Epistemology is a term that attempts to elucidate how people view knowledge and has been defines as the nature of knowledge and what can we understand and know (i.e., “how do we know what we know”) (Mcbride & Tietze, 2023) 

Science is defined by Chinn and Jacobs (1983) as “a body of knowledge including facts and theories generated by the use of controlled rigorous and precise methods within a delimited area of concern” (Mcbride & Tietze, 2023) 

            Generation of new knowledge refers to the process of creating new knowledge, “the creation of new understanding” (Wilhelm-Chapin & Koszalka, 2016). Knowledge is derived by discovering patterns and relationships between types of information.  

There are different levels of theoretical foundations for health informatics, varying by specificity and concreteness with the broadest being grand theory, followed by middle range theory and then micro level theory. Midrange theories are defined by the ability to empirically test propositions within the theory (Mcbride & Tietze, 2023). Within nursing informatics, midrange theory has commonly been used to assist in the development of objective empirical indicators like variables or constructs (Mcbride & Tietze, 2023). Midrange theories can be used in building deeper understanding about processes and actions within phenomena. Mid-range theories can offer a useful lens to describe complex processes involving humans and technology. Over the past few decades several theories and frameworks have been used. Staggers and Parks (1993) and Turley (1996) developed a framework with the idea that NI is a merger of various sources of knowledge, including information science, cognitive science, computer science, various information/knowledge attributes, and technology interface characteristics (Mcbride & Tietze, 2023). Matney et al. developed the Data Information Knowledge to Wisdom (DIKW) Model which includes the constructs of data-information knowledge. Many of the data and information attributes are used by nurses in everyday practice such as using data points like a patient’s vitals, contextualized, and structured into information about the patient (Mcbride & Tietze, 2023). Many modern-day HIT like certain decision-support systems aim to mimic the abilities of humans to organize related information into that can be used to inform practice. One example of this is the barcode administration systems. Similarly, the use of artificial intelligence has been on the rise. The DIKW Model can help practitioners see that elements like wisdom and knowledge can not be replicated by various digital technologies (Mcbride & Tietze, 2023). 

Some advanced techniques in data mining include anomaly detection, association rule learning, cluster, network and relationship analysis, classification, regression modeling, and summarization. Anomaly detection is defined as pattern detection in identifying data errors or unusual deviation from the norm (Mcbride & Tietze, 2023). Anomaly detection is often used in public health to early patterns and trends in disease outbreaks. Association rule learning is identifying association between variables. Association can be a preliminary work to predictive modeling (Mcbride & Tietze, 2023). Cluster analysis is the discovery of groups or structures in the data, such as clusters of patients who tend to go to one hospital in a given country (Mcbride & Tietze, 2023). Classification is generalizing known structure to new data or information. Classifying patient safety errors related to HIT can support taxonomy development (Mcbride & Tietze, 2023). Regression modeling is modeling data for prediction or explaining some phenomenon with the least amount of error as possible (Mcbride & Tietze, 2023). Regression models are often used for predictive analytics. Lastly, summarization is data aggregation or compacting information, including visualizations and report generation. Summarization is often used in many of the business intelligence tools that aggregate cubic views of data or report certain outcomes (Mcbride & Tietze, 2023). 

 

References

Mcbride, S., & Tietze, M. (2023). Nursing informatics for the advanced practice nurse: Patient safety, quality, outcomes, and interprofessionalism (3rd ed.). Springer Publishing Company.

Wilhelm-Chapin, M., & Koszalka, T. (2016). Generative Learning Theory and its Application to Learning Resources. https://ridlr.syr.edu/wp-content/uploads/2020/11/ConceptPaper2016-GLT.pdf

Subrahmanya, S. V. G., Shetty, D. K., Patil, V., Hameed, B. M. Z., Paul, R., Smriti, K., Naik, N., & Somani, B. K. (2021). The role of data science in healthcare advancements: applications, benefits, and future prospects. Irish Journal of Medical Science (1971 -), 191(4). https://doi.org/10.1007/s11845-021-02730-z

2. What Big Data application have you used in your own practice and have you seen a positive or negative change in using the application?

3. Can you give examples of limitations or challenges within Big Data in healthcare?

 

#9 Evidence-Based Practice:

#10 Quality of Practice:

# 15 Resource Utilization

Reference:

American Nurses Association (2015). Nursing Informatics: Scope and Standards of Practice, 2nd Edition. Silver Spring, MD: Nursesbooks.org 

AI is becoming a powerful tool for data analysis. Big data and AI go hand in hand. Without data, there would be no artificial intelligence. Without artificial intelligence, all that raw data wouldn’t be usable, as there would be no way to process it efficiently, let alone in real-time (Ayers, 2022).

We propose that updates made to the ANA Nursing Informatics Scope largely center on AI and its rising integral use in Health Care Data. There are already updates made in the Nursing Code of Ethics regarding use of AI within Nursing Practice (ANA, 2022), and the updates have touched briefly on Data and Informatics consideration. It will only be a matter of time until the use of AI specifically within Nursing Informatics will need to be defined and integrated into the ANA Nursing Informatic Scopes and Standards of Practice and Professional Performance.

References:

ANA (2022). The ethical use of Artificial Intelligence in Nursing Practice.

https://www.nursingworld.org/~48f653/globalassets/practiceandpolicy/nursing-excellence/ana-position-statements/the-ethical-use-of-artificial-intelligence-in-nursing-practice_bod-approved-12_20_22.pdf 

Ayer, R. (2022, March 29). Big data and Artificial Intelligence: how they work together. InData Labs. https://indatalabs.com/blog/big-data-tech-and-ai#:~:text=Artificial%20intelligence%20does%20rely%20on,perform%20a%20variety%20of%20tasks.

Khandavilli, P. (2022, July 4). Data Mining and Data Analysis: 4 Key Differences. Hevo Data.

https://hevodata.com/learn/data-mining-and-data-analysis/#:~:text=Data%20analysis%20necessitates%20employing%20technologies,order%20to%20make%20precise%20decisions.