Hospital contextual factors affecting the implementation of health technologies: a systematic review
Bibliographic record
Abstract
Abstract Background To keep a high quality of assistance it is important for hospitals to invest in health technologies (HTs) that have the potential of improving health outcomes. Even though guidance exists on how HTs should be introduced, used and dismissed, there is a surprising gap in literature concerning the awareness of hospitals in the actual utilization of HTs. Methods We performed a systematic literature review of qualitative and quantitative studies aimed at investigating hospital contextual factors that influence the actual utilization of HTs. PubMed, Scopus, Web of Science, Econlit and Ovid Medline electronic databases were searched to retrieve articles published in English and Italian from January 2000 to January 2019. The quality of the included articles was assessed using the Critical Appraisal Skills Programme checklist for qualitative studies, Newcastle-Ottawa Scale for the cross-sectional studies and the Mixed Methods Appraisal Tool for mixed method studies. Results We included 33 articles, which were of moderate to high methodological quality. The included articles mostly addressed the contextual factors that impact the implementation of information and communication technologies (ICTs). Overall, for all HTs, the hospital contextual factors were part of four categories: hospital infrastructure, human resource management, financial resources and leadership styles. Conclusion Our systematic review reported that the contextual factors influencing the HTs utilization at hospital level are mainly explored for ICTs. Several factors should be considered when planning the implementation of a new HTs at hospital level. A potential publication bias might be present in our work, since we included articles published only in English and Italian Language, from January 2000 to January 2019. There remains a gap in the literature on the facilitators and barriers influencing the implementation and concrete utilization of medical and surgical HTs, suggesting the need for further studies for a better understanding.
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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 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.013 | 0.017 |
| 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) | 0.004 | 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".