Implementing digital respiratory technologies: a CONNECT CRC systematic scoping review
Bibliographic record
Abstract
Background: Implementation of digital healthcare is complex and challenging. Harmonising implementation strategies can promote safe and equitable digital healthcare, but guidance for implementation is lacking. Methods: CONNECT colleagues(17 countries) identified published literature using Arksey's methodology. We searched ten databases using key terms relating to digital, respiratory and implementation, included studies embedding digital respiratory technologies in routine clinical practice and used implementation frameworks(e.g.NASSS,NPT,REAIM,TDF) to categorise results. The technology used, implementation strategies employed, barriers faced, and outcomes achieved were extracted. Results: We found 85 studies(Dec 2013-2023) including video conferencing, text messaging for remote consultation and rehabilitation training, chatbots and apps/platforms and devices for remote monitoring, self-management and education, vDOT to monitor medication adherence. Government policy shaped the programme and evaluation focus. CFIR,REAIM and TDF were the most widely used frameworks. Co-developing with end users and building initial relationships were key, helping patients and clinicians build trust. Adopters' motivation and belief about the programme’s usefulness determined their adoption and ongoing engagement. Leader commitment, involvement, group cohesion, and good communication facilitated successful implementation. Insufficient resources(time, staff, funding, devices) and technical support, poor interoperability, and lack of reimbursement were implementation barriers. Conclusion: We identified enablers and barriers for implementation. Findings will inform policy statements to promote a harmonised framework for digital respirtory.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.097 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.036 | 0.026 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".