Experiences of Nurses Working in a Fully Digital Hospital: A Phenomenological Study
Bibliographic record
Abstract
BACKGROUND: With the increasing development and integration of information and communication technology (ICT) into hospitals, there remains a lack of understanding of the impact of these technologies on the hospital's largest core users: nurses. Humber River Hospital (HRH), one of the first hospitals to completely integrate technology across all hospital systems and workflows, has sought to understand how ICTs have transformed the clinical working environment. OBJECTIVE: The aim of the study was to achieve a deeper understanding of the lived experiences of nurses practising in North America's first digital hospital. METHODS: The methodological approach was informed by van Manen's hermeneutic phenomenological methodology. Data were gathered through in-depth semi-structured interviews with eight nurses at HRH. Thematic analysis was conducted using the van Manen and Colaizzi methods of data analysis. RESULTS: Six thematic categories that formed the nurses' lived experiences of working in a digital environment were identified: safety, time, teamwork, technology failures, patient responses and adapting. CONCLUSION: Nurses at HRH identified six themes regarding their lived experiences working in a fully digital hospital that provide an insight into nurses' values and cause us to reflect on how we might use this information to further support nursing practice in the fully digital environment. Nurses at HRH seem to have normalized the nursing process within the fully digital environment. Technology appears to be viewed by nurses at HRH within the premise of nursing as an art, allowing patient responses to be acknowledged and incorporated into nursing workflows, and as a science, permitting safe care delivery. Overall, nurses perceived technology as being essential for patient safety and facilitating nursing practice. These findings offer insight into nurses' perception of ICTs, and as technological advancements continue to emerge, these findings will inform education, practice and policy.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.012 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".