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Related Concept Videos

Current Trends in Nursing II01:30

Current Trends in Nursing II

Trends in nursing are multifactorial and associated with changes in society, within the nursing profession, and in other professions. Notably, telehealth and remote nursing contribute to successful healthcare delivery for numerous patients and help reduce stress for nurses due to nursing shortages. Nurses can reach patients, monitor their conditions, and interact with them using computers, audio, visual accessories, and telephones—for example, remote patient monitoring systems. Likewise,...
Data Validation01:03

Data Validation

Data validation is an essential part of a comprehensive assessment. Validation is confirming or verifying and opening the door to gathering more assessment data as it clarifies vague or unclear data. The process of checking and verifying the collected information is called data validation. The primary purpose of data validation is to ensure data is as free from error, bias, and misinterpretation as possible.
Nursing assessment guides are generally based on holistic models rather than medical...
Ethical Issues01:27

Ethical Issues

Nurses are essential in patient care, upholding the ethical principles of their profession and effectively navigating ethical dilemmas. Neglecting ethical issues can lead to inadequate patient care, compromised therapeutic relationships, and moral distress among healthcare workers.
Ethical Concerns in Healthcare:
Ethical Dilemmas I01:17

Ethical Dilemmas I

Ethical dilemmas in nursing are of utmost importance, as they often arise from the tension between adhering to core ethical principles and the practical realities of healthcare delivery. These dilemmas require nurses to navigate complex situations where competing ethical considerations pull them in different directions.
Let us explore some examples to understand the potentially complex moral decisions nurses face.
Take the case of caring for minors, particularly in areas related to reproductive...
Documentation of Nursing Diagnosis01:10

Documentation of Nursing Diagnosis

The nurse documents nursing diagnoses and enters them into the patient record. The identified patient's nursing diagnosis is either written out with a plan of care or entered into the electronic health record.
In some settings, data-driven computerized decision support systems are in place, allowing for more accurate nursing diagnoses. The database within one of these systems includes diagnostic labels defining characteristics, activities, and indicators for nursing. A nurse enters assessment...
Current Trends in Nursing I01:28

Current Trends in Nursing I

Current trends in nursing include:

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Related Experiment Videos

Exploring perceptions of data risks in AI-enabled nursing research: A qualitative study.

Xiudi Yin1,2, Hongxia Song2, Joaquim Paulo Moreira3,4

  • 1Shandong First Medical University, Jinan, Shandong, China.

Digital Health
|July 3, 2026
PubMed
Summary

Future nursing professionals perceive five key data risks in generative AI-enabled research, including adaptation, security, quality, ethics, and response. Understanding these risks is crucial for effective healthcare management and training in digital health.

Keywords:
AI-enabled nursing researchdata risk perceptiondigital healthnursing managementqualitative research

Related Experiment Videos

Area of Science:

  • Nursing Research
  • Digital Health
  • Artificial Intelligence

Background:

  • Generative AI in nursing research faces challenges in professional adaptation, data governance, and accountability.
  • Potential risks include privacy breaches, bias amplification, academic misconduct, and accountability gaps.
  • This study focuses on the perceptions of future nursing professionals regarding these risks.

Purpose of the Study:

  • To explore the structure and connotation of data risk perception in generative AI-enabled nursing research.
  • To identify healthcare management and training needs for digital health developments.

Main Methods:

  • Purposeful maximum variance sampling recruited 20 participants from 3 universities.
  • Semi-structured one-on-one interviews were conducted.
  • The study adhered to the COREQ Protocol checklist.

Main Results:

  • Five data risk awareness themes were identified: data adaptation, security, quality, ethics, and response risks.
  • These risks span the entire research process from use and generation to sharing and responsibility.
  • A five-dimensional structure of data risk perception was observed.

Conclusions:

  • The data risk perception of nursing master's students in generative AI research has a clear five-dimensional structure.
  • This structure informs the development of frameworks for AI use boundaries, data governance, ethical compliance, and capacity building.
  • Findings support advancements in nursing research and digital health.