Chapter 17

THEORIES, FRAMEWORKS, AND MODELS
Nursing Informatics models is composed of 5 general models. 1st, Graves and Corcoran's model. 2nd, Schiwirian's model. 3rd, Turley's model. 4th, Data Information Knowledge (D-I-K) model. And the last is Benner's Novice to Expert model. The 2 specific informatics models are Philippine Health Ecosystem model and Shift Left model.


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1st, Graves and Corcoran's model.

 2nd, Schiwirian's model.

 3rd, Turley's model.
 4th, Data Information Knowledge (D-I-K) model.
 And the last is Benner's Novice to Expert model.

 The 2 specific informatics models are Philippine Health Ecosystem model and Shift Left model

According to GRAVES AND CORCORAN’S MODEL (1989) that nursing informatics as the linear progression, from data into information and knowledge. Management processing is integrated within each elements, depicting nursing informatics as the proper management of knowledge, from data as it is converted into information and knowledge.


According to SCHIWIRIAN’S MODEL (1986), nursing informatics involves identification of information needs, resolution of the needs, and attainment of nursing goals/objectives. Patricia Schwirian proposed a model intended to stimulate and guide systematic research in nursing informatics, model/framework that enables identification of significant information needs, that can foster research (somewhat similar to Maslow’s hierarchy of needs).


According to TURLEY’S MODEL (1996), nursing informatics is the intersection between the discipline-specific science (nursing) and the area of informatics. And in this model, there are 3 core components of informatics, namely Cognitive science, Information science, and Computer science.


In the DATA-INFORMATION-KNOWLEDGE MODEL, NI is a specialty that integrates nursing science, computer science and  information science to manage and communicate data, information, knowledge and wisdom into nursing practice (ANA). Nursing informatics is an evolving, dynamic process involving the conversion of data into information, and subsequently knowledge.

BENNER’S LEVEL OF EXPERTISE MODEL said that ževery nurse must be able to continuously exhibit the capability to acquire skills (in this case, computer literacy skills parallel with nursing knowledge), and then demonstrate specific skills beginning with the very first student experience. According BERNER, there are 5 levels of expertise:

·                     žNovice – individuals with no experience of situations and related content in those situations where they are expected to perform tasks.
·                     Advanced Beginner – marginally demonstrate acceptable performance having built on lessons learned in their expanding experience base; needs supervision.
·                     Competent – enhanced mastery and the ability to cope with and manage many contingencies.
·                     Proficient – evolution through continuous practice of skills, combined with professional experience and knowledge; individual who appreciates standards of practice as they apply in nursing informatics.
·                     žExpert – individual with mastery of the concept and capacity to intuitively understand the situation and immediately target the problem with minimal effort or problem solving.
According to PHILIPPINE HEALTH CARE ECOSYSTEM, nursing informatics is a huge network that encompasses all the sectors of the health care delivery system – government agencies, health care facilities, practitioners, insurance companies, pharmaceutical companies, academic institutions, and suppliers. And žthe government, different nursing associations and developmental agencies maintain and balance the network.
INTEL’S SHIFT LEFT MODEL:

·  
                Patient care shifts/progresses from a high quality delivery of lift through technology with increased costs (right side) into quality of life with minimal health costs.
·                     Inverse relationship between quality of life and cost of care/day.
PATIENT MEDICAL RECORD INFORMATION MODEL (PMRI): BASIS OF EHR

·                 žThe type and pattern of documentation in the patient record will be dependent on 3 interacting dimensions of health care:
·                 Personal Health Dimension - personal health record maintained and controlled by the individual or family; nonclinical information.
·                 Health Care Provider Dimension - promotes quality patient care, access to complete accurate patient data 24/7.
·                 Population Health Dimension - information on the health of the population and the influences to health; helps stakeholders identify and track health threats, assess population health, create and monitor programs and services, and conduct research.
ABC CODES:

·                       mechanism for coding integrative health interventions by clinician for administrative billing and insurance claims.
·                       includes complementary and alternative medicine interventions and codes that map all NIC, CCC, and Omaha system interventions.
Perioperative Nursing Data Set (PNDS):
·                       universal language for perioperative nursing practice and education; standerdize documentation of perioperative data in all perioperative settings.
·                       diagnosis based on NANDA, interventions based on NIC, and outcomes based on NOC.
SNOMED  ACT:

·                        it is a core clinical terminology containing over 357,000 healthcare concepts with unique meanings and formal logic-based definitions organized into multiple hierarchies.
As of January 2004,the fully populated table with unique  descriptions for each concept contains more than 957,000 descriptions.
The July 2004 release contained HHCC version 2,NANDA taxonomy ll, NIC version 4,NOC version 3,PNDS version 2,and Omaha system {1992}.

In 2002,the national library of medicine negotiated a long term contract to place SNOMED ACT in the public domain for low cost licensing through the National Library of Medicine .its available in English,Spanish, and German language editions.

Conceptual Understanding of Nursing Informatics

The seminal paper by Graves and Corcoran in 1989 set the stage for conceptualizing the specialty of nursing informatics. They defined nursing informatics as "a combination of computer science, information science and nursing science designed to assist in the management and processing of nursing data, information and knowledge to support the practice of nursing and the delivery of care".

 Here they draw attention to the information of nursing, elaborating data, information, and knowledge as the foundational components for collection, processing, or manipulation by technological tools. Although there are different perspectives on the relations between data, information, and knowledge (i.e., portraying linear, progressing complexity or as mutually constituted, accumulated elements), the components can be understood as follows.

 Data is "raw facts," that is, the basic elements (atomic structure) or uninterpreted building blocks for composition to information or knowledge. Information is combined or structured data interpreted in different contexts or situations. Knowledge is data or information synthesized by formulas, heuristic strategies, or formally delineated relationships . These interrelated elements of data, information, and knowledge point out important attributes of evidence in nursing, but they also differentiate foci within nursing informatics.

The establishment of comprehensive nursing vocabularies with interface and reference properties, e.g., enumerative classification systems, combinatorial terminologies, or reference terminologies is an example of the important efforts to articulate, represent, and sort nursing data . Combining data into a structure gives the meaning and value of these raw facts and basic expressions. Therefore, the structuring of nursing documentation and the developing of care protocols, care maps, or clinical pathways are examples of organizing, filtering, interpreting, and clarifying nursing information

. Examples of efforts to outline nursing knowledge include modeling the expert nurse's decision-making to provide computerized decision support to enhance clinical judgment or elaborating on the knowledge resources at play to construct standards, common guidelines, and procedures . Development of and access to information and knowledge are core requisites for supporting experiential and evidence-based decision-making, continuity of care, and patient safety. In sum, considerable achievements have been gained in terms of expressions for nursing assessment, nursing judgments, nursing interventions, and patient outcomes that are sensitive to nursing care.

 Substantial resources in informatics are currently committed to establishing comprehensive and formalized terminologies to represent clinical practice and standards for information exchange. This allows relationships among various types of data to be examined or transformed through more automated processes. This examination and transformation is surrounded with unresolved challenges due to the situated nature of patient care, the variation in personal health needs, and evolving opportunities resulting from new achievements and new tools.

Over time, scholars have suggested, discussed, and added functional elements that contribute to the collective effort to elaborate and expand the understanding, concepts, tools, and structures available to nursing informatics and nursing. These ideas include enhancing the patient's role, emphasizing the nurse's role as information integrator, developing sound collaborative relationships between providers or between providers, technologies, and specific contexts of decision-making, and including true interdisciplinary issues for patient safety and evidence-based practice .

 A significant addition to the conceptual understanding of nursing informatics came more recently when Matney et al.  suggested the inclusion of wisdom, complementing the foundational concepts of data, information, and knowledge. Wisdom is understood as the "appropriate use of knowledge to manage and solve human problems" . This is of great significance, important in understanding the variations in clinical processes in sufficient detail and in acknowledging previous experiences. In reality, Matney et al.  challenge us to acknowledge the situatedness embedded in nurses work, and to embrace accumulated clinical expertise. These are resources for skillful, competent clinical judgment, and they involve the contextual and intertwined nature of data, information, and knowledge.

2. Application and Use of Informatics in Health Care

Achievements in nursing informatics have inspired the systematization and formalization of information where data, structured information, and articulated knowledge are important building blocks for the development of IT-based applications. Most attention and in-depth study has been directed to describing information used in clinicians' judgments and modeling prototype applications for management that are decision-supportive. The studies also strive to provide the best evidence at the point of care to improve decision-making. So far, feasible practice models giving new tools for information handling have received little attention, and reported studies are often examples of workflow failure  or unintended consequences .

When we zoom in on applications using achievements in health informatics, there is also a plethora of tools, devices, and applications aiming to support the management of data, information, and knowledge related to health and wellbeing. The following types of applications add new opportunities and challenges to the field:

EHR is a repository maintained by health facilities, with a set of functionalities and services, that accumulates the health providers' assessments, actions, and evaluations of a person's clinical problems as separate episodes or over the life trajectory.

PHR is a repository maintained by the consumer, with a set of personal observations, information from health providers, and relevant information resources. The scope and comprehensiveness of the accumulating content in a PHR depends on the individual's effort.

Smart assistive tools include stand-alone applications and tools for personal self-care activities for everyday, everywhere use. Passive, environmental monitoring by sensors also supports the individual's personal health information management and safety.

These types of applications present novel opportunities and important challenges to our field, but their interrelationships and how together they may contribute to efficient information are not fully understood

The Problem-Oriented Medical Record  was among the first influential published implementation experiences where informatics supported health information management and health care processes. The core idea was to embed an information model that de facto restructured clinical documentation in the EHR, using a problem-oriented approach in an attempt to reengineer documentation processes.

 The systematization of information elements, charted according to predefined templates or as narrative texts, most often follows the problem-oriented information model rather than models that deal with body systems, basic needs, or functional status, to represent nursing practice in an EHR. The "stickiness" of the problem-oriented structure for information models is seen globally, reflected as the information model in the globally diffused nursing process, suggesting structures for clinical documentation of nursing care .

 The same information model is seen as a dimension of nurses' work currently supported by emerging nursing vocabularies  and as the commonly used structure for health care knowledge representations, as a resource for the design, implementation, and evaluation of health information systems, and as an inspiration for practice changes following the deployment of health information systems

Core features of nurses' work are teamwork, collaboration, and mobility. Nurses meet and interact with patients and their families in a variety of settings to contribute to prevention, early intervention, maintenance, or problem solving. Nurses typically care for many patients, and in a team, they assume key roles in coordinating patient care and treatment in collaboration with other providers .

 Nurses collect a lot of data, and they access evidence at the nurses' station, at the bedside, in a person's home, or in an office. Incorporating contextual and environmental cues is crucial. These elements exemplify specific features of nurses' work and of their workflow. Lack of appropriate access to evidence at the point of need is a challenge for continuity of care and patient safety. Lack of aggregate information for reporting and benchmarking has so far been detrimental to nurses' adoption of new technological tools.

Professional collaboration can be supported by access to and/or contribution to the accumulating body of a person's specific health information in the EHRs. The EHR offers an interesting collection of quantitative data (e.g., vital signs or lab values), qualitative data (e.g., patient narratives or evaluation of care efforts), and transactional data (e.g., performed tests or delivered medications)

. More recent analytic techniques and approaches to the analysis of large amounts of data, or "Big Data," open up new ways of providing evidence to support knowledge development, decision-making, and interdisciplinary collaboration. Using information in the EHR means that the aggregation of authentic, real-time patient data offers opportunities for early intervention to prevent health problems or to manage existing conditions . There are still unresolved issues of a professional, legal, and ethical nature with the new approaches to knowledge generation and to more information sharing within and across settings and levels of care .

As more health care activities migrate from hospitals to community care, the plethora of tools and functionalities follows. More work is needed to understand how better to integrate this information into the existing informatics infrastructure and into clinicians' suit of tools. Exciting opportunities for active collaboration with the patient and his or her family can be further developed through skillful use of EHRs in combination with the PHR

. A PHR contains information chosen, collected, and maintained primarily by the patient or a trusted family member. We know that people make tradeoffs between types of health information and health concerns as they use sophisticated and robust health information management strategies [30]. Therefore, information kept in the PHR will vary with the health concern and interest of the person keeping it.

A PHR can be a stand-alone application set up and maintained at the discretion of the person keeping it. More common is some connection to organized health care services. Depending on institutional and organizational arrangements and provision of services, persons using such a PHR can access parts of their EHR securely and remotely, some can take advantage of secure mail for online interactions with the health providers as well for accessing relevant health information [20,31]. In addition, the PHR carries the potential to be an even more used and valued resource for accumulating health information and supporting health information management as care activities increasingly shift to focus on prevention and early intervention and actively include the extensive self-care efforts of the citizens themselves.

 As such, a PHR can be an important resource for nurses if the recipients of nursing care choose to grant access to their caregivers. To be even more valuable for multiple users and uses, practical ways of capitalizing on benefits from the explication, systematization, and formalization of data, information, and knowledge can help us move forward. We believe that attention to wisdom and acknowledgment of the situational dimensions and specifics in nurses' interactions with their patients will open up new beneficial, collaborative relationships and further achievements.

New areas of application of health informatics can be found in the use of the growing suite of tools for ambient assisted living and smart houses and in the new potentialities for generating practice-based evidence using "Big Data" analytic techniques. New tools are increasingly used for personal monitoring and self-care activities, and we see expanding opportunities in applications labeled under the umbrella of mHealth or uHealth. These applications range from support for collecting personal observations, e.g., vital signs and observations of daily living  to support in collecting data from passive, environmental monitoring, e.g., use of space  or global positioning system for outside positioning , to the use of available tools to maintain independent living, e.g., tablets and smartphones for information access and new modes of participation .

These are novel opportunities to support the individual's personal health information management and safety requirements to allow him or her to remain at the preferred home dwelling as long as possible. Unfortunately, many of these tools "live a life of their own," that is, they are not well integrated to existing health informatics suites of tools . Applying techniques developed for "Big Data" to accumulate clinical information from different sources and in a variety of formats provides opportunities for the active use of authentic, real-time patient data in developing health care and practice changes that embrace new and novel opportunities for analysis of clinical information.

 Among the future challenges are questions of how to incorporate these new opportunities to present sound, innovative care repertoires. It remains to be explored how such analytic techniques, applied to the plethora of available information, can add to knowledge and wisdom, and what future innovative practice models will be suggested. More research will also be appreciated to understand how to incorporate data from remote monitors in decision-making and clinical judgments without familiar, observational data to help in the interpretation of the discrete data.

The Data-Information-Knowledge-Wisdom framework

Nursing informatics was created by the merge of three well established scientific fields: Information science, Computer science and Nursing science. One of the most compelling definitions of the discipline states: “Nursing informatics science and practice integrates nursing, its information and knowledge and their management with information and communication technologies to promote the health of people, families and communities worldwide” (International Medical Informatics Association – Nursing Working Group, 2010). Unfortunately, very few attempts were made to generate a broad theoretical framework for nursing informatics.

 There are several challenges to generate such framework. First, the interdisciplinary nature of nursing informatics demands the use of broad enough theoretical framework to encompass all the disciplines. Also, the required theoretical framework should consider the practice/application domain; the implementation of nursing informatics in real healthcare settings. Recently, it was suggested that the Data-Information-Knowledge-Wisdom (DIKW) framework has a high potential to address these challenges and this framework was adopted by the American Nurses Association (American Nurses Association, 2008; Matney, Brewster, Sward, Cloyes, & Staggers, 2011).

Historically, the development of the DIKW framework was urged by a search for a new theoretical model explaining the emerging field of Nursing Informatics in 1980-90s. In their seminal work, Graves and Corcoran (1989) defined that data, information, and knowledge are fundamental concepts for the discipline. Their framework was widely accepted by the international nursing community (Matney et al., 2011; McGonigle & Mastrian

, 2011). In 2008, the American Nurses Association revised the Scope and Standards for nursing informatics to include an additional concept, wisdom (American Nurses Association, 2008). Recently, Matney and colleagues (2011) have expanded on the components of the DIKW framework:

Data: are the smallest components of the DIKW framework. They are commonly presented as discrete facts; product of observation with little interpretation (Matney et al., 2011). These are the discrete factors describing the patient or his/her environment. Examples include patient’s medical diagnosis (e.g. International Statistical Classification of Diseases (ICD-9) diagnosis #428.0: Congestive heart failure, unspecified) or living status (e.g. living alone; living with family; living in a retirement community; etc.). A single piece of data, datum, often has little meaning in isolation.

Information: might be thought of as “data + meaning” (Matney et al., 2011). Information is often constructed by combining different data points into a meaningful picture, given certain context. Information is a continuum of progressively developing and clustered data; it answers questions such as “who”, “what”, “where”, and “when”. For example, a combination of patient’s ICD-9 diagnosis #428.0 “Congestive heart failure, unspecified” and living status “living alone” has a certain meaning in a context of an older adult.

Knowledge: is information that has been synthesized so that relations and interactions are defined and formalized; it is build of meaningful information constructed of discrete data points (Matney et al., 2011). Knowledge is often affected by assumptions and central theories of a scientific discipline and is derived by discovering patterns of relationships between different clusters of information. Knowledge answers questions of “why” or “how”.

For healthcare professionals, the combination of different information clusters, such as the ICD-9 diagnosis #428.0 “Congestive heart failure, unspecified” + living status “living alone” with an additional information that an older man (78 years old) was just discharged from hospital to home with a complicated new medication regimen (e.g. blood thinners) might indicate that this person is at a high risk for drug-related adverse effects (e.g. bleeding).

Wisdom: is an appropriate use of knowledge to manage and solve human problems (American Nurses Association, 2008; Matney et al., 2011). Wisdom implies a form of ethics, or knowing why certain things or procedures should or should not be implemented in healthcare practice. In nursing, wisdom guides the nurse in recognizing the situation at hand based on patients’ values, nurse’s experience, and healthcare knowledge

. Combining all these components, the nurse decides on a nursing intervention or action. Benner (2000) presents wisdom as a clinical judgment integrating intuition, emotions and the senses. Using the previous examples, wisdom will be displayed when the homecare nurse will consider prioritizing the elderly heart failure patient using blood thinners for an immediate intervention, such as a first nursing visit within the first hours of discharge from hospital to assure appropriate use of medications.

The boundaries of the DIKW framework components are not strict; rather, they are interrelated and there is a “constant flux” between the framework parts. Simply put, data is used to generate information and knowledge while the derived new knowledge coupled with wisdom, might trigger assessment of new data elements (Matney et al., 2011).

Applying the Data-Information-Knowledge-Wisdom framework to guide informatics research

The DIKW framework does not propose any relations between the distinct data elements that lead to the generation of meaningful information and knowledge. To accomplish that, a discipline specific theory is required in combination with the DIKW framework. To illustrate that, I will use a practical example from my dissertation focusing on identifying patients’ risk for poor outcomes during transition from hospital to homecare.

The wisdom component of the DIKW framework is often addressed by the clinicians in the field. For example, the final product of my dissertation will be a decision support tool helping homecare clinicians with identification of patients’ risk for poor outcomes. When using the tool in practice, the clinicians will have to act according to a specific knowledge present in each clinical situation (e.g. ethics, clinical practice regulations in each particular state in the US etc.). In other words, the clinicians will use their wisdom to interpret suggestions and make clinical judgments using information received from the decision support tool. Figure I presents the possible interplay between the discipline specific theory (Transitions theory) and different components of the DIKW framework.

Figure I: Combining the discipline specific and DIKW theoretical frameworks

Data-Information-Knowledge-Wisdom

In summary, this editorial presents a possible theoretical blueprint for nursing and healthcare informatics researchers that intend to use the DIKW framework. The combination of discipline specific theories and the DIKW framework offers a useful tool to examine the theoretical aspects and guide the practical application of informatics research