Organizational Readiness for Change in the Era of Smart Hospital Wards: Qualitative Study of Health Care Workers’ Insights
Bibliographic record
Abstract
BACKGROUND: Technology is rapidly reshaping conventional hospital environments into smart spaces, enhancing care, improving clinical workflows, and reducing workloads. However, successful implementation depends not only on the effectiveness of the technology but also on organizational readiness for change. OBJECTIVE: This study aimed to identify the key enablers and barriers to readiness for change for a smart hospital ward initiative. METHODS: We conducted a qualitative study to gauge organizational readiness for change for a smart ward initiative. Using purposive sampling, we captured diverse views from clinicians, IT staff, operational support staff, and health care redesign staff. Data were coded deductively under 3 key domains in Weiner's theory of organizational readiness: change efficacy, change commitment, and contextual factors. Subthemes were derived inductively under each domain. RESULTS: We interviewed 19 participants, including clinicians and support staff. Six subthemes emerged: (1) perceived valence and feasibility; (2) transparency and trust in management; (3) shared understanding and readiness to act; (4) resources, training, and staff capability; (5) innovation culture; and (6) past experiences. Participants viewed the initiative as valuable and were motivated to change, citing that the institution's innovation culture was a key enabler. However, there were key barriers, including unclear timelines, inconsistent training, limited resources, and a lack of infrastructure to support innovation. Concerns about overreliance on technology were also prominent, with staff wary of its impact on clinical judgment and system reliability. CONCLUSIONS: Enabling readiness for the smart ward initiative requires transparent communication of timelines and project awareness, particularly for ground staff, the development of training frameworks, and adequate prioritization of innovation. Alleviating commonly reported technology concerns, such as overreliance, loss of human touch, and system reliability, will also be key to adoption and sustainability.
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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 | 0.021 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.011 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| 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".