Data from Novice to Expert: Aligning Benner with DIKW

Introduction

The use of health data has been at the core of the nursing process since Florence Nightingale's time. Historically, data utilization in practice has been relatively stable and constant. However, the rate of change for the increasing awareness of data dependency for clinical decision-making has increased exponentially over the last decade. As the healthcare industry continues to evolve rapidly, the consumption and utilization of data require nurses to be competent in data literacy, which is critical for delivering safe, high-quality patient care and improved patient outcomes. To meet these needs and for nurses to remain relevant in practice, understanding Dr. Patricia Benner's from Novice to Expert nursing theory, aligned with the informatics Data, Information, Knowledge, Wisdom (DIKW) Model, serves as a springboard to help articulate the relationship between data concepts and the data competency levels of clinicians.

Data Literacy and Nursing Practice

Healthcare technologies continue to evolve and become increasingly engrained within nursing practice and clinical workflows.  Cognitive computing models and generative artificial intelligence are just two examples of technology currently being introduced into nursing workflow. For these technologies to be successfully adopted, data literacy and competency are essential to nursing practice and foundational nurse competency. Aligning Benner's theory from novice to expert nursing theory with the DIWK informatics framework is a logical approach to present the relationship between data use and its correlation and essentialness to nursing practice (see Figure 1). Here, we provide an overview of how data is consumed within the electronic health record harnessing the DIKW model and how this relates to the data proficiency of the nurses using the Novice to Expert model's cycle. Introducing these concepts will bridge the gap between data literacy and nursing practice.   

From Novice to Expert, Benner's theory is well-established and has been integrated throughout nursing care delivery for years (Mortimore, Reynolds, Froman, Brannigan, & Mitchell, 2021). This theory is composed of 5 levels or stages of development: novice, advanced beginner, competent, proficient, and expert. Benner's approach is broad, applied frequently across various clinical scenarios, and recognizes that nursing development is not constant but cyclic. As nurses change roles, jobs, or specialties, they may regress and repeat the Novice to Expert lifecycle. Today, this model expands beyond the limits of nurse career changes and advancements and is a beneficial framework for learning about collecting and using healthcare data.

The nursing informatics specialty has widely adopted the DIKW model to help define its scope and standard of practice. In this framework, data is the simplest element, is uninterpreted and has no meaning without context. Information is a collection of related data elements that can be interpreted and provide meaning (Nelson, 2018). Knowledge is built on the formalization of the relationships and interrelationships between data and information (Nelson, 2018). Knowledge allows for the actual understanding, interpretation, and integration of the data and information. Wisdom is the most complex element of the model, enabling the application to solve problems using “knowledge, experience, understanding, common sense, and insight” (Kaminski, 2021). As data utilization continues to increase, the practical application of the DIKW model has a vital role within nursing.

Figure 1. DIKW pyramid aligned to Benner's Novice to Expert Theory. Adapted from models developed by Benner (1982) and Cannas et al. (2019).

The table below helps to visualize the alignment between Benner’s Novice to Expert Model to the DIKW model. The table provides practical examples of how data is consumed and utilized throughout the various stages of Benner’s model and how it correlates to the DIKW Model. 

View Table

Summary

Benner's model outlines the progression of nursing proficiency from novice to expert through five stages. This model emphasizes the importance of experiential learning, as nurses evolve from relying on rules and guidelines (novice stage) to an intuitive and holistic understanding of patient care (expert stage). This progression aligns with the continuous learning and skill acquisition required in the dynamic field of nursing.

On the other hand, the DKIW model provides a framework for understanding the stages of information processing. In nursing, this model is often applied to describe the process of transforming raw data into meaningful information and, ultimately into wise decision-making (Cannas et al., 2019). Data in nursing is collected through various sources including electronic health records, health information exchanges, and other healthcare technologies. This raw data is then processed into knowledge by organizing and interpreting it, further transformed into actionable information, and finally integrated into the nurse's clinical wisdom for effective decision-making.

Benner's Novice to Expert model and the DKIW model complement each other in understanding the progressive development of nursing skills, knowledge utilization, and the intricate process of transforming data into meaningful wisdom for effective patient care. The combination of these frameworks provides a comprehensive approach to nursing education, practice, and decision-making in an ever-evolving healthcare landscape.

References

Benner, P. (1982, March). From Novice to Expert. The American Journal of Nursing, 82(3), 402-407.

Cannas, A., Tedeschi, L., Atzori, A., & Lunesu, M. (2019). How can nutrition models increase the production efficiency of sheep and goat operations? Animal Frontiers: the review magazine of animal agriculture, 9(2), 33-44.

Kaminski, J. (2021). Theory applied to informatics: DIKW Theory Editorial. Canadian Journal of Nursing Informatics, 16, pp. 3-4. Retrieved from https://cjni.net/journal/?p=9374

Mortimore, G., Reynolds, J., Froman, D., Brannigan, C., & Mitchell, K. (2021). From expert to advanced clinical practitioner and beyond. British Journal of Nursing, 30(11), 656-659. doi:https://www.doi.org/10.12968/bjon.2021.30.11.656

Nelson, R. (2018). Informatics: Evolution of the Nelson data, information, knowledge, and wisdom model: Part 1. OJIN: The Online Journal of Issues in Nursing, 23(3). doi:10.3912/OJIN.Vol23No03InfoCol01