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Nursing Informatics & Technology, Lecture notes of Nursing

Nursing Informatics & Technology

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2023/2024

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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 con temporary 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 Acad emy 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 impor tant 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
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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,