Close-Up on Ambulance Service Estimation in Indonesia: Monte Carlo Simulation Study
Bibliographic record
Abstract
BACKGROUND: Emergency medical services have a pivotal role in giving timely and appropriate responses to emergency events caused by medical, natural, or human-caused disasters. To provide adequate resources for the emergency services, such as ambulances, it is necessary to understand the demand for such services. In Indonesia, estimates of demand for emergency services cannot be obtained easily due to a lack of published literature or official reports concerning the matter. OBJECTIVE: This study aimed to ascertain an estimate of the annual volume of hospital emergency visits and the corresponding demand for ambulance services in the city of Jakarta. METHODS: In this study, we addressed the problem of emergency services demand estimation when aggregated detailed data are not available or are not part of the routine data collection. We used survey data together with the local Office of National Statistics reports and sample data from hospital emergency departments to establish parameter estimation. This involved estimating 4 parameters: the population of each area per period (day and night), the annual per capita hospital emergency visits, the probability of an emergency taking place in each period, and the rate of ambulance need per area. Monte Carlo simulation and naïve methods were used to generate an estimation for the mean ambulance needs per area in Jakarta. RESULTS: The results estimated that the total annual ambulance need in Jakarta is between 83,000 and 241,000. Assuming the rate of ambulance usage in Jakarta at 9.3%, we estimated the total annual hospital emergency visits in Jakarta at around 0.9-2.6 million. The study also found that the estimation from using the simulation method was smaller than the average (naïve) methods (P<.001). CONCLUSIONS: The results provide an estimation of the annual emergency services needed for the city of Jakarta. In the absence of aggregated routinely collected data on emergency medical service usage in Jakarta, our results provide insights into whether the current emergency services, such as ambulances, have been adequately provided.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".