Evidence map›Paper›PMID 37165033›Full record

ArticleScientific reports2023

The impact of surgical volume on hospital ranking using the standardized infection ratio.

Shangyuan Ye, Daniel Li, Tingting Yu, Daniel A Caroff, Jeffrey Guy, Russell E Poland, Kenneth E Sands, Edward J Septimus, Susan S Huang, Richard Platt and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Variable selection in modelling clustered data via within-cluster resampling.The Canadian journal of statistics = Revue canadienne de statistique · 2025
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Shangyuan YeDepartment of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, MA, 02215, USA.
Daniel LiDepartment of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, 02215, USA.
Tingting YuDepartment of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, MA, 02215, USA.
Daniel A CaroffDepartment of Infectious Diseases, Lahey Hospital and Medical Center, Burlington, MA, 01805, USA.
Jeffrey GuyClinical Operations Group, HCA Healthcare, Nashville, TN, 37203, USA.
Russell E PolandDepartment of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, MA, 02215, USA.
Kenneth E SandsDepartment of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, MA, 02215, USA.
Edward J SeptimusDepartment of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, MA, 02215, USA.
Susan S HuangDepartment of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, MA, 02215, USA.
Richard PlattDepartment of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, MA, 02215, USA.
Rui WangDepartment of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, MA, 02215, USA. rwang@hsph.harvard.edu.

Funding

Methods for Profiling Hospital Performance Based on Healthcare-AssociatedInfectionsR01HS027791 · AHRQ · HARVARD PILGRIM HEALTH CARE, INC. · PI WANG, RUI · 2020 to 2023
$1.3M
AHRQ HHS R01 HS027791
6 · The paper itself

Abstract

The Centers for Medicare and Medicaid Services require hospitals to report on quality metrics which are used to financially penalize those that perform in the lowest quartile. Surgical site infections (SSIs) are a critical component of the quality metrics that target healthcare-associated infections. However, the accuracy of such hospital profiling is highly affected by small surgical volumes which lead to a large amount of uncertainty in estimating standardized hospital-specific infection rates. Currently, hospitals with less than one expected SSI are excluded from rankings, but the effectiveness of this exclusion criterion is unknown. Tools that can quantify the classification accuracy and can determine the minimal surgical volume required for a desired level of accuracy are lacking. We investigate the effect of surgical volume on the accuracy of identifying poorly performing hospitals based on the standardized infection ratio and develop simulation-based algorithms for quantifying the classification accuracy. We apply our proposed method to data from HCA Healthcare (2014-2016) on SSIs in colon surgery patients. We estimate that for a procedure like colon surgery with an overall SSI rate of 3%, to rank hospitals in the HCA colon SSI dataset, hospitals that perform less than 200 procedures have a greater than 10% chance of being incorrectly assigned to the worst performing quartile. Minimum surgical volumes and predicted events criteria are required to make evaluating hospitals reliable, and these criteria vary by overall prevalence and between-hospital variability.

Indexed as

Digestive System Surgical ProceduresMedicareAgedHospitalsHumansRetrospective StudiesSurgical Wound InfectionUnited States

Identifiers

PMID37165033
PMCPMC10172297

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.