P17 Implementation success of a digital cardiac rehabilitation pathway: a time and motion study
Bibliographic record
Abstract
Background Cardiac rehabilitation (CR) is a structured, multidisciplinary programme that improves clinical and economic outcomes in cardiovascular disease. In the UK, these benefits are limited by poor access, uptake, and completion. Traditional in-person CR is workforce-intensive and difficult to scale amid staffing and funding constraints. Digital cardiac rehabilitation (DCR), using remote delivery through online platforms, offers a scalable alternative. While DCR may enhance access and adherence, its impact on workforce burden – a key factor in implementation success – remains unclear. Aim To evaluate DCR’s impact on workforce burden. Methods A prospective, mixed-methods, observational cohort study was conducted over nine weeks in an NHS CR department. Staff were observed at five prespecified time points before and after DCR implementation. The primary outcome was total mean task time (TMTT) per patient. Secondary outcomes included staff sentiment (questionnaire) and system usability (System Usability Scale, SUS). Results A total of 264 observations were recorded (figure 1). TMTT rose from 260.0 minutes at baseline to 318.5 minutes two weeks post-implementation, then declined to 218.0 minutes by study end – a 16.0% reduction (p < 0.0001). A significant downward trend followed implementation (β = –35.6 mins/period, R² = 0.90, p = 0.0494), with reductions across roles and tasks. Pre-implementation sentiment showed dissatisfaction with documentation. SUS scores dropped initially but returned to near baseline by study end. TMTT Total Mean Task Time; SD: Standard Deviation; DCR: Digital Cardiac Rehabilitation. Conclusion The TMTT reduction reflects a decrease in workforce burden following DCR implementation. While a temporary increase occurred post-implementation, this reversed with staff adaptation. Usability score recovery suggests familiarity improved perceptions, underscoring the importance of sustained training during digital transitions. This study provides the first quantitative evidence that DCR can reduce staff task time per patient after initial adjustment. These findings support DCR’s potential to ease workforce burden and improve CR scalability. Future work should assess long-term outcomes and economic impact.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".