Factors Impacting the Adoption and Potential Reimbursement of a Virtual Reality Tool for Pain Management in Switzerland: Qualitative Case Study
Bibliographic record
Abstract
Background: Pain and its adequate treatment are an issue in hospitals and emergency departments (EDs). A virtual reality (VR) tool to manage pain could act as a valuable complement to common pharmaceutical analgesics. While efficacy could be shown in previous studies, this does not assure clinical adoption in EDs. Objective: The main aim of this study was to investigate which factors affect the adoption and potential reimbursement of a VR tool for pain management in the ED of a Swiss university hospital. Methods: Key informant interviews were conducted using in-depth semistructured interviews with 11 participants reflecting the perspectives of all the relevant stakeholder groups, including physicians, nurses, patients, health technology providers, and health insurance and reimbursement experts. The interviews were recorded and transcribed, and the extracted data were systematically analyzed using a thematic analysis and narrative synthesis of emergent themes. A consolidated framework for eHealth adoption was used to enable a systematic investigation of the topic and help determine which adoption factors are considered as facilitators or barriers or as not particularly relevant for the tool subject of this study. Results: According to the participants, the three key facilitators are (1) organizational environment; (2) tension for change, ease of use, and demonstrability; and (3) employee engagement. Further, the three key barriers to adoption are (1) workload, (2) changes in clinical workflow and habit, and (3) reimbursement. Conclusions: This study concludes that the adoption of a VR tool for pain management in the ED of the hospital subject of this study, although benefiting from a high tension for change in pain and workload management, is highly dependent on the respective organizational environment, engagement of the clinical staff, and reimbursement considerations. While tailored incentive structures and ambassador roles could benefit initial adoption, a change in the reimbursement landscape and further investigation of the positive effects on workflow effectiveness are required to drive long-term adoption.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| 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".