The return of investment of hospital-based surgical quality improvement programs in reducing surgical site infection at a Canadian tertiary-care hospital
Bibliographic record
Abstract
OBJECTIVE: We performed a return-on-investment analysis comparing the investment in surgical site infection (SSI) prevention programs in a hospital setting to the savings from averted SSI cases. DESIGN: A retrospective case costing study using aggregated patient data to determine the incidence and costs of SSI infection in surgical departments over time. We calculated return on investment to the hospital and conducted several sensitivity and scenario analyses. SETTING: Data were compiled for the Ottawa Hospital (TOH), a Canadian tertiary-care teaching institution.PatientsWe used aggregated records for all hospital patients who underwent surgical procedures between April 2010 and January 2015.InterventionWe estimated the potential cost savings of the hospital's surgical quality improvement program, namely the Surgeons National Surgical Quality Improvement Program (NSQIP) and the Comprehensive Unit-based Safety Program (CUSP). RESULTS: From 2010 to 2016, TOH invested C$826,882 (US$624,384) in surgical quality improvement programs targeting SSI incidence and accrued C$1,885,110 (US$1,423,460) in cumulative savings from averted SSI cases, generating a return of $2.28 (US$3.02) per dollar invested (95% confidence interval [CI], -0.67 to 7.37). The study findings are sensitive to the estimated cost to the hospital per SSI case and the rate reduction attributable to the prevention program. CONCLUSIONS: The NSQIP and CUSP have produced a positive return on investment at TOH; however, the result rests on several assumptions. This positive return on investment is expected to continue if the hospital can continue to reduce SSI incidence at least 0.25% annually without new investments. Findings from this study highlight the need for continuous program evaluation of the quality improvement initiatives.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".