassing factors such as cost, time requirements, and integration with the patient’s overall care plan involving multiple providers. Fifthly, the acceptability of the intervention to the patient is crucial. This includes respecting the patient’s values, beliefs, religion, and cultural background. Lastly, the nurse’s capability is a determining factor. This involves the nurse’s understanding of the scientific rationale behind the intervention, possessing the necessary psychomotor and interpersonal skills, and the ability to function effectively within the healthcare setting where the intervention is to be performed.
Selecting the most appropriate interventions based on these factors is vital for enhancing the quality of patient care. Each NIC intervention includes a list of suggested activities that nurses can select to customize the intervention to meet the specific needs of each patient. Addressing safety issues is paramount in care planning and the selection of nursing interventions.
At the organizational level, NIC has been effectively used to measure nurse competency in performing specific interventions. Nolan (1998) describes how one healthcare organization utilized NIC to validate competency in nursing interventions frequently performed within their institution. Competency, as defined by Nolan (1998), is an individual’s actual performance in a particular situation, reflecting how effectively they integrate knowledge, skills, attitudes, and behavior to deliver care according to expected standards. Competency assessment is a complex evaluation of an employee’s ability to meet job expectations and consistently provide effective patient care. This competency assessment relies on the organization’s ability to identify the most frequently performed interventions (as well as diagnoses and outcomes) across the organization and within individual units. This data-driven approach ensures that competency education and testing are directly relevant to actual practice patterns. This knowledge is invaluable for planning orientation programs for new employees and for developing relevant continuing education programs for existing staff. Educational sessions can incorporate clinical reasoning case studies focused on intervention selection to enhance nurses’ decision-making skills in this critical area. Given the dynamic nature of healthcare, frequently used nursing interventions need ongoing validation as changes in practice and patient needs evolve with new medical treatments and approaches. Continuous data collection at both organizational and unit levels is essential to monitor nurse competency and its impact on quality care and safety outcomes across all healthcare settings. The NIC classification includes domains specifically focused on safety and health system considerations, assisting nurses in identifying interventions that ensure quality care and safety are integral components of the patient care plan.
SNLs and Measuring Patient Outcomes
The Nursing Outcomes Classification (NOC) is indispensable for evaluating the effectiveness of nursing interventions applied to address identified nursing diagnoses. NOC provides standardized terms and definitions for patient outcomes, enabling nurses to measure and communicate the results of their care more effectively. Historically, nurses have relied on individualized goal statements to evaluate care. While patient-specific, these goals are challenging to compare across different patient populations and healthcare settings. NOC outcomes are designed to facilitate such comparisons, allowing for the analysis of outcomes for diverse patient populations treated by nurses in various settings and across the continuum of care. The measurement scales associated with each outcome in NOC enable nurses to assess patient status both before and after interventions, at critical junctures such as sudden changes in condition, at shift changes, before transfers to other units, and at discharge.
Nurses must consider several factors when selecting appropriate NOC outcomes. These include the nature of the health concern, the patient’s nursing, medical, and overall health problems, patient characteristics, available resources, patient preferences, capabilities, and treatment potential. Clinical evaluation of the measurement scales within NOC has been conducted for a significant number of outcomes. These evaluations have demonstrated the reliability of the scales and their ability to capture changes in patient status, even during short hospitalizations in acute care settings. These studies have also highlighted the importance of maintaining stable outcome ratings for elderly patients in long-term care facilities as a key indicator of quality care in these settings. Clinically useful tools for measuring the day-to-day care of patients are essential for nurses. Outcome measurement is crucial for communicating the quality of care provided to patients, healthcare system administrators, and public policymakers.
Many healthcare organizations are increasingly focusing on measuring outcomes related to “never events,” such as falls, pressure ulcers, and urinary tract infections. NOC offers a systematic approach to measuring patient-focused outcomes that reflect the positive impact of nursing interventions and the results of interventions aimed at preventing adverse events. Outcomes can be measured post-intervention to assess the effectiveness of the care plan. Patient responses can range from dramatic improvements, such as a shift from a rating of “1” to “5,” to incremental changes over time. The advantage of using NOC outcomes lies in its capacity to allow both patients and providers to measure the effects of interventions and for nurses to track trends in specific outcomes within patient populations they frequently treat.
Research Support for Linking NNN
Historically, research on NNN (NANDA-I, NIC, NOC) often examined these as distinct and separate languages. However, there is a growing body of research investigating the value of using these three languages in conjunction. Studies increasingly explore the synergistic effects of linking nursing diagnoses, interventions, and outcomes in patient care. When studied separately, the research types and foci varied. Research on NANDA-I diagnoses primarily centered on individual diagnoses relevant to specific patient populations, while studies of NIC and NOC have often been broader, evaluating the systems as a whole rather than individual concepts within them. Research from Switzerland, Sweden, the Netherlands, and the United States increasingly supports the integrated application of NNN in clinical practice and research.
In a pretest-posttest study design, Muller-Staub et al. (2007) examined the impact of an educational intervention on the quality of documentation and patient outcomes among nurses across 12 wards in a Swiss hospital. The educational program focused on the implementation of nursing diagnoses, nursing interventions, and nursing-sensitive patient outcomes. Before and after the educational intervention, two sets of 36 randomly selected patient records were assessed for quality using the validated and reliable Quality of Nursing Diagnoses, Interventions and Outcomes (Q-DIO) instrument. The results showed a significant improvement in the quality of nursing diagnoses documentation post-intervention, with mean scores increasing from 0.92 to 3.50.
Another pretest–posttest study evaluating nursing process documentation following a year-long education initiative in a large hospital system (encompassing 50 inpatient wards and 30 outpatient clinics) demonstrated improvements in nursing assessment, nursing diagnosis, and nursing intervention documentation. The SNLs of NANDA-I and NIC were used to educate nurses on documenting human responses and nursing interventions. Notably, patient outcomes were not taught using SNLs in this initiative, and this aspect of the nursing process was the only one that did not show improvement, highlighting the importance of comprehensive training in all components of SNLs.
The accuracy of six aspects of nurses’ documentation was evaluated in a random sample of 10 medical centers in the Netherlands. Patient records were assessed by trained reviewers using the D-Catch instrument to measure the accuracy of record structure, admission documentation, diagnosis documentation, intervention documentation, progress and outcome evaluation, and legibility. The findings indicated that a significant portion of records did not fully adhere to nursing process stages, suggesting that EHR systems should better support nurses in accurate documentation by providing structured guidelines and logical systems.
Consensus validation studies employing action research methods have been conducted with staff nurses to identify nursing diagnoses, interventions, and outcomes most relevant to specific patient populations in hospitals, long-term care, ambulatory settings, and end-of-life care. These studies aimed to simplify the application of NNN in clinical units serving patients with specific health problems. Carlson (2006, 2010) developed the Total Consensus Method to achieve complete consensus among experienced nurses on the specific terms to be used in standards of care and EHR front screens. This method was used to identify terms for an Electronic Nursing Documentation System (ENDS) used by military nurses specializing in latent tuberculosis infection care.
In a study focused on identifying relevant NNN terms for adults with traumatic brain injury (TBI), a consensus of nurses identified 29 nursing diagnoses, each linked with 3–11 NIC interventions and 1–13 NOC outcomes, as pertinent to the TBI patient population served in their facility. Similarly, a hospital-based study involving nurses caring for adults with diabetes identified 17 nursing diagnoses, each associated with 7–19 NIC interventions and 4–14 NOC outcomes, as relevant for this patient group. These consensus validation methods show considerable promise for enabling nurses in various healthcare units to select the most pertinent terms from the extensive NNN lexicon for specific patient care scenarios.
Decision Support Systems and NNN
The next wave of research supporting NNN is increasingly focused on testing decision support systems (DSS). Carlson (2010) evaluated the ENDS in army military settings, aiming to assess the value of nursing care by identifying patient care outcomes positively influenced by ENDS, establishing practice standards, providing data for resource management, developing a reliable patient acuity index, and identifying revenue generation methods. The ENDS performed as intended, demonstrating that nurses effectively used nursing diagnoses, linked NIC interventions, rated NOC outcomes, and tracked intervention times. Patient outcome scores post-intervention were significantly higher than baseline scores, indicating the positive impact of nursing diagnoses and interventions facilitated by ENDS.
The HANDS documentation system utilizes NNN to ensure interoperability at technical, semantic, and process levels, supporting continuity of care through consistently gathered, readily available, and uniformly formatted data that retains meaning across users. Research over a decade has established HANDS as cost-effective and sustainable, capable of automatically generating new evidence from collected data and delivering data feedback directly to the point of care.
The WiCareDoc expert system uses 26 questions to aid nurses in selecting the most appropriate terms for clinical practice, prompting nurses to evaluate hypothetical diagnoses and suggesting interventions and outcomes based on diagnoses. Developed and tested in Switzerland, WiCareDoc is representative of expert systems being developed in other countries, such as Spain, Brazil, and Japan, to enhance clinical decision-making in nursing.
SNLs and Evidence-Based Nursing Practice
The growing emphasis on evidence-based practice (EBP) presents a significant opportunity for the advancement, application, and evaluation of nursing language development. The demand for EBP encourages nurses to utilize the best available research, including studies on SNLs, to inform clinical decisions regarding treatments and outcomes. Current evidence strongly supports the integration of SNLs into clinical practice and EHRs. Using SNLs within EHRs enables nurses to formulate research questions that can be addressed using existing local databases.
A follow-up meeting to the Institute of Medicine (IOM) report highlighted the need for improved documentation for chronic health problems—including diabetes, acute pain, heart failure, and asthma—to enhance quality and cost-effectiveness in patient care. The report emphasized the need for better measurement of patient-centered outcomes, standardized systems for disseminating information and sharing EBPs, and promoting self-management. Evidence derived from the consistent use of SNLs in nursing documentation can contribute significantly to realizing these goals and advancing research-driven nursing care.
Seven levels of evidence are recognized in the literature, ranging from systematic reviews of randomized controlled trials (RCTs) to expert opinions. While research on SNLs continues to expand, further work is needed, particularly at higher levels of evidence. It is crucial for nurses to continue building research around the development, implementation, and evaluation of SNLs and to integrate these languages into EHRs. The availability of large databases using standardized languages is essential for testing, refining, and evaluating SNLs, and for developing predictive models that accurately link patient care elements with cost and staffing demands.
| Level of Evidence | SNL Research |
|---|---|
| Level 1: Evidence from systematic review meta-analysis of all relevant control trials or evidence-based practice guidelines based upon RCTs | None identified |
| Level 2: Evidence obtained from at least one well-designed randomized clinical trial (RCT) | Studies of teaching SNLs and accuracy of nursing diagnoses (Levin, Lunney, & Krainovich-Miller, 2004; Mueller-Staub et al., 2007; Paans et al., 2010, May) and accuracy of documentation (Paans et al., 2010b) |
| Level 3: Evidence obtained from well controlled clinical trials without randomization (quasi experimental) | Studies of the effect of teaching critical thinking (e.g., Cruz et al., 2009), and of implementing policy (e.g., Thoroddsen & Enfors, 2007) |
| Level 4: Evidence from nonexperimental studies, for example, case control or cohort studies | Extensive numbers of nonexperimental studies (e.g., del Bueno, 2005; Gordon, 1987; Sparks, 1990). Measurement (Hoskins, 1989; Kim, 1990) |
| Level 5: Evidence from systematic reviews of descriptive/qualitative studies | Epidemiological studies on occurrence or frequency (e.g., Schroeder, 1990); testing and refinement, and some systematic reviews related to generation of diagnoses |
| Level 6: Evidence from single descriptive/qualitative studies | Many studies that focused on populations or groups to identify high frequency or commonly occurring nursing diagnoses. The literature continues to report these studies: Flanagan and Jones (2009), Jeffries, Cox, et al. (2010, in press), Gordon, 1987, Gordon and Sweeney (1979) Fehring’s validation model (1987) to estimate content and construct validity of the concepts (Whitley, 1996) |
| Level 7: Evidence from opinion of authority or experts | Much of early development used expert opinion (e.g., Gebbie & Lavin, 1975), including Delphi methods, for concept development, testing, and refinement |
SNLs, Nursing Administration, and EHRs: Enhancing Healthcare Systems
The adoption and effective utilization of Standardized Nursing Languages (SNLs) offer significant advantages to nurse executives and administrators. SNLs provide a robust framework for managing the complexities of the healthcare environment, fostering a professional practice setting that promotes clear communication, reduces administrative burdens on staff, optimizes patient experiences, and enhances job satisfaction for nurses, patients, and their families. The value of SNLs to the nursing workforce is intrinsically linked to the opportunities derived from the clear and consistent communication of nursing phenomena.
Facilitating Storage of Nursing Data in Electronic Health Records
Globally, healthcare systems are transitioning from paper-based records to Electronic Health Records (EHRs) in standardized formats. This shift encompasses all facets of patient care, integrating data currently dispersed across various formats. In every healthcare setting, from primary to acute to ambulatory care, data will be communicated in prescribed formats at local, regional, national, and international levels. EHRs rely on standard terminology—file names that healthcare providers use to store and retrieve similar information. Without standardized file names, specific types of healthcare data cannot be effectively identified, aggregated, analyzed, or compared.
The primary goals of EHRs are to comprehensively describe the care provided, facilitate seamless communication of this care to others (interoperability), and assess whether patient care meets established benchmarks for quality-based care (meaningful use). For nursing care to be visible and effectively represented in EHRs, nurses must utilize file names that are specific to nursing, distinct from those used by medicine, psychiatry, and other healthcare disciplines. ANA-approved SNLs provide these standardized file names, ensuring that nursing care is accurately and consistently communicated within EHR systems.
Visibility of nursing practice within EHRs enables nurses and other healthcare professionals to identify the care being delivered and to analyze its quality. Using SNLs and measurement schemas, the accuracy of documentation in patient records can be effectively assessed. Enhancements in nursing care quality are contingent upon the ability to clearly describe care and compare it against quality benchmarks. For instance, the identification of acute pain and the implementation of appropriate interventions can be evaluated to determine whether nursing care aligns with standards advocated by organizations like the American Pain Society. Prior research has indicated variability in nursing diagnoses and interventions related to pain management, underscoring the need for standardized approaches facilitated by SNLs.
Moreover, the integration of SNLs into EHRs by nurses enhances the visibility of nursing practice and promotes the continued development of concepts essential to the nursing discipline. Researchers gain access to rich databases for testing and refining nursing diagnoses, interventions, and outcomes, and for developing predictive models to inform staffing decisions and accurately determine costs associated with nursing care.
Enhancing Quality and Patient Safety Through SNLs
Improved quality of care and enhanced patient safety are anticipated outcomes of clear communication enabled by SNLs regarding nurses’ data interpretations (diagnoses), nursing interventions, and patient outcomes. The phenomena addressed by nurses are complex and diverse, making it challenging to select the most appropriate diagnoses, interventions, and outcomes. The vast array of possibilities within human behavior and strategies to improve health can be overwhelming. SNLs mitigate cognitive demand by providing readily accessible, standardized terms that best apply to specific clinical situations, thereby facilitating high-quality care and effective communication.
Given the complexity of interpreting and communicating human responses and experiences related to health and illness, achieving accurate interpretations of patient data is exceptionally challenging. Variance in accuracy is expected, regardless of whether nurses use a standardized language for nursing diagnosis. Studies have consistently demonstrated this variability. Factors influencing the accuracy of nurses’ diagnoses can be broadly categorized into three areas: the nature of the diagnostic task, the situational context, and the diagnostician themselves. SNLs, which include a comprehensive array of possible diagnoses and data interpretations, offer nurses the standardized labels, definitions, and defining characteristics needed for accurate assessment and validation with patients in collaborative care models. Studies have consistently shown significant variability in data interpretations and diagnostic accuracy among nurses, emphasizing the need for tools like SNLs to improve consistency and precision.
| Categories and Researchers | Factors | Significant Findings |
|---|---|---|
| Situational Context | ||
| Gordon, 1980 | Time constraints | When information was deliberately restricted to no more than 12 units of info. subjects were more accurate (88%) than with unlimited information (48%; p =.001). |
| Cianfrani, 1984 | Increased time to diagnose was associated with lower accuracy. | |
| Tanner et al., 1987 | Increased time to diagnose was associated with lower accuracy. | |
| Lenz et al., 1986 | Role in the health care system | Differences in interpretations of data were associated with CNS preparation or role. |
| Hasegawa et al.. 2007 | Diagnostic decision making responsibility | In a national survey of Japanese nurses (N = 376, 85% response rate), those who reported diagnostic responsibility demonstrated significantly higher competence in specific parts of the task case studies. |
| Nature of the Diagnostic Task | ||
| Matthews & Gaul, 1979 | Task complexity | With two case studies (CS), there was an inverse relationship between diagnostic ability and complexity of the CS (significance not mentioned, validity & reliability of the cases not established). |
| Corcoran, 1986 | Task complexity | Complexity influenced planning interventions for cancer pain (diagnosis of cancer pain was implied). |
| Hughes & Young. 1990 | Task complexity | Three CS were used with increasing task complexity (n = 101 nurses). Task complexity was associated with less consistency in decision making. Decision making varied with each task; Decision making was task specific. |
| Gordon, 1980 | Task complexity | Subjects did better when information was limited (see above); unlimited amount of data was assoc with continuation of predictive hypothesis testing. |
| Cianfrani, 1984 | Amount of data | With high amounts of data, accuracy decreased with 1 of the 3 CS (p = .001). There was an increase in errors with 2 of the 3 CS (p = .02; p = .001) and an increase in time with 2 of the 3 CS (p = .005; p = .05). More problems were hypothesized with 2 of the 3 CS (p = .05; p = .01). |
| Relevance of data | Accuracy decreased with low relevance data for all 3 CS (p p = .04; p = .000). | |
| Hicks, Merritt, & Elstein, 2003 | Task complexity | 31% of critical care nurses (N = 54) from 3 hospitals demonstrated consistency of intervention decision making (diagnosis was implied) with a low complexity task; only 11% demonstrated consistent decision making with a high complexity task. |
| Diagnostician: Education | ||
| Aspinall, 1976 | MastersBS degreeAssociate (AAS)Diploma (DIP) | Mean number of correct diagnoses out of 12 4 3.93 3.35 3.23Significant difference between BS and AD (p p |
| Matthews & Gaul, 1979 | Masters studentsBaccalaureatestudents | % stated correct diagnosis (p 62 (Explanation: more use of negative & positive cues)50% who listed task diagnosis (estimated from bar graph) |
| Craig, 1986 | Masters students | 82 (had been taught the diagnostic process) 35 (no previous nursing, 1st year)30 (entered as nurses, 1st year)45 (entered as nurses, 2nd year)42 (generic); 49 (RN) |
| BaccalaureatestudentsDiploma students | 46 (with internship); 46 (without internship) 52 (with one year experience) | |
| Konno et al.. 2000 | Diploma graduates College education Technical education | With a written CS, nurses with college education were more accurate than nurses with technical education. 91% had never learned nursing diagnosis so differences were related to other factors. |
| Lunney, 1992 | Continuingeducation | BS nurses (n =86) who reported having additional education on nursing diagnosis after graduation were more accurate with 3 written CSs than nurses who reported that they had no additional education (p |
| Lunney et al.. 1997 | Continuingeducation | Nurses (n = 62) who reported having additional education on nursing diagnosis after graduation were more accurate with actual cases than nurses who reported they had no additional education. |
| Mueller-Staub et al., 2007 | Continuingeducation | In a pretest-posttest study of nurses from 6 randomly selected wards of a Swiss hospital, the quality of patient records showed significant improvement in formulating nursing diagnoses (p |
| Cruz, Pimenta, & Lunney, 2009 | Continuing education (CE) | A 16-hour CE course on critical thinking for clinical judgment was offered to experienced nurses (N = 39); a pretest-posttest design was used to measure the effects. Accuracy of diagnosis improved with case study one (p = .008), case study two (p = .042 ) and overall (p = .001). |
| Hasegawa et al., 2007 | Knowledge of nursing diagnosis definitions | Those nurses who scored higher on the test of nursing diagnosis definitions demonstrated higher accuracy with the two case studies. |
| Diagnostician: Use of Teaching Aids | ||
| Aspinall, 1979 | Decisions trees List of problems No teaching aid | Three groups (gp), matched for education & experience, one experimental, two controls, t tests done, p Experimental gp; m = 3.8; highest possible score = 6Control gp; m = 2.567 Control gp; m = 1.667 |
| Craig, 1986 | Taught the diagnosis process | 82% listed task diagnosis (p 7 other groups (see above) ranged from 30% to 56% |
| Tanner, 1982 | Not taught Taught hypothesis testing | No significance between pre and post test; One explanation: Scoring of accuracy did not allow for variations in statements. |
| Thiele et al., 1986 | Computersimulation | Junior and senior sts improved in cue recognition, cue sorting/linking, & clinical decision making with computer simulation (p |
| Fredette & O’Neill, 1987 | 5 hours didactic content on diagnostic process | 2 studies, experimental & control gps.1st study-experimental gp identified more diagnoses2nd study-exp gp did better overall in diagnosing a case study & in written papers, did better in two categories |
| Pinnell et al., 1992, & Spies et al., 1994 | 20-hour course on nursing process with 4 hours on diagnostic reasoning | Average pre-course accuracy (n = 73 nurses) was 2.6 on Lunneys 7 point scale; after the course accuracy improved to n average of 3.1 (p |
| Lasater & Nielson, 2009 | Concept-based learning | The intervention group (n = 15 students) who were taught using a concept-based approach scored statistically higher (p n = 13) on four types of clinical judgment, including data interpretation. |
| Paans et al., 2010,May | Education about the PES system | In a randomized factorial design study with four groups, knowledge of the PES system was significantly related to accuracy of nursing diagnoses. |
| Diagnostician: Nursing Experience | ||
| Aspinall, 1976 | Below 10 years | Greater number of correct diagnoses |
| Over 10 years | Fewer number of correct diagnoses | |
| Aspinall, 1979 | Below 2 years | Scored highest; doubled score with decisions trees |
| 2–10 years | Scored lowest; profited least with decision trees | |
| Over 10 years | Scored low; profited most with decision trees | |
| Tanner et al., 1987 | Junior students | Positive association of gp status & accuracy |
| (n = 15) | (p | |
| Senior students | Generally, no difference between gps in | |
| (n = 13) | 4 categories of data acquisition. | |
| Westfall et al.,1986 | Nurses (n = 15) | Experienced nurses generated more complex |
| Same population/study as Tanner et al. | hypotheses than either group of students (p = .03) | |
| Holden & Klingner, 1988 | Students | Task was to diagnose why an infant was |
| 1st semester | crying (teething). Experienced gp (nurses | |
| juniors | and parents) asked for more information than | |
| Last semester | inexperienced gp (p | |
| seniors | was more likely to ask for valid and reliable | |
| Parents | cue on 1st choice than inexperienced gp | |
| (juniors & | (p | |
| seniors) | associated with 100% accuracy | |
| Konno et al., 1999, 2000 | Experienced nurses | Experienced nurses were less accurate than students. |
| Years of experience | There was no difference in accuracy by years of experience. | |
| Junnola et al.,2002 | Perceptions of influence of experience | 90% of nurse participants (N = 107) said that professional experience influenced their identification of problems in an oncology case simulation |
| delBueno, 2005 | Years of Experience | With 10 years of competency data from more than 30,000 nurses, it showed that ability to use clinical judgment for identification of patients’ problems in 3 case simulations varied widely, with new nurses significantly worse than experienced nurses |
| Hasegawa et al.,2007 | Years of Experience | Years of nursing experience was associated with higher diagnostic competency in all three measures of competency (p |
| Diagnostician: Cognitive Strategies | ||
| Gordon, 1980 | Hypothesis generation | Number of hypotheses generated is not as important as the correct one being considered early in the task.Cessation of this strategy in the first half of the task was associated with accuracy. |
| Predictive hypothesis testing | Use of this strategy in the second half of the task was associated with accuracy. | |
| Specific hypothesis testing | Number of hypotheses generated is not as important as “correct” one being considered early in the task. | |
| Tanner, 1982 | Hypothesis generation | Even when hypotheses are generated, they were not necessarily tested. |
| Hypothesis testing Activation of early hypotheses | In one of the 3 CS (videotapes & hospital records), more experienced nurses activated early hypotheses (73%) than junior (27%) or senior students (38%) (p = .029). | |
| Tanner et al., 1987 | Question relevance | For 1 of the 3 CS, experienced nurses asked more relevant questions than students (p = .022). |
| # of hypotheses# of questions | No difference among 3 gps with different levels of education and experience. | |
| Matthews & Gaul, 1979 | Types of cues used | Use of both negative and positive cues specific to the case was associated with accuracy; supports Gordons results re: hypothesis testing |
| Thiele et al., 1991 | Cue selectionrelevant or nonrelevant | In a perioperative simulation, the pattern of cue selection of 86 junior sts was 68–85% accurate for relevant cues but also, with overselection, there was 50–60% selection of nonrelevant cues. 72% selected accurate diagnoses & 72% selected appropriate Nursing interventions; also 50–60% selected inaccurate diagnoses and inappropriate interventions. |
| Brannon & Carson, 2003 | Representative Heuristic | Both nurses and student nurses (N = 182) dismissed the appropriate physical diagnoses when situational variables such as loss of job were included, showing that the representative heuristic was being used. |
| Ferrario, 2003 | Four types of heuristics | With a national random sample of experienced (n = 173) and inexperienced (n = 46) emergency room nurses (n = 173) judged cases by causal factors significantly more often than inexperienced nurses (n = 46). |
| Junnola et al..2002 | Information Acquisition | With a computer-based oncology case simulation, the four most important problems were mentioned by 65% of nurses (N = 107). Information acquisition in general was associated with identification of problems (p |
| Paans et al. 2010, May | Critical thinking Disposition | In a randomized study with four groups, truth-seeking and open-mindedness were positively related to accuracy of nursing diagnoses. |
| Diagnostician: Cognitive Abilities | ||
| Gordon, 1980 | Inferential ability | No relationship with accuracy using the results from the Graduate record Exam & Miller Analogies Test. |
| Matthews & Gaul, 1979 | Critical thinking | No relationship between nursing diagnosis and critical thinking, as measured by the Watson-Glaser critical thinking appraisal. |
| Lunney, 1992 | Divergent production of semantic units | Three valid and reliable case studies were used. Scores were low on the tests of divergent thinking. |
| (Fluency) | Accuracy was positively related to fluency with CS 2 (p = .002) | |
| Divergent production of semantic classes (Flexibility) | Accuracy was positively related to flexibility with CS 2 (p = .03). | |
| Divergent production of semanticimplications(Elaboration) | Accuracy was positively related to elaboration with CS 2 (p = .03) and CS 3 (p = .03). | |
| Divergent thinking is probably more relevant to actual cases than written cases with defined amounts of data. | ||
| Paans et al., 2010, May | Analysis & Inference | In a randomized study with four groups, analysis and inference, as measured by the health sciences reasoning test were positively related to accuracy of nursing diagnoses. |
Integrating SNLs into EHRs enhances interdisciplinary communication, improves decision-making accuracy, and refines documentation practices. These improvements collectively contribute to reducing errors, precisely targeting patient problems, providing more accurate quality measures, and identifying factors that elevate patient risk.
Enhancing Effectiveness and Efficiency of Care
SNLs improve both the effectiveness and efficiency of healthcare delivery by establishing common meanings that are universally accessible to all users. When healthcare professionals utilize the same defined terms, communication regarding patient care becomes clearer, thereby enhancing both effectiveness and efficiency. Although there is an initial learning curve associated with adopting EHR systems, in the long term, documentation becomes quicker as standardized terms can be selected from lists, rather than requiring extensive narrative notes. A time and motion study across 36 hospitals revealed that nurses spend a significant portion of their time on documentation, underscoring the need to streamline this process.
SNLs are also invaluable for measuring nursing workload, which is crucial for effective and efficient resource allocation. Both NANDA-I and NIC are utilized in developing workload measurement methodologies.
As the healthcare industry increasingly focuses on the cost-benefit ratio of nursing care, integrating SNLs into EHRs becomes even more critical. Insurers and third-party payers have emphasized the need for national-level adoption of SNLs to facilitate cost accounting and reimbursement negotiations for nursing services. With a substantial volume of clinical data generated through SNL utilization, third-party reimbursement for nursing care is becoming an increasingly feasible prospect for the future.
Autonomy and Professional Control in Nursing Practice
Professional autonomy is a fundamental attribute of nursing. When nurses autonomously practice using SNLs, they engage in generating diagnoses and interventions rooted in nursing’s unique body of knowledge, exercising self-controlled actions that do not necessitate external authorization. As early as 1969, Abdellah emphasized the importance of nursing diagnosis and prescribed nurse actions for developing nursing science. Without SNLs, the distinct voice of nursing is diminished, and professional autonomy is compromised.
A study on the influence of SNLs on nurses’ autonomy concluded that consistent use of SNLs in every patient encounter is vital for fostering professional autonomy and clarifying nurses’ control over their practice. SNLs are seen as a means to promote professional unity and role clarity within healthcare environments. Conversely, the absence of SNLs in the workplace can render nursing decisions arbitrary and undermine the scientific basis of nursing practice.
Workforce Implications and Standardized Nursing Terminologies
Establishing and justifying nursing workforce targets requires the ability to continuously assess the impact of nurse staffing levels and roles on healthcare delivery and patient outcomes. Without a clear methodology to demonstrate the contributions of nursing care, systematically improving care, justifying effective staffing patterns, and promoting cost-effective care becomes impossible. Historically, the absence of nursing terminologies made quantifying nursing’s impact in a way that would support quantitative assessments exceedingly difficult. This lack of meaningful data characterizing nursing’s contribution has hindered the profession’s ability to make credible workforce projections linked to specific patient outcomes.
The creation and availability of standardized nursing terminologies provide the foundational elements needed to assess nurses’ impact on healthcare and patient outcomes. Capturing nursing diagnoses, outcomes, and interventions using standardized terminologies within electronic documentation (EHRs) enables the retrieval of information about the focus and nature of nursing care provided and its subsequent impact. While terminologies are necessary, they are not sufficient in isolation. If each EHR vendor system and healthcare organization independently adapts standardized terminologies to meet unique specifications, the potential to evaluate the impact of nursing is significantly diminished. This customization, or “tweaking,” which occurs at various levels, from the user interface to database architecture, currently prevails and compromises the reliability and validity of data captured using nursing terminologies, rendering it less useful as evidence to characterize nursing practice.
The HANDS Research Initiative: An Interoperable Solution
A common approach is needed to integrate nursing terminologies into EHRs in a way that ensures the reliability and validity of nursing data for accurately characterizing the nursing workforce’s impact on healthcare and patient outcomes. This objective has been the driving force behind the HANDS research team for over a decade. Since 1996, this team has been engaged in developing and testing a web-based dynamic care plan documentation and handoff communication system. HANDS utilizes the standardized nursing terminologies of NANDA-I, NOC, and NIC to represent nursing diagnoses, outcomes, and interventions, respectively. HANDS is designed to connect to any EHR and serve as the care coordination and care planning component. HANDS achieves the three levels of interoperability recommended by Health Level 7’s EHR Interoperability Work Group to ensure data validity and reliability across all systems using HANDS: semantic, technical, and process interoperability.
Semantic interoperability ensures that the meaning of terms remains consistent across users. Technical interoperability is achieved through a single standardized user interface and database structure. Process interoperability is maintained through adherence to standardized training modules and consistent rules of use in practice, such as updating the care plan at every formal nursing handoff and using the care plan to structure communication during handoffs.
In a recent AHRQ-funded multi-site study, HANDS was implemented and tested in four types of hospitals across eight diverse medical-surgical units, capturing data from 39,322 episodes of care over two years. Quota sampling ensured a broad representation of medical-surgical unit types and organizations. Participating units met study readiness criteria, including adequate staffing and agreement to fully use HANDS as directed for documenting care plans and communicating at every formal handoff. The study aimed to determine if interoperability could be maintained at all three levels across diverse units and to evaluate user satisfaction with HANDS and standardized terminologies. Mixed methods were used, and results provided strong evidence that these interoperability levels are achievable and sustainable across diverse settings. Nurses reported HANDS as significantly more useful than previous care planning methods and expressed greater satisfaction with NANDA-I, NOC, and NIC after using HANDS for one to two years.
| Hospital | Unit | Unit Type | Total No. Episodes of Care | Total No. EOL | % EOL Episodes | No. EOL Episodes With Pain | % Pain Out of EOL Episodes |
|---|---|---|---|---|---|---|---|
| LCH1 | 1 | General Medical | 5,451 | 189 | 3.5 | 72 | 38.1 |
| LCH1 | 2 | Medical ICU | 1,065 | 163 | 15.0 | 69 | 42.3 |
| LCH1 | 3 | Gerontology | 9,046 | 519 | 5.7 | 113 | 21.8 |
| UH | 4 | Cardiac Surgical | 6,061 | 51 | 0.8 | 33 | 64.7 |
| UH | 5 | Neuro Surgical | 8,119 | 97 | 1.2 | 65 | 67.0 |
| LCH2 | 6 | Medical Gerontology | 1,557 | 116 | 7.5 | 71 | 61.2 |
| LCH2 | 7 | General Medical | 3,276 | 156 | 4.8 | 104 | 66.7 |
| SCH | 8 | Medical Surgical | 4,747 | 134 | 2.8 | 69 | 51.5 |
| Total | 39,322 | 1,425 | 3.6 | 596 | 41.8 |
Number of EOL patients as a percentage of all patients and the number of EOL patients with pain diagnosis as a percentage of all EOL patients in the HANDS database from 2005 to 2007.
LCH1, large community hospital 1; UH, university hospital; LCH2, large community hospital 2; SCH, small community hospital, all in the Midwest.
The AHRQ study results provide robust evidence that implementing and maintaining a universally applicable care plan system using NANDA-I, NIC, and NOC across diverse care settings is both possible and feasible. These findings indicate that valid and reliable nursing care data can be generated through an electronically supported care plan system designed and tested to meet user and stakeholder needs. Data collected with HANDS includes nursing diagnoses, outcomes, interventions, changes over time, patient and nurse demographics, and nursing workload, supporting both daily care and secondary data uses.
Secondary Applications of HANDS Data
The HANDS research team is currently focused on demonstrating the secondary uses of valid and reliable data captured in systems like HANDS, particularly in supporting workforce policy. Pilot studies are underway, utilizing statistical, data mining, and usability engineering teams. One study examines pain management in end-of-life patients using data mining and statistical techniques to develop decision support alerts. Analysis of anonymized data from the AHRQ study identified end-of-life episodes based on criteria such as NOC outcomes (Comfortable Death, Dignified Life Closure), NIC interventions (Dying Care), discharge to hospice, or patient expiration. Preliminary findings indicate that pain management in end-of-life care is suboptimal in representative acute care units and that specific combinations of nursing interventions are associated with better pain outcomes.
Another pilot study uses AHRQ study data to examine nurse-related characteristics, specifically the impact of shift length and nurse continuity (number of unique nurses per episode of care) on patient outcomes. The ability to address these workforce-related questions precisely is due to the data automatically captured as nurses used the HANDS care plan system to document and monitor care. These pilot studies illustrate the vast range of nursing-related questions that can be addressed when standardized terminologies are widely implemented in interoperable documentation and communication systems that are user-friendly.
Future Directions in SNLs and Nursing Practice
The Institute of Medicine (IOM) report, The Future of Nursing: Leading Change, Advancing Health, underscores the pivotal role nursing will play in leading healthcare improvements. A key goal highlighted in the report is the necessity for enhanced data collection and an improved information infrastructure to support effective workforce planning and policymaking. SNLs are crucial for addressing workforce demands, developing predictive models for high-quality, safe, efficient, and effective care, costing nursing services accurately, and documenting nursing’s contribution to patient outcomes. Achieving this vision requires nursing to be distinctly visible within EHR systems through the use of standardized languages and comprehensive educational preparation for nurses in documenting their practice.
While nursing diagnoses, interventions, and outcomes, informed by models like McFarland’s, provide valuable insights into patient care influenced by nursing, they do not fully encompass the breadth of nursing practice. Continued development, testing, and refinement of nursing languages, alongside advancing disciplinary knowledge through research, will expand nursing science and its knowledge base. The potential application of SNLs in randomized trials, population-based studies, and data mining will extend nurses’ opportunities to contribute to knowledge-driven care. Qualitative studies focused on understanding the nuances of human experiences (e.g., self-care, resilience) can identify themes and concepts, potentially leading to the naming of new phenomena of concern. Quantitative studies can validate phenomena, identify significant relationships, and generate predictive care models to improve care accuracy and comprehensiveness.
Adopting a standardized and unified language internationally will facilitate cross-cultural and population-based studies of nursing phenomena. This standardization will also drive instrument development for measuring these phenomena, promote intervention studies, and lead to outcome and evidence-based studies, including randomized clinical trials. With consistent use of standardized language, administrative models can be developed to refine staffing ratios and patterns, integrating provider levels to enhance safe, efficient, cost-effective, and high-quality patient-centric care.
Contributor Information
Dorothy Jones, Boston College, William F Connell School of Nursing, Chestnut Hill, MA, USA.
Margaret Lunney, City University of new York, College f Staten Island, Staten Island, NY, USA.
Gail Keenan, College of Nursing, University of Illinois at Chicago, Chicago, IL, USA.
Sue Moorhead, Center for Innovations in Care Delivery, Massachusetts General Hospital, Boston, MA 02114, USA.
REFERENCES
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