Quantitative Analysis of Operating Room Inventory Management Practices at a Tertiary Cancer Center
Bibliographic record
Abstract
PURPOSE: In Ontario, health care spending has grown to 45% of total government expenditures. In a public health care system, changes in demographics and the emergence of innovative technologies challenge our ability to adapt to evolving patient needs. To maintain a high standard of clinical effectiveness, there is a need to identify opportunities to improve health care delivery. This study was structured to meet the following objectives: to understand the operating room (OR) inventory practices at a tertiary academic hospital, to mathematically model this process to ascertain service levels based on changes in inventory and demand, and to define the appropriate level of reusable inventory for open and laparoscopic colorectal surgery. METHODS: We retrospectively reviewed OR throughput for all cases of colorectal cancer from January 1, 2010, to January 31, 2011. The process flow of OR instrumentation was studied to understand delays in the provision of inventory. Combining total surgeries performed with surgeon-specific instrument preferences generated daily instrument demand. We fitted parametric demand distributions for two instrument sets for major colon resections. Markovian models were used to estimate the distribution of available inventory and the likelihood of insufficient instruments on any given day. RESULTS: We reviewed 1,458 cases, 39.5% of which involved major open surgery, whereas 26.2% involved laparoscopic surgery. Demand for open and laparoscopic instrument sets was observed to fit binomial (20, 0.15) and Poisson (1.41) distributions, respectively. On the basis of these curves, we estimated the probability distribution of the in-stock inventory and, subsequently, the probability that demand would exceed supply on any given day ( Table 1 ). In particular, with 10 open and six laparoscopic sets currently owned by the institution, the probabilities that there would be insufficient inventory were 3.02% and 2.17%, respectively. [Table: see text] Conclusion: This analysis will guide purchasing decisions based on desired service levels and forecasted changes in demand. Furthermore, by ensuring that demand is being serviced, this analysis will help to curb loss of revenue, decrease wait times, and limit potential patient morbidity. Strategic purchasing can also reduce excessive inventory and therefore minimize shrinkage and obsolescence and increase working capital and institutional flexibility.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| 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 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".