Bibliographic record
Abstract
Surgical unit costs a high percentage of the hospital budget and represents one of the largest segments of the healthcare budget. Surgical unit provides direct care to diverse surgical patients through pre-surgical, operative, post-operative, and recovery steps. Through delivering the surgical services, surgical units generally face series of operational challenges and lag behind in efficiency. These challenges are amplified by conflict of interests among different stakeholders. To address these issues and deliver the best service to the surgical patients, governments or the health insurers encourage surgical units to be more cost effective. In this respect, Operations Research techniques can be applied to help hospital managers to better utilize their resources. The aim of this thesis is to provide an integrated framework for making effective decisions on the hospital surgical case-mix problem (CMP). This research is inspired by the managerial challenges at the surgical unit of the Montreal Jewish General Hospital (JGH). The significance of this research is to mathematically model and simulate the CMP with the Master Surgical Scheduling and the Advanced Scheduling problems in an Integrated Surgical Case Mix (ISCM) model. This empowers hospital managers to enhance surgical unit efficiency by integrating surgical case mix plan, allocating Operating Room (OR) blocks to surgical divisions and surgeons, and assigning elective surgical cases to the operating rooms, at the strategic, tactical, and operational levels, respectively. The ISCM model is developed under various reimbursement mechanisms (e.g., activity based funding, and global budget) and bed configuration policies (e.g., Semi-pooled, and pooled). The usefulness of ISCM model is boosted by incorporating emergency and off-service patients into the model. A Surgical Ward Design (SWD) simulation model is developed to i. validate the ISCM results under various scenarios, ii. explore the impact of different OR schedules on the surgical unit patient flow, and iii. simulate different surgical unit bed configurations. From technical perspective, a stochastic integer model is developed which limits the probability of the downstream bed shortage through a Chance-Constrained programming approach. Moreover, it controls the risk of high bed shortage cost in a Conditional Value at Risk framework. The linear form of the stochastic model is approximated and calibrated with the full-scale data on 72 surgical procedures, 40 surgeons, and 7 specialties in JGH. Then, the sample average approximation method is presented to solve the ISCM model. The results demonstrate that the stochastic ISCM model outperforms deterministic ISCM model in terms of bed shortage level and OR utilization. The activity based funding policy and the global budget with incentive policy result in the similar surgical case mix. Semi-pooled bed configuration increases the daily bed occupancy variance and the number of required beds versus the pooled bed configuration. Also, off-service patient admission is recommended mostly on Fridays, Saturdays, and Sundays for at most two patients per day. It is observed that the stochastic ISCM optimal results are quite robust to a range of bed shortage cost and OR idle/over-time cost.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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; both teacher heads agree on what is shown here.
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".