Optimising the mandatory reporting process for drivers admitted to an inpatient stroke rehabilitation unit
Bibliographic record
Abstract
Ontario physicians are legally obligated to report patients who may be medically unfit to drive to the Ministry of Transportation of Ontario (MTO). Currently at Toronto Rehabilitation Institute (TRI), there are no standardised processes for MTO reporting, resulting in inconsistent communication regarding driving with patients and between healthcare providers, redundant assessments and ultimately reduced patient satisfaction. TRI received 10 patient complaints regarding the driving reporting process in the 5 years prior to this project and a large number of patients were not being reported appropriately. The project aim was to use Lean Methods to achieve 100% reporting and optimise communication and education of drivers admitted to a 23-bed inpatient stroke rehabilitation unit. Interventions included process mapping, identification of wasteful steps and implementation of a standard work. Chart audits before and after implementation were performed. Value stream process mapping identified inconsistent reporting procedures and lack of use of the government-issued driver reporting form. Following implementation of standard work processes, use of the MTO Medical Conditions Report Form increased from 0% to 100%. Indication of whether drivers were reported to the MTO in Physical Medicine & Rehabilitation consultation notes increased from 50% to 91%. Identifying reported drivers in the discharge summary, of which patients receive a copy at the time of discharge, increased from 0% to 90%. Physician satisfaction with the new standard work process was qualitatively assessed to be high, with no negative impacts reported. Lean methodology was effective for increasing the usage of the MTO Medical Conditions Report Form, documenting driver status in the initial Physical Medicine & Rehabilitation consultation and indicating MTO reporting status in the discharge summary. Communication between healthcare providers regarding patients' driving status has been successfully standardised, resulting in improved coordination of care and a reduction in patient complaints to zero in the 14 months since implementation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".