INNOVATIVE TECHNIQUES TO EVALUATE HEALTHCARE DELIVERY STRATEGIES TO REDUCE WAIT TIMES IN ORTHOPAEDIC FOOT CARE
Bibliographic record
Abstract
Orthopaedic forefoot conditions such as bunion deformities, claw toes, and arthritis of the foot are often overlooked by our healthcare system. Access to care with orthopaedic foot and ankle (FA) specialists is concerningly difficult, leading to some of the highest wait times amongst all orthopaedic procedures. Unfortunately, the COVID-19 pandemic has further exacerbated an already troubling systemic issue in terms of adequate and efficient access to FA care. To help address the significant wait times and the backlog of cases, many hospitals have implemented different wait time reduction strategies (WTRS). Popular WTRS include expansion of virtual care, extended hours, and implementation of volume funding remuneration. The primary objective of this study is to determine which WTRS can most effectively address the wait times for FA surgery. The secondary objective will look at the cost-effectiveness of these WTRS. The workflow of an outpatient hospital in Canada was modeled retrospectively from 2016-2022 to assess the efficacy of the aforementioned WTRS. Data sources included hospital decision support, accounting, Ministry of Health wait times, and literature values. To accurately reconstruct the workflow, a discrete event simulation (DES) model was constructed. The DES structure was based on process mapping stemming from a combination of observing workflow, reviewing OR and clinic schedules, and expert opinion from clinicians and hospital management. The three WRTS models that were run included 1) virtual clinics, 2) extended surgical hours and 3) bundled payments model. The outcome metrics included: 1) throughput for clinic/ORs (number of patients seen/cases completed), 2) wait time reductions, and 3) financial impact. For the virtual clinic models, running a range of half to one day of additional virtual clinics reduced wait times by 40.2-58.9%. Ranging the proportion of clinics being virtually from 10% to 90% resulted in a decrease in wait times by 2.7%-25.9%. For extended surgical hours, running the model for a period of 200 OR days a year resulted in 719 cases completed (profit = $518,149.92). Adding one to three additional hours resulted in 816 to 1015 cases done, a 13.5 to 41.2% increase in throughput and $18,828.78 to $76,497.63 increase in profit. For the bundled payments model, the average profit per case without bundled payments was $660.78. Incorporating a bundled pay remuneration model of $1000-$8000 per case resulted in a profit of $-988.55 to $6011.45, with cases being profitable at remuneration of >$2000/case. This study demonstrates that wait time reduction strategies such as virtual clinics, extended surgical hours and bundled payment models have the potential to reduce wait times to clinics and ORs, increase surgical throughput and are financially more viable. Moreover, demonstrating this at an outpatient centre has broad implications in the Canadian healthcare context as it allows for an efficient and cost effective solution to the current surgical backlogs. Future work should aim to prospectively analyse how dedicated outpatient centres may improve patient care, decrease surgical wait times and reduce healthcare costs.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".