Implementation Challenges for Danish Hospitals in Digital Transformation
Bibliographic record
Abstract
Background Increased digitalization of hospitals is a goal for national and regional health strategies. Since 2019, it has been an explicit goal to increase the use of virtual consultation with hospital patients. Two years after the start of the pandemic, virtual consultation for hospital patients has increased. At Odense University Hospital (OUH), we have seen a 30% and 337% increase in the annual number of telephone and video consultations with patients, respectively. However, the annual number of video consultations is still below 1% of the total number of outpatient visits. Objective This presentation describes challenges that may explain the slow implementation of telemedicine at OUH and how these challenges are handled in practice. Methods The description is based on 12 meetings with hospital managers and staff at OUH. Analysis of the content of the meetings has been condensed into the major themes specified in the results. Results Three main challenges have been found: (1) uncertainty regarding the quality of telemedicine interventions, (2) uncertainty regarding the technical and communicative skills needed to do video consultation, and (3) misunderstandings regarding the economic consequences of telemedicine. To address the uncertainty among our staff toward the clinical quality of telemedicine, a database including randomized trials of telemedicine interventions described in the PubMed database from 2010-2022 was produced. The database shows that more than 96% of interventions results in similar or improved clinical outcomes for selected patient groups. To ensure the skills needed by the hospital staff to do video consultation, we have offered courses in the technical and communicative aspects of video consultation to interested departments. Finally, some members of our staff believe that reducing the number of physical visits may reduce the hospital budget, which is contrary to the actual financial agreements with capitation payment. To address this misunderstanding, information about the true economic consequences of implementing telemedicine has been provided. Conclusions Successful implementation of telemedicine requires more than solid evidence; it also requires initiatives focusing on the challenges among the hospital staff. Conflicts of Interest None declared.
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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 | 0.032 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.019 | 0.006 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".