Application of the Activity-Based Costing Method for Unit-Cost Calculation in a Hospital
Bibliographic record
Abstract
BACKGROUND: Choosing an appropriate accounting system for hospital has always been a challenge for hospital managers. Traditional cost system (TCS) causes cost distortions in hospital. Activity-based costing (ABC) method is a new and more effective cost system. OBJECTIVE: This study aimed to compare ABC with TCS method in calculating the unit cost of medical services and to assess its applicability in Kashani Hospital, Shahrekord City, Iran. METHODS: This cross-sectional study was performed on accounting data of Kashani Hospital in 2013. Data on accounting reports of 2012 and other relevant sources at the end of 2012 were included. To apply ABC method, the hospital was divided into several cost centers and five cost categories were defined: wage, equipment, space, material, and overhead costs. Then activity centers were defined. ABC method was performed into two phases. First, the total costs of cost centers were assigned to activities by using related cost factors. Then the costs of activities were divided to cost objects by using cost drivers. After determining the cost of objects, the cost price of medical services was calculated and compared with those obtained from TCS. RESULTS: The Kashani Hospital had 81 physicians, 306 nurses, and 328 beds with the mean occupancy rate of 67.4% during 2012. Unit cost of medical services, cost price of occupancy bed per day, and cost per outpatient service were calculated. The total unit costs by ABC and TCS were respectively 187.95 and 137.70 USD, showing 50.34 USD more unit cost by ABC method. ABC method represented more accurate information on the major cost components. CONCLUSION: By utilizing ABC, hospital managers have a valuable accounting system that provides a true insight into the organizational costs of their department.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".