Simulation application for resource allocation in facility management processes in hospitals
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose The increasing percentage of aging population (longer life expectancy) and the changing financial policies in the healthcare systems put governments under pressure to optimize its healthcare expenditures without compromising quality. One way to cut down the costs is through improving and optimizing the facility management processes. This paper aims to focus on the issues surrounding this. Design/methodology/approach To demonstrate the application of the research, service management (SM) process which deals with the building services related requests from the customer, one of the facility management (FM) processes, is taken as the focus of this paper. The study applies the lean principles to the SM process to identify the value added and non‐value added activities in the process. Process logistics flow is modified to comply with the lean theory. The collected data from six participating hospitals in Germany for the two months of the year 2002 are also used as inputs for the simulation model. Findings Simulation is used to quantify the impact of the lean principles proposed changes on the system performance. The simulation analysis has proved to be an effective tool in the selection of optimum resources for the SM process in hospitals. The implementation of lean and simulation will assist the facility manager in the selection of the optimum crew size in various sub processes, thus eliminating the trial and error approach. Research limitations/implications To develop a generic model for all categories of hospitals, substantial data are needed for the simulation model. In this paper, the SM process results from one category of hospitals are presented. Practical implications The methodology can be extended to the other FM processes in different hospitals, with proper modification. Originality/value The simulated process model was useful to analyze “what if” scenarios for the decision‐making regarding optimum resource allocation.
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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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it