Cost-Effectiveness of Day Surgery With Remote Patient Monitoring for Acute Cholecystitis: Economic Modeling Study
Bibliographic record
Abstract
Background: Reducing the time to surgery for patients requiring cholecystectomy may lessen the risk of adverse outcomes. Dedicated day-surgery lists supported by out-of-hospital remote monitoring have been explored as a potential solution; however, the cost-effectiveness of such innovative care models remains largely unexplored. Objective: This study presents a cost-effectiveness analysis comparing an acute day-surgery care model with remote patient monitoring to a conventional inpatient-centric care model for high-acuity cases of cholecystitis. Methods: Post-surgical complications, effectiveness (measured by bed days saved and quality-adjusted life years [QALYs]), and health care costs associated with the two models of care were compared over a 1-year time horizon using a decision tree model. Health care costs were estimated from the Australian health care funder perspective and expressed in 2023 Australian dollars. Uncertainty was assessed using both deterministic and probabilistic sensitivity analyses. Results: The acute day-surgery care model dominated the conventional inpatient-centric care model by saving a mean of 1.7 inpatient days per patient (3.2 days for the conventional model versus 1.5 days for the acute day-surgery model) and lowering net health care costs by a mean of AU $1,416 (US $935) per case over the 1-year time horizon. There was no meaningful difference in QALYs between the care models. These results remained robust in both deterministic and probabilistic sensitivity analyses. Conclusions: An acute day-surgery care model with remote patient monitoring for individuals with acute cases of cholecystitis requiring cholecystectomy would likely free bed days and provide economic benefits to the health care system compared to inpatient-centric practice. Uncertainty in QALY estimates remains a limitation.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 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".