Investigating DRG cost weights for hospitals in middle income countries
Bibliographic record
Abstract
Identifying the cost of hospital outputs, particularly acute inpatients measured by Diagnosis Related Groups (DRGs), is an important component of casemix implementation. Measuring the relative costliness of specific DRGs is useful for a wide range of policy and planning applications. Estimating the relative use of resources per DRG can be done through different costing approaches depending on availability of information and time and budget. This study aims to guide costing efforts in Iran and other countries in the region that are pursuing casemix funding, through identifying the main issues facing cost finding approaches and introducing the costing models compatible with their hospitals accounting and management structures. The results show that inadequate financial and utilisation information at the patient's level, poorly computerized 'feeder systems'; and low quality data make it impossible to estimate reliable DRGs costs through clinical costing. A cost modelling approach estimates the average cost of 2.723 million Rials (Iranian Currency) per DRG. Using standard linear regression, a coefficient of 0.14 (CI = 0.12-0.16) suggests that the average cost weight increases by 14% for every one-day increase in average length of stay (LOS).We concluded that calculation of DRG cost weights (CWs) using Australian service weights provides a sensible starting place for DRG-based hospital management; but restructuring hospital accounting systems, designing computerized feeder systems, using appropriate software, and development of national service weights that reflect local practice patterns will enhance the accuracy of DRG CWs.
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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 | 0.007 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.009 | 0.013 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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 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".