Economic impact of the use of the National Surgical Quality Improvement Program
Bibliographic record
Abstract
OBJECTIVES: The American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP®) is a validated, risk-adjusted database for improving the quality and security of surgical care. ACS NSQIP can help participating hospitals target areas that need improvement. The aim of this study was to systematically review the literature analyzing the economic impact of using NSQIP. This paper also provides an estimation of annual cost savings following the implementation of NSQIP and quality improvement (QI) activities in two hospitals in Quebec. METHODS: In June 2018, we searched in seven databases, including PubMed, Embase, and NHSEED for economic evaluations based on NSQIP data. Contextual NSQIP databases from two hospitals were collected and analyzed. A cost analysis was conducted from the hospital care perspective, comparing complication costs before and after 1 year of the implementation of NSQIP and QI activities. The number and the cost of complications are measured. Costs are presented in 2018 Canadian dollars. RESULTS: Out of 1,612 studies, 11 were selected. The level of overall evidence was judged to be of moderate to high quality. In general, data showed that, following the implementation of NSQIP and QI activities, a significant decrease in complications and associated costs was observed, which improved with time. In the cost analysis of contextual data, the reduction in complication costs outweighed the cost of implementing NSQIP. However, this cost analysis did not take into account the costs of QI activities. CONCLUSIONS: NSQIP improves complication rates and associated costs when QI activities are implemented.
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 | 0.019 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".