Wait times and patient throughput after the implementation of a novel model of virtual care in an outpatient neurology clinic: A retrospective analysis
Bibliographic record
Abstract
INTRODUCTION: Neurology wait times - from referral to consultation - continue to grow, leading to various adverse effects on patient outcomes. Key elements of virtual care can be leveraged to improve efficiency. This study examines the implementation of a novel virtual care model - Virtual Rapid Access Clinics - at the Neurology Centre of Toronto. The model employs a patient-centred care workflow, involving multidisciplinary staff and online administrative tools that are synthesized to expedite care and maintain quality. METHODS: Virtual Rapid Access Clinic efficacy was studied by determining average wait times and patient throughput, calculated from anonymous data that was extracted from the clinic patient database (n = 1542). Comparative analysis focused on new patient consultations during the 12-month periods prior to (pre-Virtual Rapid Access Clinic, n = 456) and following (post-Virtual Rapid Access Clinic, n = 1086) Virtual Rapid Access Clinic implementation. RESULTS: After Virtual Rapid Access Clinic implementation, there was a mean 15-day wait time reduction, and a monthly average 52-patient increase in patient throughput. Wait time reductions and increased patient throughput were observed in all three Virtual Rapid Access Clinic sub-clinics - epilepsy, headache and concussion. Respectively, average wait times reduced significantly by 26.4 and 18.9 days and insignificantly by 1.1 days; monthly average patient throughputs increased by 235%, 95% and 161%. DISCUSSION: These findings demonstrated that the Virtual Rapid Access Clinic model of care is effective at reducing patient wait times and increasing patient throughput. While the Virtual Rapid Access Clinic presents a feasible model both during and after pandemic restrictions, further research exploring its scalability in other care contexts, potential changes in care quality and efficiency outside of pandemic restrictions must be performed.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".