The Transformative Role of Technology in Modern Nursing Practice
Bibliographic record
Abstract
Background: The healthcare sector is experiencing rapid technological advancement, fundamentally changing how nursing care is delivered. While technology's impact on nursing practice has been studied, there remains a need to comprehensively analyze how recent technological innovations are reshaping nursing workflows, patient care quality, and healthcare outcomes. Methods: This review synthesizes findings from peer-reviewed literature published between 2019-2024, analyzing the implementation and impact of various technologies in nursing practice. We conducted a systematic search across major healthcare databases, including PubMed, CINAHL, and Scopus, focusing on electronic health records (EHRs), telehealth platforms, wearable devices, and artificial intelligence applications in nursing. Results: Analysis revealed that technology integration in nursing practice led to a 35% reduction in documentation time, 42% improvement in medication administration accuracy, and 28% enhancement in patient monitoring efficiency. Telehealth applications showed particular promise, with 89% of nurses reporting improved ability to manage patient care remotely. However, challenges including technical literacy requirements and initial implementation costs were identified as significant barriers. Conclusions: Technology has become an indispensable component of modern nursing practice, significantly improving care delivery efficiency and patient outcomes. Strategic implementation of technological solutions, coupled with adequate training and support systems, is crucial for maximizing their benefits in nursing practice.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.015 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".