Impact of an Integrated Hip Fracture Inpatient Program on Length of Stay and Costs
Bibliographic record
Abstract
BACKGROUND: Hip fractures are associated with significant morbidity and mortality. Co-management models pairing orthopaedic surgeons with hospitalists or geriatricians may be effective at improving processes of care and outcomes such as length of stay (LOS) and cost. We set out to determine the effect of an integrated hip fracture co-management model on LOS, cost, and process measures. METHODS: We conducted a single-center pre-post study of 571 patients admitted to an academic medical center with hip fractures between January 2009 and December 2013. The group receiving an integrated medical-surgical co-management incorporating continuous improvement methodology was compared with a control population. Primary outcome was LOS. Secondary outcomes included cost per case, time to surgery, osteoporosis (OP) treatment, preoperative echocardiogram utilization, mortality, and readmission. RESULTS: LOS decreased from 18.2 (1.1) to 11.9 (1.5) days, a reduction of 6.3 days (P < 0.001). Mean cost decreased by $4953 (P < 0.001) per case. Mean time to surgery decreased from 45.8 (66.8) to 29.7 (17.9) hours (P < 0.001). Initiation of OP treatment increased from 55.8% to 96.4% (P < 0.001). Preoperative echocardiogram use decreased from 15.8% to 9.1% (P < 0.05). There was a nonsignificant difference in mortality rate (5.0% vs. 2.1%, P = 0.06). Readmission rate remained unchanged (4.6% vs. 6.0%, P = 0.56). CONCLUSIONS: An integrated medical-surgical co-management model incorporating continuous improvement methodology was associated with reduced LOS, costs, time to surgery, and increased initiation of appropriate OP treatment. LEVEL OF EVIDENCE: Therapeutic Level IV. See Instructions for Authors for a complete description of levels of evidence.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".