Saving costs and improving clinical outcomes: A two-year cost-utility analysis of emergency department care models for managing persons presenting with musculoskeletal pain using hybrid modelling
Bibliographic record
Abstract
ABSTRACT As access to primary healthcare remains challenging, numerous persons presenting musculoskeletal pain will visit the emergency department (ED) to receive care. Several approaches have been tried to optimize their management, such as the implementation of ED physiotherapy care models. However, no study has evaluated the efficiency of ED care models used to manage musculoskeletal pain beyond three months. This study evaluated the two-year efficiency of two ED care models (i.e., management by an emergency physician, management by a physiotherapist and an emergency physician) by conducting a cost-utility analysis using a hybrid mathematical model (decision tree + Markov model) from two different perspectives: Public Payer and Society. Data for this study came from a randomized clinical trial (n=78, # NCT04009369 ) and from the scientific and grey literature. A probabilistic approach was used to ensure the results’ robustness (Monte Carlo simulation, n=10,000 iterations). All costs were reported in CAD 2024 values. After two years, mean total cost per person for the physiotherapist and emergency physician management was lower than that of usual management by an emergency physician under both perspectives (Public Payer: $6,150 vs $6,840; Society: $30,978 vs $47,222). Mean quality of life gain was also higher in persons managed by a physiotherapist and an emergency physician (1.57 vs 1.47 quality-adjusted life years, QALYs). Management by a physiotherapist and an emergency physician was dominant under both perspectives. Integrating physiotherapists in EDs could result in long-term savings for the Public Payer and Society, while also helping to improve patients’ clinical outcomes.
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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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".