Identifying the Dimensions and Challenges of Using Digital Innovation in Hospitals: A Systematic Review Study
Bibliographic record
Abstract
Background: With the advancement of digital technologies, hospitals are increasingly using digital innovations to improve the quality of care, increase productivity, and reduce costs. However, the implementation of these innovations is accompanied by several challenges that can limit their impact. This study aimed to identify the dimensions and challenges of using digital innovation in hospitals. Methods: The present study was conducted using a systematic review method. To collect data, reputable English databases including PubMed, Scopus, and Web of Science, Google Scholar and Persian databases including Magiran, Iran Medex, and SID, were searched. Initially, 1144 articles in English and 38 articles in Persian published from January 2015 to January 2025 were reviewed which examined the dimensions and challenges of digital innovation in hospitals, and finally 16 articles were included in the study. The Newcastle-Ottawa Scale (NOS) was used to assess the quality of studies. Results: According to the survey, the most important dimensions of digital innovation in hospitals include improving the quality of healthcare services, increasing hospital efficiency, reducing healthcare costs, increasing patient satisfaction, and improving hospital information security. Also, key challenges in this field include the lack of appropriate infrastructure and skilled workforce, staff resistance, security concerns and patients data privacy, financial problems, and legal and ethical challenges. Conclusion: Digital innovation in hospitals can increase the quality of services and patient satisfaction, but its optimal utilization requires addressing financial, infrastructure, security, and human resource challenges.
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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 | 0.022 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.018 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".