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Nursing Informatics: Scope and Standards of Practice, 3rd Edition 3
The Scope of
Nursing Informatics
Practice
During their initial deliberations, the review and revision workgroup mem-
bers identified the need to develop a revised definition of nursing infor-
matics specialty to better reflect the contributions and evolution of this
nursing specialty within the health care environment. They focused not
only on the immediate contemporary world and the impact of the COVID-
19 pandemic but envisioned significant future influences related to the
2021 publications of the American Association of Colleges of Nursing
(AACN) Essentials: Core Competencies for Professional Nursing Education
and the National Academy of Sciences, Engineering, and Medicine
(NASEM) The Future of Nursing 2020–2030: Charting a Path to Achieve
Health Equity.
Extensive review of the draft nursing informatics scope and standards
document during the 2021 online public comment period confirmed the
draft definition was not favorably endorsed. As a result, the workgroup
members examined numerous other existing definitions of informatics
specialties and crafted this concise definition:
Nursing informatics is the specialty that transforms data into
needed information and leverages technologies to improve
health and health care equity, safety, quality, and outcomes.
The nursing informatics specialty and its constituent members of infor-
matics nurses (IN) and informatics nurse specialists (INS) contribute to
achieving the important goal of improved health of populations, commu-
nities, groups, families, and individuals. An informatics nurse is a regis-
tered nurse with an interest or experience in an informatics field, most
4 The Scope of Nursing Informatics Practice
often nursing informatics. The informatics nurse specialist is a registered
nurse with formal graduate education in informatics. The INS is expected
to have experience in informatics projects or processes and have achieved
and maintains applicable certifications. The evolving nursing informatics
practice environment reaffirms the need for the informatics nurse to con-
sider graduate level preparation to assume the informatics nurse special-
ist role supporting work in operations as project managers, analysts, and
department leaders and doctoral preparation for system level leaders, inno-
vators, and data scientists. The term informatics nurse is often used in
this document as the global inclusive term representing the IN and INS
collective.
Nursing informatics activities include, but are not limited to, the design,
development, implementation, and evaluation of effective informatics solu-
tions and technologies within the clinical, administrative, educational,
and research domains of practice. Advocacy, policy development, and iden-
tification of issues, challenges, and opportunities are also important prac-
tice initiatives. The value of nursing informatics becomes more evident
from this perspective.
Figure 1. The Nursing Informatics Specialty Components and Rela-
tionships introduces a conceptual framework that serves as a lens through
which to view nursing informatics practice. It provides a rich representa-
tion of the diverse concepts and relationships important to this specialty.
Nurses, health care consumers, patients, and other stakeholders are the
center of interest or focus. The immediate surrounding concentric circle
represents the action of decision-making to achieve desired outcomes.
The next adjacent concentric circle bearing the people, processes, struc-
tures, and technologies terms is intended to characterize those impor-
tant active supports, facilitators, and agents incorporated within nursing
informatics practice. The externally positioned identification, manage-
ment, communication, and integration terms are co-located and aligned
respectively with data, information, knowledge, and wisdom, the foci of
those actions.
6 The Scope of Nursing Informatics Practice
VALUE STATEMENTS
With a unique contextual understanding of the health care ecosystem, INs
and INSs are essential to harness the rapidly increasing power of technol-
ogy, information, and communications to advance health and health care
delivery across the spectrum of human experience. Using their continu-
ally evolving knowledge and skills, these nurses provide value in the
following ways:
- Unique combination of nursing and informatics practice
expertise
Informatics nurses ensure clinical context is brought to system design,
closing the semantic gap between clinical practice and information
systems to preserve data, information and meaning. They serve as lead
innovators and entrepreneurs for advancement of nursing practice using
technology, information, and communications. Informatics nurses engage
the appropriate subject matter experts, clinical and operational end users,
and other stakeholders in requested projects and initiatives.
- Expert understanding of health care delivery and operational
flow
Informatics nurses provide an in-depth understanding of clinical care
delivery operations in nursing and ancillary areas and the impact of sys-
tem change within and outside health care delivery. Their expertise in
applied clinical informatics helps INs reduce or eliminate the burdens of
current and developing technology. Informatics nurses are critical resources
for analyzing, designing, and implementing effective user experiences for
nurses, patients, families, and other members of the health care team.
- Data, information, and knowledge management for
individuals and populations
Informatics nurses keep concepts of care and patient outcomes at the
forefront by applying concepts of nursing theory during management of
Nursing Informatics: Scope and Standards of Practice, 3rd Edition 7
data. They help ensure that data and information quality remain crucial
aspects of current and emerging analytics and resulting outcomes. Bring-
ing workflow and practice awareness forward helps ensure the right infor-
mation is available in the right format to the right people at the right time
for the right purpose. Adding nursing clinical expertise enhances efforts
in data science, natural language processing, machine learning, precision
medicine, and other developing strategies.
- Informatics leadership and organizational strategy
Informatics nurses promote collaboration with clinicians, vendors, qual-
ity and safety partners, information technology (IT) professionals, and
other stakeholders to develop and implement efficient and effective solu-
tions for clinical problems. Informatics nurses influence key stakeholders
at the executive level, as well as external leaders, on overall strategy, policy
development, vendor selection, and executive sponsorship for informatics
initiatives.
- Health care policy influence
Informatics nurses advocate for ethical standards and principles dur-
ing policy development. They inform state and federal decision-makers on
legislation that impacts the direction of technology, data, information, and
communication solutions used by health care professionals, patients, fam-
ilies, consumers, and populations. Informatics nurses help communicate
the need for new advisory opinions, declaratory rulings, or position state-
ments to appropriate entities. They hold important roles in educating clin-
ical and technology leaders on proposed and final regulations to support
overall health care strategy and compliance activities.
- Scientific research and discovery
Informatics nurses conduct basic and applied research to improve the
design, implementation, and use of technology, data, information, and
communication solutions in health care delivery. They integrate current
evidence in the design, implementation, and evaluation of informatics
Nursing Informatics: Scope and Standards of Practice, 3rd Edition 9
Clinical judgement is the “observed outcome of critical thinking and
decision-making” (NCSBN, 2019, p. 1). Decision-making is the process of
choosing among alternatives and is dependent upon access to quality data.
Decision-making in health care is guided by critical thinking, an intellec-
tually disciplined process. The decisions are characterized by the out-
comes of the resulting actions. Clinicians, as knowledge workers, make
numerous decisions that influence the lives and well-being of individuals,
families, groups, communities, and populations.
For example, the nursing process includes assessment, diagnosis, out-
comes identification, planning, implementation, and evaluation and is
dependent upon quality data and critical thinking skills that are supported
by information and communication technologies. Effective clinical judge-
ment is dependent upon the ability of the nurse to use their nursing
knowledge to interpret available data and information. Properly designed
and implemented technologies optimize the nurse’s ability to collect,
categorize, and analyze data. This enables sharing of relevant information
between all members of the health care team, including the patient, to pro-
mote team collaboration and enhance the continuity of care. The infor-
matics nurse is ideally suited to help select, implement, and evaluate
technology that assists members of the care delivery team in reaching the
goal of positive patient safety and quality outcomes.
data, iNforMatioN, KNowledge, aNd wisdoM
In 1989, Graves & Corcoran expanded on the work of Blum (1986) to
describe the nursing informatics concepts of data, information, and knowl-
edge. Their seminal work provided a definition of nursing informatics
and an information management model that identified data, information,
and knowledge as key components of NI practice (Figure 2).
The three concepts as defined by Blum are:
- Data: Discrete entities that are described objectively without
interpretation.
- Information: Data that have been interpreted, organized, or
structured.
10 The Scope of Nursing Informatics Practice
Source: Graves & Corcoran (1989). Reprinted with permission of the publisher.
FIGURE 2 Conceptual Framework for the Study
of Nursing Knowledge
Management
processing
Data Information Knowledge
- Knowledge: Information synthesized so that relationships are
identified and formalized.
Data, information, and knowledge are of value to all health care provid-
ers across the continuum of care. Nelson expanded upon their original
model by adding the concept of wisdom (Figure 3). This model depicts how
data are transformed into information and information into knowledge,
with each level increasing in complexity and requiring greater application
of human intellect. The x-axis represents the nonlinear interactions and
interrelationships between the four concepts. The y-axis represents the
increasing complexity of the concepts, as well as the dynamic interactiv-
ity of the inter- and intra-environmental factors that influence the move-
ment across and within the data-to-wisdom continuum.
Wisdom, defined as the application of knowledge in the management
of human problems, consists of knowing when and how to apply knowl-
edge to deal with complex or specific human needs (Nelson & Joos, 1989;
Nelson & Staggers, 2016). Whereas knowledge focuses on what is known,
wisdom focuses on the appropriate application of that knowledge and
an appreciation of the consequences of selected actions. For example, a
knowledge base may include several options for managing an anxious
family, wisdom involves nursing judgment about which of these options
is most appropriate for a specific family and applying the selected option
in the delivery of nursing care.
12 The Scope of Nursing Informatics Practice
from a newborn, they mean one thing, but if obtained from an adult, they
have a very different meaning. The nurse’s knowledge of normal vital sign
values for different types of patients, and the condition of the patient from
whom the numbers were obtained, provide a context within which the
nurse can interpret the information. Then the nurse will know if the num-
bers represent a normal, expected result or an abnormal, even pathologi-
cal result. The numbers must be then placed in a context so that the nurse
can take appropriate clinical action, thereby demonstrating “knowledge-
in-use” or wisdom.
Benner (1982) defined the experiential stages of the nursing professional
and later contributed Thinking-in-Action as an approach to administra-
tion of care (Benner et al., 2011). The addition of wisdom raised new and
important research questions, challenging the profession to develop tools
and processes for classifying, measuring, and encoding wisdom as it
relates to nursing and informatics education.
wisdoM iN NursiNg
As nurses we seek to better understand how to gain nursing wisdom and
apply it in our daily practice. ANA added wisdom as a core nursing meta-
structure that is supported by nursing informatics and integrally connected
to nursing actions. Wisdom is the application or use of knowledge to solve
human problems (ANA, 2008). However, the concept and experience of
wisdom in nursing practice was not well defined; hence, Matney developed
the Theory of Wisdom-in Action (WIA) for Clinical Nursing (Matney, 2015).
As an emerging theory on nursing wisdom, the WIA for Clinical Nursing
represents the output of IN and INS contributions to patient care and rein-
forces the informatics specialty within the domain of nursing practice.
Matney created the theory in three phases. In phase one, a prelimi-
nary theory was developed deductively using derivation and synthesis,
based on theories and models from psychology, education, and nursing
(Matney, Avant, & Staggers, 2016). Pertinent concepts were identified,
and nursing-specific definitions were created. Next, a constructivist
grounded theory approach inductively captured the experience of wis-
dom in nursing practice, based on wisdom narratives from 30 emergency
Nursing Informatics: Scope and Standards of Practice, 3rd Edition 13
department nurses (Matney, Staggers, & Clark, 2016). Finally, the theories
were synthesized into the resultant Theory of Wisdom In Action (WIA) for
Clinical Nursing presented in Figure 4 (Matney, Avant, Clark, & Staggers,
The theory describes two antecedent dimensions: person-related and
setting-related factors and two types of wisdom processes. General wis-
dom processes apply to patient care and describe the actions nurses
take during a stressful or uncertain event. Personal wisdom develops
afterwards, as a feedback loop with reflection, discovery of meaning, and
learning, followed by increased knowledge and confidence.
The theory demonstrates how wise nurses make decisions in stressful
situations using an iterative process that includes applying knowledge
based on skilled clinical judgment. Implemented decisions produce con-
sequences, which, in turn, imitate reflection, discovery of meaning, and
learning. Finally, new information is integrated back, refining knowledge
and judgment when necessary.
The Theory of WIA expands well beyond what is encompassed by the
processing and transformation of data to information and synthesis of
information to uncover knowledge. Having knowledge supports decision-
making regarding the science of nursing, but those components are insuf-
ficient when it comes to the affective emotional processes that occur
simultaneously with technical processes during wisdom in action.
The theory can guide the IN and INS regarding the use of the types of
information accessed and knowledge needed. This will be of importance
to those who need to understand and articulate what is valuable informa-
tion within an electronic health record. The Theory of WIA provides
informatics nurses with a beginning understanding of the psychosocial
process of wisdom in nursing practice. It clearly articulates that some
aspects of wisdom are amenable to informatics methods and tools, whereas
other aspects are personal and reflective.
Wisdom is critical for all areas of nursing practice. The Theory of WIA
for Clinical Nursing provides a working framework for translating wisdom
in clinical nursing practice into theoretical and practical terms, depicting
Nursing Informatics: Scope and Standards of Practice, 3rd Edition 15
both the science and the art of nursing. This novel theory displays how
nurses’ practice incorporates wisdom and reveals that wisdom-in-action
requires clinical skills, experience, knowledge, and affective proficiency.
Value of iNtegratiNg NursiNg iNforMatics iNto practice
Data, information, knowledge, and wisdom are central to effective health
care delivery systems. Nurses are skilled in managing and communicat-
ing information and delivering quality care. Nursing informatics is also
concerned with the creation, structure, storage, delivery, exchange, interop-
erability, and reuse of nursing and clinical information along the contin-
uum of care. As electronic health information systems are integrated into
every nursing role and setting, the use of technology at the point of care
delivery; the external use of clinical information for quality, legal, and reg-
ulatory activities; and the use of analytics of data and metadata contribute
to the creation of new nursing knowledge. Such an evolution in the health
care environment and ubiquitous use of data, information, and knowledge
resources contribute to the blurring of the boundaries between the roles
of nurses, informatics nurses, and informatics nurse specialists.
NursiNg KNowledge represeNtatioN
“If we cannot name it, we cannot control it, practice it, finance it,
teach it, finance it, or put it into public policy” (Clark & Lang,
1992, p. 109).
Standardized nursing terminologies are essential to representing nursing
in the documentation of patient care and the continued evolution of the
nursing body of knowledge. Nursing leaders have developed many differ-
ent vocabularies and ways of organizing data, information, and knowledge
pertinent to nursing through numerous established research initiatives
that have spanned decades. In the early 1990s, ANA began to formally
recognize these languages, vocabularies, and terminologies as valuable
representations of nursing practice and to promote the integration of
standardized terminologies into information technology solutions. In
its 2018 position statement, ANA reaffirmed support for standardized
nursing terminologies: “The American Nurses Association continues to
16 The Scope of Nursing Informatics Practice
advocate for the use of the ANA recognized terminologies supporting
nursing practice within the Electronic Health Record (EHR) and other
health information technology solutions” (ANA, 2018).
Table 1 provides the ANA currently recognized standard nursing
terminologies.
The SNOMED-CT reference terminology includes clinical concepts
describing nursing care such as diagnosis, intervention, and outcome. Of
note, while nursing concepts are included in SNOMED-CT, “the concepts
were not developed exclusively for nursing” (Coenen et al., 2001). “Data in
healthcare systems must persist in content and meaning across organ-
izations and time to support direct patient care.. .” and “... the specifica-
tion of data has a direct relationship to patient safety and the effective re-use
of clinical data for knowledge acquisition” (Coenen et al., 2001). Maps
between the coded concepts from one code system to another are published
to support interoperability. This mapping from one terminology to another
enables data to be passed between systems. Despite the significant adop-
tion of “maps,” there is no normative guidance on how maps should be
used and no objective quality measurement for the terminology mappings
(Coenen et al., 2001).
TABLE 1 ANA- Recognized Standard Nursing Terminologies
Interface Terminologies Minimum Data Sets
Clinical Care Classification System (CCC)
International Classification for Nursing
Practice (ICNP)
NANDA
Nursing Interventions Classification
System (NIC)
Nursing Outcomes Classification (NOC)
Omaha System
Perioperative Nursing data Set (PNDS)
Alternative Medicine Billing and Coding
(ABC Codes)
Nursing Minimum Data Set (NMDS)
Nursing Management Minimum
Data Set (MMMDS)
Reference Terminologies
Logical Observation Identifiers
Names and Codes (LOINC)
Systemized Nomenclature of
Clinical Terms of Medicine—
Clinical Terms (SNOMED CT)
18 The Scope of Nursing Informatics Practice
Concepts and Tools from Information
Science and Computer Science
cliNical decisioN support aNd expert systeMs
Nurses’ decision-making is the cognitive evaluation of one or more factors
in relation to the delivery of nursing care. Proficiency in decision-making
is a requirement for the execution and delivery of nursing care to improve
the health of persons, families, groups, communities, and populations. An
expert or decision-support based system can augment the clinical decision-
making process through system evaluation of specific data points and
information such as abnormal lab results, active procedural or medication
orders, or clinical documentation.
With data and information, an expert system can generate care sug-
gestions or warnings, such as potential medication interactions that
might result in an adverse drug event, based on a predefined set of rules,
augmenting nurses’ existing knowledge. These care suggestions can take
the form of active alerts or passive reminders that promote safety and
improve quality of care and should be designed to include interventions
supported by evidence. Nurses must thoroughly evaluate system generated
data, information, and knowledge-based recommendations and employ
wisdom in the decision-making process.
Big data, data laKes, aNalytics
The NI practice environment is constantly advancing with the emergence
of new technologies and tools. With the advent of Big Data (Johnson, 2019)
and the move towards data lakes (described below), there is an expanded
capacity for greater precision in the IN and INS roles for analyzing and
querying data. The IN and INS systematically extract and analyze health
care data from these large or complex datasets to actively improve system
and care efficiencies and population outcomes. In documentation frame-
works, the IN and INS use Big Data by supporting the use of standardized
terminologies, ontologies, and classification systems mapped to nationally
accepted clinical nomenclatures (e.g., SNOMED CT®, LOINC). In doing so,
endorsement of research-based assessment scales and instruments encoded
with these nomenclatures allows interoperable nursing data reuse for
Nursing Informatics: Scope and Standards of Practice, 3rd Edition 19
comparative effectiveness research, quality metric implementation, and
knowledge generation (Harper & Sensmeier, 2015; HIMSS, 2015; Keenan,
Working with data requires the IN and INS to have knowledge of the
different data storage options and approaches to processing stored data.
Databases and data warehouses are characteristically relational databases
and position data in structured or modeled formats creating discrete and
unconnected “silos.” Data lakes are centralized repositories accommodat-
ing structured, semistructured, and unstructured data (O’Dowd, 2018;
Rouse, 2019; Watts, 2017). While each supports Big Data analytics, highly
structured data are less agile in configuration and reconfiguration for ana-
lytic models, queries, and machine learning applications to allow discov-
ery of patterns and relationships between the data (HIMSS, 2015; Watts,
Collaborating with or assuming the role of a data scientist, the IN and
INS engage with other stakeholders to prepare, manage, and examine data
to extract specific meaning based on the initial requirements of the
data warehouse. Configuration of the data warehouse is based on which
data type to include or exclude to accommodate the end user’s objectives.
For example, a query on pressure injury for a specific hospitalized patient
population occurring within a specified time frame would require patient
care data. The identified data elements extracted from the database for the
query would be represented in the report or data visualization dashboard
for analysis of cost and quality of care delivery.
Data scientists actively partner with the IN and INS to help scale
algorithms needed to extract meaningful information from data lakes.
A data lake accepts and retains all forms of data in an unstructured,
unorganized, and nonhierarchical configuration and uses cloud-based
distributed frameworks because of the magnitude of data volume (Marr,
2018; O’Dowd, 2019). The data scientist employs data analytic models
guided by the expertise of the IN and INS to verify the accuracy of data
requirements for and outputs from the analytic applications that use
artificial intelligence (AI), machine learning (ML), natural language
processing (NLP), and deep learning (DL). The primary goal is for the
Nursing Informatics: Scope and Standards of Practice, 3rd Edition 21
between AI and computational linguistics (Mulkar, 2016; Nadkarni et al.,
2001; Sarkar, 2018). Working from unstructured data, NLP rules and
analytical models extract semantic concepts or perform syntactic analy-
sis by converting text into machine-readable structured data (Jiang et
al., 2017; Nadkarni et al., 2011; Tutorialspoint, 2021). While NLP relies on
linguistics and the human use of language, ML procedures only utilize
algorithmic techniques and structured data and are often applied within
the NLP space. Machine learning techniques cluster patients’ traits and
outcomes targeting disease indicators for individual and population health
interventions (Jiang et al., 2017). The linear regression models applied to
ML form neural networks with many layers and provide the foundation
for DL (Jiang et al., 2017; Mulkar, 2016). Also used in NLP, DL focuses on
the quantitative interpretation within data sets and vision-based classifi-
cations primarily in medical imaging (Mulkar, 2016; DHHS, 2017).
forecastiNg aNd predictiVe aNalytics
Forecasting and predictive analytics can be applied in nursing practice
to improve relevance and performance of nursing interventions. Infor-
matics nurses collaborate with data scientists or directly engage in data
Adapted from T. Danner, 2020, aunalytics.com/artificial-intelligence-machine-learning-and
- deep-learning%E2%81%A0/
FIGURE 6 AI, NLP, ML and DL Operations.
Artificial Intelligence
Natural Language
Processing
Machine Learning
Deep Learning
- Rules
- If - Then - Else, Knowledge Bases
- Rules
- If - Then - Else, Knowledge Bases
- Linguistics
- Pattern Detection
- Regression
- Clustering
- Image Classification
- Machine Translation
22 The Scope of Nursing Informatics Practice
preprocessing to create machine learning algorithms used in health care
databases to promote knowledge discovery from direct patient care infor-
mation. By combining clinical nursing knowledge along with discovery of
unique patterns in patient data, evidence-based personalized and precise
interventions are created for each patient. Patient-centric algorithms enable
development of forecasting and predictive analytics that contribute to high
value care individualized for the patient to forecast future health patterns.
This can help prevent advancement into high-risk groups requiring expen-
sive and suboptimal interventions.
user experieNce aNd related coNcepts
What is the primary goal of informatics nurses and what do they ultimately
seek to accomplish? Answers to these questions are impor tant and help
define what makes nursing informatics practice unique and how it creates
demonstrable value.
The fundamental answer involves creating an environment of technol-
ogy and data systems that enable those in that environment to successfully
accomplish their desired goal(s). Members of the environment are referred
to as a user, “the person who interacts with the system, product or ser vice”
and includes nurses, patients, consumers, and others (International Organ-
ization for Standardization, 2018a).
The strategy of informatics nurses when creating and maintaining this
environment is to make all aspects of user interactions with technology
and data both easy to use and beneficial. As such, informatics nurses are
the barons of what is referred to as “user experience.” The International
Organization for Standardization (ISO) defines user experience as the
“user’s perceptions and responses that result from the use and/or antici-
pated use of a system, product or ser vice” (International Organization for
Standardization, 2018b).
Note the definition provides additional information:
- Concepts of user perceptions and responses are described as
“the users’ emotions, beliefs, preferences, perceptions, comfort,
