Improving hand therapy delivery during care transitions in multisystem trauma patients
Bibliographic record
Abstract
High-quality hand therapy is critical to maximising functional capacity and optimising overall outcomes following hand injuries. Therapy delivery requires clear communication between surgeons and occupational therapists. At Sunnybrook Health Sciences Centre (SHSC), Canada's largest tertiary care centre, suboptimal communication is a significant barrier to efficient hand therapy delivery in acute multisystem trauma patients. A baseline audit at SHSC found that 41% of hand therapy orders required clarification and 35% of patients waited over 24 hours before their order was fulfilled. In many cases, communication errors created unacceptably long delays that were suspected by surgeon stakeholders to impede patient outcomes. This highlighted an opportunity for investigation and system improvement.Using process mapping methodology, we outlined standard process involved in patient care and identified barriers to successful communication. We collaborated with key stakeholders to codesign a standardised template for care orders. We aimed to improve order clarity and consistency with the goal of reducing the incidence of clarification and delays.Postimplementation, the percentage of hand therapy orders requiring clarification was decreased to 24%. The number of patients waiting over 24 hours for therapy was also reduced; however, further investigation is required to verify this finding. In addition, essential order components were more consistently and comprehensively included. Next steps of this work include expanding the use of the order template outside of the multisystem trauma population and improving the communication of hand therapy at discharge from hospital.
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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 | 0.016 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".