The DOI matches its Crossref record.
Does Surgical Quality Improve in the American College of Surgeons National Surgical Quality Improvement Program: An Evaluation of All Participating Hospitals
Abstract
BACKGROUND/OBJECTIVE: The National Surgical Quality Improvement Program (NSQIP) has demonstrated quality improvement in the VA and pilot study of 14 academic institutions. The objective was to show that American College of Surgeons (ACS)-NSQIP helps all enrolled hospitals. METHODS: ACS-NSQIP data was used to evaluate improvement in hospitals longitudinally over 3 years (2005-2007). Improvement was defined as reduction in risk-adjusted "Observed/Expected" (O/E) ratios between periods with risk adjustment held constant. Multivariable logistic regression-based adjustment was performed and included indicators for procedure groups. Additionally, morbidity counts were modeled using a negative binomial model, to estimate the number of avoided complications. RESULTS: Multiple perspectives reflected improvement over time. In the analysis of 118 hospitals (2006-2007), 66% of hospitals improved risk-adjusted mortality (mean O/E improvement: 0.174; P < 0.05) and 82% improved risk adjusted complication rates (mean improvement: 0.114; P < 0.05). Correlations between starting O/E and improvement (0.834 for mortality, 0.652 for morbidity), as well as relative risk, revealed that initially worse-performing hospitals had more likelihood of improvement. Nonetheless, well-performing hospitals also improved. Modeling morbidity counts, 183 hospitals (2007), avoided ~9598 potential complications: ~52/hospital. Due to sampling this may represent only 1 of 5 to 1of 10 of the true total. Improvement reflected aggregate performance across all types of hospitals (academic/community, urban/rural). Changes in patient risk over time had important contributions to the effect. CONCLUSIONS: ACS-NSQIP indicates that surgical outcomes improve across all participating hospitals in the private sector. Improvement is reflected for both poor- and well-performing facilities. NSQIP hospitals appear to be avoiding substantial numbers of complications- improving care, and reducing costs. Changes in risk over time merit further study.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Bilimoria, K. Y., Liu, Y., Paruch, J. L., Zhou, L., Kmiecik, T. E., Ko, C. Y., & Cohen, M. E. (2013) · Journal of the American College of Surgeons · doi:10.1016/j.jamcollsurg.2013.07.385 · Cited by 1,905
Self-citationCohen, M. E., Ko, C. Y., Bilimoria, K. Y., Zhou, L., Huffman, K., Wang, X., Liu, Y., Kraemer, K., Meng, X., Merkow, R., et al. (2013) · Journal of the American College of Surgeons · doi:10.1016/j.jamcollsurg.2013.02.027 · Cited by 583
Self-citationBertsimas, D., Dunn, J., Velmahos, G. C., & Kaafarani, H. M. A. (2018) · Annals of Surgery · doi:10.1097/sla.0000000000002956 · Cited by 321
Surgical research using national databases
Alluri, R. K., Leland, H., & Heckmann, N. (2016) · Annals of Translational Medicine · doi:10.21037/atm.2016.10.49 · Cited by 266
The Cosmos Collaborative: A Vendor-Facilitated Electronic Health Record Data Aggregation Platform
Tarabichi, Y., Frees, A., Honeywell, S., Huang, C., Naidech, A. M., Moore, J. H., & Kaelber, D. C. (2021) · ACI Open · doi:10.1055/s-0041-1731004 · Cited by 139
Metadata
- Authors
- Hall, B. L., Hamilton, B. H., Richards, K., Bilimoria, K. Y., Cohen, M. E., & Ko, C. Y.
- Venue
- Annals of Surgery
- Published
- 2009-09
- Volume
- 250
- Issue
- 3
- Pages
- 363-376
- ISSN
- 0003-4932 (ISSN-L)
- OpenAlex
- W2074515379
- Cited by
- 753
- References
- 0
- FWCI
- 65.25
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Does Surgical Quality Improve in the American College of Surgeons National Surgical Quality Improvement Program: An Evaluation of All Participating Hospitals. https://app.cochat.ai/papers/10.1097/sla.0b013e3181b4148f
- The paper
- Hall, B. L., Hamilton, B. H., Richards, K., Bilimoria, K. Y., Cohen, M. E., & Ko, C. Y. (2009). Does Surgical Quality Improve in the American College of Surgeons National Surgical Quality Improvement Program: An Evaluation of All Participating Hospitals. Annals of Surgery. https://doi.org/10.1097/sla.0b013e3181b4148f
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.