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Social determinants of health and the prediction of missed breast imaging appointments
BACKGROUND: Predictive models utilizing social determinants of health (SDH), demographic data, and local weather data were trained to predict missed imaging appointments (MIA) among breast imaging patients at the Boston Medical Center (BMC). Patients were characterized by many different variables, i...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9714014/ https://www.ncbi.nlm.nih.gov/pubmed/36451240 http://dx.doi.org/10.1186/s12913-022-08784-8 |
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author | Sotudian, Shahabeddin Afran, Aaron LeBedis, Christina A. Rives, Anna F. Paschalidis, Ioannis Ch. Fishman, Michael D. C. |
author_facet | Sotudian, Shahabeddin Afran, Aaron LeBedis, Christina A. Rives, Anna F. Paschalidis, Ioannis Ch. Fishman, Michael D. C. |
author_sort | Sotudian, Shahabeddin |
collection | PubMed |
description | BACKGROUND: Predictive models utilizing social determinants of health (SDH), demographic data, and local weather data were trained to predict missed imaging appointments (MIA) among breast imaging patients at the Boston Medical Center (BMC). Patients were characterized by many different variables, including social needs, demographics, imaging utilization, appointment features, and weather conditions on the date of the appointment. METHODS: This HIPAA compliant retrospective cohort study was IRB approved. Informed consent was waived. After data preprocessing steps, the dataset contained 9,970 patients and 36,606 appointments from 1/1/2015 to 12/31/2019. We identified 57 potentially impactful variables used in the initial prediction model and assessed each patient for MIA. We then developed a parsimonious model via recursive feature elimination, which identified the 25 most predictive variables. We utilized linear and non-linear models including support vector machines (SVM), logistic regression (LR), and random forest (RF) to predict MIA and compared their performance. RESULTS: The highest-performing full model is the nonlinear RF, achieving the highest Area Under the ROC Curve (AUC) of 76% and average F1 score of 85%. Models limited to the most predictive variables were able to attain AUC and F1 scores comparable to models with all variables included. The variables most predictive of missed appointments included timing, prior appointment history, referral department of origin, and socioeconomic factors such as household income and access to caregiving services. CONCLUSIONS: Prediction of MIA with the data available is inherently limited by the complex, multifactorial nature of MIA. However, the algorithms presented achieved acceptable performance and demonstrated that socioeconomic factors were useful predictors of MIA. In contrast with non-modifiable demographic factors, we can address SDH to decrease the incidence of MIA. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12913-022-08784-8. |
format | Online Article Text |
id | pubmed-9714014 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-97140142022-12-02 Social determinants of health and the prediction of missed breast imaging appointments Sotudian, Shahabeddin Afran, Aaron LeBedis, Christina A. Rives, Anna F. Paschalidis, Ioannis Ch. Fishman, Michael D. C. BMC Health Serv Res Research BACKGROUND: Predictive models utilizing social determinants of health (SDH), demographic data, and local weather data were trained to predict missed imaging appointments (MIA) among breast imaging patients at the Boston Medical Center (BMC). Patients were characterized by many different variables, including social needs, demographics, imaging utilization, appointment features, and weather conditions on the date of the appointment. METHODS: This HIPAA compliant retrospective cohort study was IRB approved. Informed consent was waived. After data preprocessing steps, the dataset contained 9,970 patients and 36,606 appointments from 1/1/2015 to 12/31/2019. We identified 57 potentially impactful variables used in the initial prediction model and assessed each patient for MIA. We then developed a parsimonious model via recursive feature elimination, which identified the 25 most predictive variables. We utilized linear and non-linear models including support vector machines (SVM), logistic regression (LR), and random forest (RF) to predict MIA and compared their performance. RESULTS: The highest-performing full model is the nonlinear RF, achieving the highest Area Under the ROC Curve (AUC) of 76% and average F1 score of 85%. Models limited to the most predictive variables were able to attain AUC and F1 scores comparable to models with all variables included. The variables most predictive of missed appointments included timing, prior appointment history, referral department of origin, and socioeconomic factors such as household income and access to caregiving services. CONCLUSIONS: Prediction of MIA with the data available is inherently limited by the complex, multifactorial nature of MIA. However, the algorithms presented achieved acceptable performance and demonstrated that socioeconomic factors were useful predictors of MIA. In contrast with non-modifiable demographic factors, we can address SDH to decrease the incidence of MIA. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12913-022-08784-8. BioMed Central 2022-11-30 /pmc/articles/PMC9714014/ /pubmed/36451240 http://dx.doi.org/10.1186/s12913-022-08784-8 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Sotudian, Shahabeddin Afran, Aaron LeBedis, Christina A. Rives, Anna F. Paschalidis, Ioannis Ch. Fishman, Michael D. C. Social determinants of health and the prediction of missed breast imaging appointments |
title | Social determinants of health and the prediction of missed breast imaging appointments |
title_full | Social determinants of health and the prediction of missed breast imaging appointments |
title_fullStr | Social determinants of health and the prediction of missed breast imaging appointments |
title_full_unstemmed | Social determinants of health and the prediction of missed breast imaging appointments |
title_short | Social determinants of health and the prediction of missed breast imaging appointments |
title_sort | social determinants of health and the prediction of missed breast imaging appointments |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9714014/ https://www.ncbi.nlm.nih.gov/pubmed/36451240 http://dx.doi.org/10.1186/s12913-022-08784-8 |
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