2014
DOI: 10.3121/cmr.2014.1250.b1-3
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B1-3: Improving Surgical Case Duration Accuracy with Advanced Predictive Modeling

Abstract: In logistic regression models predicting participation, the most important predictors of participation were having at least one physical examination (OR 1.46, 95%-CI 1.34-1.59) in Whites and long-term membership in Hispanics (OR 1.38, 95%-CI 1.11-1.69). None of the restrictions significantly predicted participation in Blacks (p for interaction with race <0.001). Conclusions: The application of restrictions based on longer membership and regular physical examinations may increase recruitment of non-Hispanic Whi… Show more

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Cited by 2 publications
(6 citation statements)
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“…Most published literature that we were able to identify to date, examining case duration accuracy, are limited to retrospective data modeling and theoretical improvements . We found 1 recently published study that performed implementation of a statistical regression model, showing the ability to better predict when the surgical day would end.…”
Section: Discussionmentioning
confidence: 95%
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“…Most published literature that we were able to identify to date, examining case duration accuracy, are limited to retrospective data modeling and theoretical improvements . We found 1 recently published study that performed implementation of a statistical regression model, showing the ability to better predict when the surgical day would end.…”
Section: Discussionmentioning
confidence: 95%
“…In studies focusing on efforts to reduce surgical case duration and turnover, Dexter et al, Strum et al, and Stepaniak et al emphasized that improving the reliable time estimate of surgical cases leads to improved efficiency of OR processes. Their published literature examining accuracy in the estimation of case duration has been limited to retrospective data modeling.…”
Section: Introductionmentioning
confidence: 99%
“…Chen et al [29] also reported that the predictive capabilities of the model were much lower for new patient appointments in comparison to follow-up visits. Of note, the nal predictive power and precision of a model have also been shown to depend on the type of medical institution and patient characteristics [30]. These two factors were also considered when training our AI model.…”
Section: Discussionmentioning
confidence: 99%
“…For the NHS England, the percentage of no-shows dropped from 8.3% in 2007-2008 to 6.7% in 2017-2018 [31], even after the introduction of a wide range of interventions, highlighting the need for additional, more re ned solutions based on advanced technologies, such as AI. One of the rst AI models to predict noshows was developed by Dravenstott et al [30]; this model used arti cial neural networks to predict the no-show risk separately for primary care and endocrinology clinics. To teach the model, historical data on 3 million visits over the previous two years were included, which covered clinic, provider and patientspeci c variables.…”
Section: Discussionmentioning
confidence: 99%
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