Cost Analysis of Selected Patient Categories Within A Dermatology Department Using an ABC Approach
Bibliographic record
Abstract
BACKGROUND: Present trends in hospital management are facilitating the utilization of more accurate costing methods, which potentially results in superior cost-related information and improved managerial decision-making. However, the Activity-Based Costing method (ABC), which was designed for cost allocation purposes in the 1980s, is not widely used by healthcare organizations. This study analyzes costs related to selected categories of patients, those suffering from psoriasis, varicose ulcers, eczema and other conditions, within a dermatology department at a Czech regional hospital. METHODS: The study was conducted in a hospital department where both inpatient and outpatient care are offered. Firstly, the diseases treated at the department were identified. Further costs were determined for each activity using ABC. The study utilized data from managerial and financial accounting, as well as data obtained through interviews with departmental staff. Using a defined cost-allocation procedure makes it possible to determine the cost of an individual patient with a given disease more accurately than via traditional costing procedures. RESULTS: The cost analysis focused on the differences between the costs related to individual patients within the selected diagnoses, variations between inpatient and outpatient treatments and the costs of activities performed by the dermatology department. Furthermore, comparing the costs identified through this approach and the revenue stemming from the health insurance system is an option. CONCLUSIONS: Activity-Based Costing is more accurate and relevant than the traditional costing method. The outputs of ABC provide an abundance of additional information for managers. The benefits of this research lie in its practically-tested outputs, resulting from calculating the costs of hospitalization, which could prove invaluable to persons involved in hospital management and decision-making. The study also defines the managerial implications of the performed cost analysis for the hospital management. Based on the analysis results, it is possible to standardize activities and performance appraisal (Benchmarking), and provide all necessary information for hospital budgeting practices, especially Activity-Based Budgeting (ABB).
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".