Authors

  1. Yuan, Changrong PhD, RN, FAAN

Article Content

This is a new era for the power of data. In recent years, data science emerged as a new and important discipline. Data science can be viewed as an amalgamation of statistics, data mining, databases, and distributed systems.1 Data science has an important role in medicine, health, and nursing. A new concept derived from data science is Data Quotient (DQ), which refers to an individual's ability to use data effectively to anticipate and solve problems. Like IQ (Intelligence Quotient) and EQ (Emotional Quotient), DQ should be a characteristic for people in this new era and a core competence for oncology nurses in the future.

 

Big data can enable nursing practice. The importance of data science in nursing has been realized, and efforts are being made to make use of big nursing data. For example, the Nursing Knowledge Big Data Science Initiative, introduced in 2013, is to develop a roadmap for achieving "sharable and comparable" nursing data and to ensure the timely adoption of big data methodologies across all of nursing's domains.2 Publications have shown that big data can be applied to a range of nursing practice areas,3 including cancer.4 Data can help drive improvements in the way cancer patients are treated and cared for.

 

For DQ to be a core competence for oncology nurses, these nurses would have a strong awareness of data, sensitivity to data, and making use of data. These abilities are not limited to data scientists but also to general health care providers and likely to laypeople in the future. Oncology nurses can help contribute to DQ by collecting and analyzing data about patient care, treatment, and encounters, all of which will contribute to better outcomes through data-driven results. The major elements of DQ for oncology nurses could comprise (a) understanding the importance of big data science in cancer care; (b) collecting or generating data from cancer patients and their families; (c) identifying and interpreting patterns and meanings in data by using big data techniques and statistics; (d) predicting behaviors, events, and processes based on analysis of big data; and (e) developing precise evidence-based interventions for better quality care.

 

Data science and research will empower oncology nurses to discover knowledge in data, make data-driven decisions, and solve care problems encountered in clinical practice by using large-scale data and asking data-based questions. Data Quotient for an oncology nurse is the competence to prevent, diagnose, treat, and evaluate health conditions and contribute to breakthroughs for the health of individuals, families, communities, and populations. Data science can be applied in cancer nursing to prevent risky conditions of patients, making nursing diagnosis and prognosis, developing decision aids system, facilitating symptom management, and evaluating care outcomes. A specific application of data science is to track and quantify patient-reported outcomes of any kind of biological, physical, behavioral, or environmental information5 regarding a particular condition or event and use those findings to develop patient-centered care interventions.

 

Data Quotient for oncology nurses can be developed and enhanced by education and research. Emerging new disciplines and techniques should be reflected in education first. Data science should be a part of the formal education curriculum in medicine, health, and nursing so that students can learn the theories and techniques related to data science before they enter clinical practice. Nurses' DQ can be further developed via conducting big data research to solve the problems encountered in their clinical practice. More research is needed in precise cancer care via the utility of big data and computer-based techniques by nurses. Communication and networking with other healthcare professionals interested in data science, such as Nursing Knowledge: Big Data Science Conference,2 are also useful approaches for improving one's DQ.

 

As an oncology nurse, you need to embrace this new era for data science because it is a powerful tool to improve our practice. Do consider establishing your DQ from now on.

 

Best regards,

 

Changrong Yuan, PhD, RN, FAAN

 

Editorial Board Member, Cancer Nursing

 

School of Nursing, Fudan University, Shanghai, China

 

References

 

1. van der Aalst W. Process Mining: Data Science in Action. 2nd ed. Springer-Verlag Berlin Heidelberg: Springer; 2016:3-23. [Context Link]

 

2. Delaney CW, Weaver C. 2018 Nursing Knowledge Big Data Science Initiative. Comput Inform Nurs. 2018;36(10):473-474. [Context Link]

 

3. Westra BL, Sylvia M, Weinfurter EF, et al. Big data science: a literature review of nursing research exemplars. Nurs Outlook. 2017;65(5):549-561. [Context Link]

 

4. Sanson G, Alvaro R, Cocchieri A, et al. Nursing diagnoses, interventions, and activities as described by a nursing minimum data set: a prospective study in an oncology hospital setting. Cancer Nurs. 2019;42(2):E39-E47. [Context Link]

 

5. Swan M. The quantified self: fundamental disruption in big data science and biological discovery. Big Data. 2013;1(2):85-99. [Context Link]