A Coordinator Program in Post-Fracture Osteoporosis Management Improves Outcomes and Saves Costs
Bibliographic record
Abstract
BACKGROUND: The orthopaedic unit at a university teaching hospital hired an osteoporosis coordinator to identify patients with a fragility fracture and to coordinate their education, assessment, referral, and treatment of underlying osteoporosis. We report the results of an analysis of the cost-effectiveness of the use of a coordinator (in comparison with the use of no coordinator) in avoiding future costs of subsequent hip fracture. METHODS: A one-year decision-analysis model was developed. The health outcome was subsequent hip fracture; only direct hospital costs were considered. With use of patient-level data from a previously described coordinator program and data from the literature, the expected annual incidence of subsequent hip fracture was calculated, on the basis of the type of index fracture (wrist, hip, humerus, other), attribution to osteoporosis, age, and gender. The rate of patient referral, the initiation of osteoporosis treatment, and adherence to therapy were modeled to modify the expected incidence of future hip fracture in the presence of a coordinator (with use of data from the program) and in the absence of a coordinator (with use of data from the literature). Sensitivity analysis modeling techniques were used to assess variable uncertainty and to evaluate coordinator cost-effectiveness. RESULTS: Deterministic cost-effectiveness analysis showed that a tertiary care center that hired an osteoporosis coordinator who manages 500 patients with fragility fractures annually could reduce the number of subsequent hip fractures from thirty-four to thirty-one in the first year, with a net hospital cost savings of C$48,950 (Canadian dollars in year-2004 values), with use of conservative assumptions. Probabilistic sensitivity analysis indicated a 90% probability that hiring a coordinator costs less than C$25,000 per hip fracture avoided. Hiring a coordinator is a cost-saving measure even when the coordinator manages as few as 350 patients annually. Greater savings are anticipated after the first year and when additional costs such as rehabilitation and dependency costs are considered. CONCLUSIONS: Employment of an osteoporosis coordinator to manage outpatients and inpatients who have fragility fractures is predicted to reduce the incidence of future hip fractures and to save money (a dominant strategy). A probabilistic sensitivity analysis showed a high probability of cost-effectiveness of this intervention from the hospital cost perspective.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".