Environmental Factors Predictive of No-Show Visits in Radiology: Observations of Three Million Outpatient Imaging Visits Over 16 Years

Mieloszyk, Rebecca J.; Rosenbaum, Joshua; Hall, Christopher S.; Hippe, Daniel S.; Gunn, Martin L.; Bhargava, Puneet

doi:10.1016/j.jacr.2018.12.046

Cited by 38 publications

(21 citation statements)

References 16 publications

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“…Transportation is a key factor in addition to environmental factors that affect patient attendance and have value in predicting no-show. Factors including weather, distance, socioeconomic status and number of show in previous appointments [39]. These results con rm previous ndings by Dantas et al (2019) that lead-time and number of previous show/no-show are important factors in appointment attendance.…”

Section: Discussionsupporting

confidence: 90%

Predictors of Outpatients’ No-Show: Big Data Analytics using Apache Spark

Daghistani¹,

AlGhamdi²,

Alshammari

et al. 2020

Preprint

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Outpatients who fail to attend their appointments have a negative impact on the healthcare outcome. Thus, healthcare organizations facing new opportunities, one of them is to improve the quality of healthcare. The main challenges is predictive analysis using techniques capable of handle the huge data generated. We propose a big data framework for identifying subject outpatients’ no-show via feature engineering and machine learning (MLlib) in the Spark platform. This study evaluates the performance of five machine learning techniques, using the (2,011,813, 19) outpatients’ visits data. Conducting several experiments and using different validation methods, the Gradient Boosting (GB) performed best, resulting in an increase of accuracy and ROC to 79% and 81%, respectively. In addition, we showed that exploring and evaluating the performance of the machine learning models using various evaluation methods is critical as the accuracy of prediction can significantly differ. The aim of this paper is exploring factors that affect no-show rate and can be used to formulate predictions using big data machine learning techniques

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Section: Discussionsupporting

confidence: 90%

Predictors of Outpatients’ No-Show: Big Data Analytics using Apache Spark

Daghistani¹,

AlGhamdi²,

Alshammari

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Transportation is a key factor in addition to environmental factors that affect patient attendance and have value in predicting no-show. Factors including weather, distance, socioeconomic status and number of show in previous appointments [40]. These results con rm previous ndings by Dantas et al (2019) that lead-time and number of previous show/no-show are important factors in appointment attendance.…”

Section: Discussionsupporting

confidence: 90%

Predictors of Outpatients’ No-Show: Big Data Analytics using Apache Spark

Daghistani¹,

AlGhamdi²,

Alshammari

et al. 2020

Preprint

View full text Add to dashboard Cite

Outpatients who fail to attend their appointments have a negative impact on the healthcare outcome. Thus, healthcare organizations facing new opportunities, one of them is to improve the quality of healthcare. The main challenges is predictive analysis using techniques capable of handle the huge data generated. We propose a big data framework for identifying subject outpatients’ no-show via feature engineering and machine learning (MLlib) in the Spark platform. This study evaluates the performance of five machine learning techniques, using the (2,011,813) outpatients’ visits data. Conducting several experiments and using different validation methods, the Gradient Boosting (GB) performed best, resulting in an increase of accuracy and ROC to 79% and 81%, respectively. In addition, we showed that exploring and evaluating the performance of the machine learning models using various evaluation methods is critical as the accuracy of prediction can significantly differ. The aim of this paper is exploring factors that affect no-show rate and can be used to formulate predictions using big data machine learning techniques.

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“…This work contributes to the existing literature on no-show modeling in Radiology [3,9,15,21,22,24,[30][31][32][33] in several ways. First, by corroborating ndings on no-show predictors in previous works and compiling such results (see Additional le 4: Table S3).…”

Section: Discussionmentioning

confidence: 94%

Modeling the No-Show of Patients to Exam Appointments of Computed Tomography

Silva

Fogliatto

Garcia

et al. 2020

Preprint

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Background: No-shows of patients have negative impacts on healthcare systems, such as resources’ underutilization, efficiency loss, and cost increase. Predicting no-show is key to develop strategies that counteract its effects. In this paper, we propose a model to predict the no-show of ambulatory patients to exam appointments of computed tomography at the Radiology department of a large Brazilian public hospital.Methods: We carried out a retrospective study on 8,382 appointments made to computed tomography (CT) exams between January and December 2017. Penalized logistic regression and multivariate logistic regression were used to model the influence of 15 candidate variables on patients’ no-show. The predictive capabilities of the models were evaluated analyzing the Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC).Results: The no-show rate in computerized tomography exams appointments was 6.65%. The two models performed similarly in terms of AUC. The penalized logistic regression model was selected using the parsimony criterion, with 8 of the 15 variables analyzed appearing as significant. One of the variables included in the model (number of exams scheduled in previous year) had not been previously reported in the related literature.Conclusions: Our findings may be used to guide the development of strategies to reduce the no-show of patients to exam appointments.

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Environmental Factors Predictive of No-Show Visits in Radiology: Observations of Three Million Outpatient Imaging Visits Over 16 Years

Cited by 38 publications

References 16 publications

Predictors of Outpatients’ No-Show: Big Data Analytics using Apache Spark

Predictors of Outpatients’ No-Show: Big Data Analytics using Apache Spark

Predictors of Outpatients’ No-Show: Big Data Analytics using Apache Spark

Modeling the No-Show of Patients to Exam Appointments of Computed Tomography

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