Nursing Informatics: Concepts, Theoretical Models, and Frameworks

Introduction

  • The complexity and responsibility of nursing practice today requires long-term and ongoing career development.

  • Understanding is needed for the differences between experienced nurses and novice nurses.

Intended Learning Outcomes

  • By the end of this unit, students will be able to:

    • Describe the theoretical models in nursing informatics.

    • Explain distinguishing features of these models.

    • Describe the levels of Novice to Expert theory.

Theoretical Model

  • The Novice to Expert Theory is a construct theory first proposed by Hubert and Stuart Dreyfus in 1980, known as the Dreyfus Model of Skill Acquisition.

  • This theory was later applied and modified for nursing by Patricia Benner in 1984, providing a useful framework relevant to nursing informatics.

Application of the Theory

  • The Novice to Expert theory can be applied in various domains within nursing informatics:

    • Development of nursing informatics skills and competencies for nursing informatics specialists.

    • Development of technological system competencies for practicing nurses in institutions.

    • Education of nursing students from their first year until graduation.

    • Transition from graduate nurse to expert nurse.

Levels of Development in Novice to Expert Theory

  • Benner (1984) illustrated five levels of development, which progress upward from:

    1. Novice: Starting point for individuals with no prior knowledge; requires memorization of context-free aspects.

    2. Advanced Beginner: Dependent on rules but begins to notice additional elements relevant to real-life situations.

    3. Competent: Grasps relevant rules and facts, and can apply personal judgment for the first time to individual cases.

    4. Proficient: Learners transition from analyzing situations in a step-by-step manner to perceiving the scenario holistically.

    5. Expert: Possesses an extensive repertoire of experiences that allow for immediate intuitive decision-making in specific situations.

Distinguishing Features

  • Dreyfus and Benner estimated a time frame of approximately five years to transition through the five stages from novice to expert.

  • It is noted that not all novices will become experts; some individuals may find themselves stagnating at the competent or proficient stages.

  • Two defining personal characteristics that aid in progressing to the expert level are:

    • Deliberate practice.

    • Willingness to take risks and transcend the norm.

Conclusion of Novice to Expert Theory

  • The Novice to Expert theory serves as a guideline for clinical practices within nursing careers.

Online Discussion and Assignment

  • Question: Describe the Novice to Expert theory and its distinguishing features.

Relationship Between Data, Information, and Knowledge

Introduction

  • Data, information, and knowledge are core components of nursing informatics, crucial for understanding clinical information systems and their health care impacts.

Learning Objective

  • By the end of this unit, students will be able to:

    • Discuss the definitions of data, information, and knowledge in relation to nursing care.

    • Describe the relationships among data, information, and knowledge.

Definitions and Framework

  • In 1986, Blum introduced the concepts of data, information, and knowledge as a framework for understanding clinical information systems.

  • Blum classified clinical information systems according to three types of objects they process:

    1. Data: Discrete, objective entities without interpretation.

    2. Information: Data that has been interpreted, organized, or structured to provide context.

    3. Knowledge: Information that has been synthesized, recognizing relationships among elements.

Importance to Nursing Practice

  • Data translates into information and information into knowledge, with increasing complexity demanding greater intellectual application.

  • All nursing practice areas are concerned with these elements.

  • Example of data: Direct care vital signs including heart rate, respiration, temperature, and blood pressure.

Detailed Explanation of Concepts

  • Data: For example, an individual’s vital signs can be viewed as a set of data.

  • Information: When these data points are organized (e.g., height, weight, age, and gender), they can be processed to calculate Body Mass Index (BMI) which indicates an individual's weight status (underweight, overweight, normal weight, or obese).

  • Knowledge: Involves understanding the interrelationships among data elements; for instance, recognizing that a calculated BMI over 30 does not always indicate obesity, illustrating an advanced understanding of health based on synthesized knowledge.

Additional Examples

  • An instance of vital signs is just data. When a continuous set of vital signs across a timeframe is considered, it becomes information for longitudinal comparisons.

  • For example, recognizing patterns in vital sign changes (e.g., dropping blood pressure, increased heart rate, respiratory rate, fever) in a catheterized elderly patient can reflect abnormal conditions (potential sepsis), which illustrates knowledge.

Discussion and Assignment

  • Question: Explain each of the following in relation to Nursing Informatics: Data, Information, and Knowledge.