A minimum of 300 words, scholarly written, APA formatted .  A minimum of 2 scholarly references are required other than the textbook and the required readings posted in each module. These are not the complete guidelines for participating in discussions. Please refer to the Grading Rubric for Online Discussion found in the Course Resource module. 

Overview

Nursing professionals, as knowledge workers, possess skills for understanding the value of information for improving healthcare delivery systems and advancing nursing knowledge. Application of nursing informatics (NI) occurs as a result of various sciences including nursing, computer, and information. Delivery of quality healthcare services is dependent on professionals who can utilize data to improve systems of care. In this module the Foundation of Knowledge model is introduced. This conceptual framework guides the introduction and understanding of healthcare informatics and the generation of knowledge in nursing. The following readings are critical to the understanding of health informatics in serving as building blocks for enabling the application of data in the doctor of nursing practice role.

Readings

• Mastrian & McGonigle (2021): Chapters 1, 2, 3, 4, and 5
• Strome: Chapters 1 and 2

Web Sites

• http://www.aaai.org/home.html
• http://www.pnl.gov/coginformatics
• http://www.healthit.gov/

Articles

• The IMIA Code of Ethics for Health Information Professionals
• National Council of State Boards of Nursing (NCSBN) (2018).    A nurse’s guide to the use of social media. https://www.ncsbn.org/3739.htm 
  

  Videos

The Foundation of Knowledge (2017):  https://youtu.be/_37s9z7vplE

Objectives

By the end of this module students will be able to:

• Discuss the Foundation of Knowledge as an organizational framework for generating knowledge.
• Explain how data integrity promotes quality in delivery of healthcare.
• Define ethical decision-making.
• Integrate the Ethical Model in clinical practice and utilization of nursing informatics.

Nurses as Knowledge Workers

Application of nursing informatics in clinical practice is not new for most nursing professionals, but with the ongoing complexity faced by all disciplines in healthcare, nurses are challenged to expand their knowledge of health informatics.  As knowledge workers with professional accountability, “nurses must possess the technical skills to manage equipment and perform procedures; the interpersonal skills to interact appropriately with people; and the cognitive skills to observe, recognize, and collect data, analyze and interpret data, and reach a reasonable conclusion that forms the basis of a decision” (McGonigle & Mastrian, 2021, p. 6). This statement summarizes some competencies required of nursing professional for mastering NI.  To remain competitive in the healthcare industry and adapt to changes generated by technology, nursing professionals must be more knowledgeable than ever in their respective roles. As knowledge workers who comprise the largest segment of healthcare workers, continuing education and commitment to learning these changes are a priority in professional development.

Mastery of the ability to perform data management and transform information into knowledge is critical for nursing professionals with a Doctor of Nursing practice (DNP) degree. According to Essential IV of the The Essentials of Doctoral Education for Advanced Nursing Practice (AACN, 2006) DNP prepared graduates need to set themselves apart and provide leadership in healthcare systems and academic settings by applying technology to assess and evaluate patient care provided.  Gaining an understanding of the DIKW paradigm and Foundation of Knowledge Model can assist with learning.

Fig. 1: Foundations of Knowledge Model

The Foundation of Knowledge Model is an organizing conceptual framework for generating knowledge. The DIKW paradigm contains data-information-knowledge-wisdom. Data are raw facts without meaning.  Knowledge is a single concept or idea derived from data that is interpreted into information. The definition of knowledge has evolved over the past years and originally was noted according to Hebda and Czar (2009) as being synthesized from data and information, after undergoing a logical, analytical process. Mastrian and McGonigle (2021) state that knowledge is relative to the person or group according to viewpoints, beliefs, and understandings. For this course knowledge will be defined as the “awareness and understanding of a set of information and ways that information can be made useful to support a specific task or arrive at a decision” (p. 62). This identified knowledge becomes wisdom for integration in problem-solving. Nurses apply past experiences and individual thinking patterns for decision-making in healthcare delivery.

Computer Science

Computer science is the application of science “that studies the theoretical foundations of information and computation and their implementation and application in computer systems” (Mastrian & McGonigle, 2021, p. 423). Understanding computer science requires knowledge of computer hardware and software. Computers have catapulted society into the Information Age by increasing the generation and availability of knowledge like no other time in history. Advancing nursing practice requires DNP graduates to acquire skills in NI for contributions to knowledge that are based in wisdom.

Computers were invented in the 1940’s, and today, we can’t even begin to imagine what our lives would be without them. These vital pieces of equipment for managing information meet our needs in practically every aspect of our life. Technology continues to downsize computer hardware and make devices that are more portable and user friendly for integration in clinical practice and other facets of our life. As input-output systems, computers process data in complex, but logical formats, by manipulating data and producing information. Both cognitive and computer science are applied in the development of computer hardware and software.

Admittedly, informatics has become a tremendous challenge for healthcare disciplines staying in pace with advances in technology. Nurses are being required to acquire and maintain current knowledge in NI. Advances are occurring rapidly, and for doctoral prepared nursing professionals to become organizational leaders and contribute towards health system changes, cutting edge skills will be required to meet the demand of healthcare organizations (HCO). The Essentials of Doctoral Education for Advanced Practice Nursing (AACN)(2006) addresses this demand and the need to excel and display competency in NI for impacting health delivery systems. The nursing profession must grow at all levels with the ever-changing technological advances in healthcare in order for knowledge to emerge and wisdom to prevail.

Cognitive Science and Informatics

Cognitive science is “the interdisciplinary field that studies the mind, intelligence, and behavior from an information processing perspective” (Mastrian & McGonigle, 2021, p. 422).  Understanding how the mind operates in generating knowledge has been studied for centuries. Today, we continue to investigate how the brain operates. The mind is compared to a computer by researchers on a regular basis. Awareness of this is evident in the increased demand for neuroscience programs/research surfacing in all disciplines such as finance and healthcare, for example. During the 1960s, operations of computers were linked to cognitive science. Application of nursing informatics for improving quality of care is better understood when cognitive science is seen as the modem for enhancing integration and implementation of technologies for healthcare knowledge workers.

Cognitive informatics is an emerging field dedicated to studying how information is processed in the mind and acquired, represented, remembered, retrieved, generated, and communicated for problem-solving and decision making. Studies conducted in this field continue to evolve; these outcomes are leading the initiatives for developing clinical decision support programs, which impact the decision-making process in clinical workflows. These initiatives can present as tools such as “….. computerized alerts and reminders to care providers and patients; clinical guidelines; condition-specific order sets; focused patient data reports and summaries; documentation templates; diagnostic support, and contextually relevant reference information, among other tools.” (HealthIT.gov, para 1).

Ethical Application

Changing paradigms of the Information Age challenge “business as usual” and technology creates different moral dilemmas unsettling to already established values. Although the same ethical models for evaluating these dilemmas can be instituted, the conditions in which these issues will be debated have become more complicated. In 2006 and revised in 2016, The International Medical Informatics Association wrote a detailed code of ethics for health professionals related to the various ethical questions that may arise.

Numerous issues exist when applying ethics to informatics:

• Global healthcare and informatics is driven by informaticians having various perspectives on ethical dilemmas due to diverse backgrounds related to politics, social issues, and humanity.
• Language barriers of the informaticians create the need for technologic translators.
• International panels, committees, and organizations are developing informatics standards for management of ethics.
• Ethical approaches used currently can be applied by healthcare professionals to informatics. Application of past experiences in collaboration with other healthcare professionals is acceptable.
• Informatics tools are incorporated for improved patient outcomes and should be integrated ethically.
• Ethical dilemmas should remain analyzed in the context of origin.
• Healthcare professionals should self-reflect on their own ethical actions and those of colleagues and patients.
• Competence with informatics is required to make ethical decisions on informatics technologies.
• Social media and mobile devices have seen rapid growth posing increased ethical and legal dilemmas. Nurses, along with other disciplines, are not prepared with the decision making required for maintaining confidentiality of information. 

Artificial Intelligence and Machine Learning

Artificial intelligence (AI) and machine learning has gained much attention in many industries, including education and healthcare.  It is important to note the difference between AI and machine learning.  Machine learning is a subset of artificial intelligence.  Machine learning algorithms process data to gain knowledge about individuals, processes, events, health systems, etc.  (Sarker, 2021). 

Haug and Drazen (2023) remind the reader AI and machine learning in healthcare are not new, these forms of technology assist to read medical images such as X-rays, EKGs, etc.  The use of AI and machine-learning programs has expanded to helping public health measures identify outbreaks, supporting genomic medicine and preventive health, and identify rare conditions which may otherwise have gone unnoticed.  Haug and Drazen (2023) also note AI and machine learning also have the potential to support providers efficiency and effectiveness, supporting identifying differential diagnoses and allowing more time to spend with the consumer. 

Sunarti et al. (2021) also emphasizes the benefits of AI in healthcare.  Through a literature review Sunarti et al. (2021) identified common themes among available evidence describing Ais application including cost reduction, less referrals, greater efficiency and assisting healthcare organizations in rural areas recruit and retain providers.  Challenges noted in the literature Sunarti et al. (2021) reviewed include sustainable implementation, consideration of the user’s perspective, and adoption to support public health initiatives. 

We will explore more ethical implications in this discussion for Week Two.

Haug, C. J., & Drazen, J. M. (2023). Artificial intelligence and machine learning in clinical medicine, The New England Journal of Medicine, 388(13), 1201-1208. https://doi.org/10.1056/NEJMra2302038

Sarker I. H. (2021). Machine Learning: Algorithms, real-world applications and research directions. SN Computer Science, 2(3), 160. https://doi.org/10.1007/s42979-021-00592-x

Sunarti, S., Rahman, F. F., Naufal, M., Risky, M., Febriyanto, K., & Masnina, R. (2021). Artificial intelligence in healthcare: opportunities and risk for future. Gaceta Sanitaria, 35, S67-S70.