Reducing wait times for access to inpatient rehabilitation for patients with acquired brain injury: a quality improvement initiative
Bibliographic record
Abstract
BACKGROUND: Acquired brain injury (ABI) is a major cause of disability. Timely access to inpatient ABI rehabilitation is associated with improved outcomes at the patient, system, and societal levels. PROBLEM: Wait times to access the inpatient ABI rehabilitation programme at the local regional rehabilitation centre by patients referred from community hospitals were consistently greater than provincial targets and benchmarks. OBJECTIVE: This initiative aimed to reduce the mean wait times from referral to admission by 30% (from 27 to 19 days) during the 12-month project period. The secondary aim was to concurrently achieve a reduction in the mean wait times for decisions from 9.5 to 5 days during the same period. METHODS: A non-randomised time-series design was undertaken. Baseline and postintervention wait times were obtained from patient intake charts. Process mapping, value analysis and five-why analysis were used to identify root causes for delays in the intake process. Referring hospital staff and the ABI Rehab interdisciplinary team members were engaged in change idea development and refinement. Iterative Plan-Do-Study-Act cycles were used to improve the change ideas' usability and uptake. Data analysis was performed using the QI Macros package in Microsoft Excel. RESULTS: Root causes for delays included frequent incomplete referrals, variability in the intake process and limitations in rehab bed availability. Two change ideas were introduced: (1) a standardised intake protocol and (2) a referral checklist. Within 3 months, the mean decision wait times were reduced to 4 days (58% reduction), and the mean admission wait times were reduced to 12 days (54% reduction). CONCLUSION: This initiative led to the systematic standardisation of the intake process for the local regional ABI rehab programme. Wait time improvements surpassing the initial aims were observed and may inform future benchmarks.
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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 | 0.045 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".