Hospital episode-of-care costs for hip fractures: an activity-based costing analysis
Bibliographic record
Abstract
Background: Despite the large impact of hip fracture care on hospital budgets, accurate episode-of-care costs (EOCC) calculations for this injury remains a challenge. The objective of this article was to assess EOCC for geriatric patients with hip fractures using an activity-based costing methodology and identify intraoperative, perioperative, and patient-specific factors associated with higher EOCC. Material and Methods: This is a retrospective cohort study involving a total of 109 consecutive patients with hip fracture treated surgically at a Canadian level-1 trauma center from April 2018 to February 2019. Clinical and demographic data were extracted through the institution's centralized data warehouse. Data acquisition also included direct and indirect costs per episode of care, adverse events, and precise temporal data. Results: The median total EOCC was $13,113 (interquartile range 6658), excluding physician fees. Out of the total cost, 75% was attributed to direct costs, which represented a median expenditure of $9941. The median indirect cost of the EOCC was $3322. Based on the multivariate analysis, patients not operated within the 48 hours guidelines had an increased length of stay by 5.7 days (P = 0.003), representing an increase in EOCC of close to 5000$. Higher American Society of Anesthesiology (ASA) scores were associated with elevated EOCC. Conclusion: The cost of managing a patient with geriatric hip fracture from arrival in the emergency department to discharge from surgical ward represented $13,113. Main factors influencing the EOCC included adherence to the 48-hour benchmark surgical delay and ASA score. High-quality costing data are vital in assessing health care spending, conducting cost effectiveness analyses, and ultimately in guiding policy decisions. Level of Evidence: Level III (3), retrospective cohort study.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 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 | 0.001 |
| 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".