Capacity Constraints and Cost-Effectiveness: A Discrete Event Simulation for Drug-Eluting Stents
Bibliographic record
Abstract
BACKGROUND: Waiting times for access to care, for example, for diagnostic imaging or surgery, are a highly relevant issue in health care. Waiting or deferred treatment caused by limited resource capacities can affect treatment success, quality of life, and costs. However, when treatment alternatives are compared in economic models, often unrestricted availability of resources is assumed, and dynamic changes in waiting lines remain unconsidered. The objective of this study was to evaluate the impact of potential real-world capacity restrictions and implied waiting lines on cost-effectiveness results and additional model outcomes. METHODS: A case study of drug-eluting and bare-metal stent treatment illustrates the effect of hypothetical capacity limitations of daily stenting procedures. Therefore, a decision-analytic model which allows for explicitly defined resource capacities and dynamic waiting lines was built using discrete event simulation. Cost-effectiveness, utilization, waiting time, and budgetary impact of alternative treatment scenarios are analyzed under the assumption of limited and unlimited resource capacities. RESULTS: The compared treatment allocation scenarios in the case study demonstrate that the additional cost for waiting increases the average treatment cost per patient. The different scenarios have different impacts on waiting lines because of the number of repeated interventions. Additionally, this effect leads to changes in cost-effectiveness results for the hypothetical capacity limit. Explicitly modeled capacities allow for further analysis of capacity utilization, waiting lines, and budgetary impact. CONCLUSION: Our model shows that neglected limited capacities can cause wrong cost-effectiveness results. Therefore, capacities should be explicitly included in decision-analytic models if there is evidence of scarcity.
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 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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".