The Return on Investment of a Province-Wide Quality Improvement Initiative for Reducing In-Hospital Sepsis Rates and Mortality in British Columbia, Canada
Bibliographic record
Abstract
OBJECTIVES: Sepsis is a life-threatening medical emergency. There is a paucity of information on whether quality improvement approaches reduce the in-hospital sepsis caseload or save lives and decrease the healthcare system and society's cost at the provincial/national levels. This study aimed to assess the outcomes and economic impact of a province-wide quality improvement initiative in Canada. DESIGN: Retrospective population-based study with interrupted time series and return on investment analyses. SETTING: The sepsis cases and deaths averted over time for British Columbia were calculated and compared with the rest of Canada (excluding Quebec and three territories). PATIENTS: Aggregate data were obtained from the Canadian Institute for Health Information on risk-adjusted in-hospital sepsis rates and sepsis mortality in acute care sites across Canada. INTERVENTIONS: In 2012, the British Columbia Sepsis Network was formed to reduce sepsis occurrence and mortality through education, knowledge translation, and quality improvement. MEASUREMENTS AND MAIN RESULTS: A return on investment analysis compared the financial investment for the British Columbia Sepsis Network with the savings from averted sepsis occurrence and mortality. An estimated 981 sepsis cases and 172 deaths were averted in the post-British Columbia Sepsis Network period (2014-2018). The total cost, including the development and implementation of British Columbia Sepsis Network, was $449,962. Net savings due to cases averted after program costs were considered were $50.6 million in 2018. This translates into a return of $112.5 for every dollar invested. CONCLUSIONS: British Columbia Sepsis Network appears to have averted a greater number of sepsis cases and deaths in British Columbia than the national average and yielded a positive return on investment. Our findings strengthen the policy argument for targeted quality improvement initiatives for sepsis care and provide a model of care for other provinces in Canada and elsewhere globally.
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".