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Research article
Rodolfo Benedito Zattar Silva
Universidade Federal do Rio Grande do Sul
Corresponding Author
ORCiD: https://orcid.org/0000-0003-2075-5622
License:
This work is licensed under a CC BY 4.0 License. Read Full License
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.
Keywords
No-show modeling, No-show predictors, Radiology, Computed tomography, Variable selection.
Figure 1
Figure 2
This is a list of supplementary files associated with this preprint. Click to download.
- Additionalfile1.pdf
Additional file 1. Information on descriptive variables and references that previously considered them as predictors in no-show models. (PDF)
- Additionalfile2.pdf
Additional file 2. Mathematical nature of the relationship between continuous predictive variables and patient no-show. (PDF)
- Additionalfile3.pdf
Additional file 3. Practical Application of the CT exam no-show prediction model. (PDF)
- Additionalfile4.pdf
Additional file 4. Comparison of findings between the current study and similar studies reported in the literature regarding significance of no-show predictors and restricted to radiology datasets. (PDF)
- Additionalfile5.pdf
Additional file 5. Health expenditure in Brazil in the public sector as percentage of the GDP between 2000 and 2017. (PDF)
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A new preprint design is coming!
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This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Research article
Rodolfo Benedito Zattar Silva
Universidade Federal do Rio Grande do Sul
Corresponding Author
ORCiD: https://orcid.org/0000-0003-2075-5622
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
History
CURRENT STATUS: Posted
Version 1
Posted 30 Sep, 2020
No community comments so far
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
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.
Figure 1
Figure 2
This is a list of supplementary files associated with this preprint. Click to download.
- Additionalfile1.pdf
Additional file 1. Information on descriptive variables and references that previously considered them as predictors in no-show models. (PDF)
- Additionalfile2.pdf
Additional file 2. Mathematical nature of the relationship between continuous predictive variables and patient no-show. (PDF)
- Additionalfile3.pdf
Additional file 3. Practical Application of the CT exam no-show prediction model. (PDF)
- Additionalfile4.pdf
Additional file 4. Comparison of findings between the current study and similar studies reported in the literature regarding significance of no-show predictors and restricted to radiology datasets. (PDF)
- Additionalfile5.pdf
Additional file 5. Health expenditure in Brazil in the public sector as percentage of the GDP between 2000 and 2017. (PDF)
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