Nelson Data Management 12 Textbook Pdf

[Mariko Bloomgren]

TheData, Information, Knowledge and Wisdom Model (Nelson D-W) depicting the megastructures and concepts underlying the practice of nursing informatics was included for the first time in the 2008 American Nurses Association (ANA) Scope and Standards of Practice for Nursing Informatics (ANA, 2018). The date of this publication was almost 20 years after the first version of the model had been published. In 1989, a colleague and I wrote a brief article defining the concepts of data, information, knowledge, and wisdom. (Nelson & Joos, 1989 Fall). At that time, the three concepts of data, information, and knowledge were well established in the field of information science and had been introduced in the emerging discipline of medical informatics. However, adding the concept of wisdom to these three concepts and defining how wisdom was related to the established concepts was new. Part 1 of this two-part Informatics Column will focus on the addition of wisdom to the model. Part 2 will explore how the model has changed in the almost 30 years since this first brief article.

The second driving force came from my experience as a university faculty member in a clinical setting. When I returned in the fall 1988 to my faculty position, this literature and these concepts building the foundation of nursing informatics were well engraved in my thinking.

One of my primary responsibilities as a faculty member was teaching medical surgical nursing to senior nursing students in the classroom and the clinical setting. The clinical setting was in a major medical center. The nursing staff in this unit were excellent and provided outstanding role models for students. The patients were acutely ill with major medical problems. In other words, it was an ideal setting for teaching senior nursing students.

During this fall term one of the patients on this clinical unit had a major impact on my thinking about the concepts of data information and knowledge. The patient was a young woman who had delivered her first child and had been immediately transferred to the medical center with a variety of serious medical problems and related symptoms including high volume congestive heart failure, 4 plus edema and pulmonary effusion. One year earlier she had been fully heathy and planning her first pregnancy. Shortly after admission to our unit she was diagnosed with a terminal illness. Caring for such a patient is always a heart wrenching challenge.

The students and I worked closely with the staff in providing quality care for this patient, but one thing was obvious to me from the first day. Experienced staff seems to be intuitive in how to provide both physical and emotional care. They knew what to say and what to leave unsaid. They knew when to move forward and finish a difficult procedure such as deep suctioning and when to stop and let the patient rest. They were comfortable and confident in this role as caregiver. Students on the other hand were very uncomfortable. While they were dedicated in learning their role they were also very afraid of saying or doing the wrong thing. Before walking in the room, I would often see a student take a deep breath.

When the concept of wisdom was first proposed several experts in the field questioned whether the concept belonged in the model depicting the conceptual framework for nursing informatics. For example, the 2001 edition of the ANA Nursing Informatics: Standards and Scope of Practice included the following statement:

After the Graves and Corcoran (1989) article, others proposed adding the concept of wisdom to the triad of data, information, and knowledge (Nelson and Joos, 1989). Wisdom may be defined as the appropriate use of data, information, and knowledge in making decisions and implementing nursing actions. It includes the ability to integrate data, information, and knowledge with professional values when managing specific human problems.

Some nursing informatics (NI) experts believe strongly that wisdom is the purview of humans and cannot or should not be considered as a function within technology. Others believe that informatics solutions consistent with professional values and useful to expert nurses will require the incorporation of wisdom. This controversy makes the inclusion of wisdom into the triad of data, information, and knowledge currently an unresolved issue within NI. (American Nurses Association, 2001, p. 130)

In the figure, an information system processes data to produce information. A decision support system is defined as an automated system that can support a decision maker in the process of decision making by providing data and information. An expert system goes one step farther and actually uses data and information to make a decision. A common example of an expert system in operation can be seen if one has ever opened a new credit card account while checking out of a store. In a few minutes an automated system makes a decision rather or not to offer credit. Historically these types of decisions were made by human beings based on information included in an application for a credit card as well as other sources of data. The judgement of credit worthiness of the customer depended on how a person interpreted that information and data. Today in many cases the decision concerning the creditworthiness of a customer has been automated.

The first model, Figure 1, failed to clearly demonstrate the overlapping interrelationship between the concepts used in the model and the levels of technology as classified within the Figure. In response to this reality, Figure 2 was developed showing the overlapping interrelationships.

While the new model does a better job of showing overlapping relationships there have been problems how this model is understood. There has been as least one textbook published that modified this figure and described their modification as the Nelson D-W model. I have been assured this error will be corrected with the second printing of that book. However, as a result of this error, I have been contacted by two doctoral students to date who are planning to use the Nelson D-W model in their doctoral dissertation research and have been confused by this error. Figure 2 as depicted here is used to illustrate how the concepts in the Nelson D-W model might interact with the various levels of information technology. It does not illustrate the relationships and interrelationships with the actual model. The evolution of the model as well as the relationships and interrelationships will be further explored in part 2 of this series.

One must always remember that the capabilities of computers (which are always changing) do not define the scope of practice for nursing informatics; rather, it is how nurses use these tools that define their practice. In turn, if we do not understand how nurses at all levels of knowledge and experience use computers to support their practice, we cannot design automated systems that truly support the goals of nursing.

The Data, Information, Knowledge and Wisdom Model (DIKW Model) provides a theoretical framework for defining the scope of practice for nursing informatics. In Part 1 of this two-part column, the driving forces for the creation of the DIKW Model were explained. Here in Part 2, the development of the model, and the evolution of the figures used to illustrate the model, are described. The figure used in this column, illustrating the model is still a 'work in process.' This column concludes by discussing limitations of the current figure and encouraging future leaders to develop a figure that more fully and effectively articulates the rich and complex relationships within the model, and in turn the scope of practice for nursing informatics.

Regarding the history of the DIKW Model and Related Figures, the initial attempt to articulate the concepts within the DIKW Model was a brief article defining the concepts of Data-Information-Knowledge-Wisdom (Nelson & Joos, 1989). This publication did not include a model, but rather a table briefly explaining the concepts (See Table). The third column of the Table also identified the relationship between the concepts of data, information, knowledge, and wisdom and the various levels of computer systems.

Over the next decade subsequent publications, for example, (Joos, et al., 1996; Nelson, 2001) expanded on the four concepts, but did not create a figure showing the relationships between the concepts.

The first figure (See Figure 1) illustrating the model focused on the overlapping relationships between the concepts of data, information, knowledge and wisdom. The overlapping nature of the relationships between these concepts was depicted in the overlapping circles and in the words used to define the concepts. For example, the word organizing is included with both data and with information. This first version of the model (Nelson, 2002) was published by Mosby, in a health informatics textbook (Englebardt & Nelson, 2002).

At the time this textbook was written, Mosby was owned by Harcourt Brace & Company. In 2001, Reed Elsevier acquired Harcourt Brace & Company and Mosby then joined Elsevier. During this period, I was a member of the American Nurses Association (ANA) task force that had been charged with revising the 2002 ANA Scope and Standards of Practice for Nursing Informatics. In that role, I requested and received permission from Elsevier/Mosby for the ANA to print a modified version of Figure 1 that was subsequently titled Figure 2. Figure 2, as printed in this column, is a copy of that figure as it was printed in the 2002 ANA Scope and Standards for Nursing Informatics (ANA, 2002), including the permission that was used in that publication..

This second figure was an improvement on the first figure in that the interactions between and across the concepts was more effectively articulated. The double-headed arrow moving through each of the concepts signified that the flow of interactions moved throughout the continuum from data to wisdom. The oval with a dashed line labeled constant flux illustrated that this is an open system with constant interaction between the internal and external environment. In other words, context is important when using the model to understand specific phenomenon.

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