Application of Simulation in Healthcare Service Operations
Bibliographic record
Abstract
The health system is intricate due to its dynamic nature and critical service requirements. The involvement of multiple layers of health service providers quadrupled this complexity and results in a complicated operating environment. Simulation is often considered an apt technique to model and study complex systems in the literature. The popularity of simulation in the healthcare domain had only accelerated with time and resulted in a large number of articles intended to solve myriad healthcare problems. This article analyzes healthcare simulation literature of the past decade (2007--2016) that addresses operations management issues in various healthcare service delivery levels and categorizes the literature accordingly. In the next step, we attempt to assimilate the entire literature to capture specific health issues addressed, operations management concepts applied, and simulation methods used, and identify major research gaps. Finally, we develop the research agenda from dividing these gaps into the contextual, conceptual, and methodological genre that is consistent with the previous state-of-the-art literature reviews in operations management. Furthermore, this article demonstrates other minute aspects such as “sources of funding” and “tools used for the research” to maintain coherence with the previous reviews in the healthcare simulation. The objective of this work is twofold: to connect the knowledge continuum to the present, and to provide potential research directions for future academicians.
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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".