Cargando…

Exploring potential barriers in equitable access to pediatric diagnostic imaging using machine learning

In this work, we examine magnetic resonance imaging (MRI) and ultrasound (US) appointments at the Diagnostic Imaging (DI) department of a pediatric hospital to discover possible relationships between selected patient features and no-show or long waiting room time endpoints. The chosen features inclu...

Descripción completa

Detalles Bibliográficos
Autores principales: Taheri-Shirazi, Maryam, Namdar, Khashayar, Ling, Kelvin, Karmali, Karima, McCradden, Melissa D., Lee, Wayne, Khalvati, Farzad
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9998668/
https://www.ncbi.nlm.nih.gov/pubmed/36908403
http://dx.doi.org/10.3389/fpubh.2023.968319
_version_ 1784903514921631744
author Taheri-Shirazi, Maryam
Namdar, Khashayar
Ling, Kelvin
Karmali, Karima
McCradden, Melissa D.
Lee, Wayne
Khalvati, Farzad
author_facet Taheri-Shirazi, Maryam
Namdar, Khashayar
Ling, Kelvin
Karmali, Karima
McCradden, Melissa D.
Lee, Wayne
Khalvati, Farzad
author_sort Taheri-Shirazi, Maryam
collection PubMed
description In this work, we examine magnetic resonance imaging (MRI) and ultrasound (US) appointments at the Diagnostic Imaging (DI) department of a pediatric hospital to discover possible relationships between selected patient features and no-show or long waiting room time endpoints. The chosen features include age, sex, income, distance from the hospital, percentage of non-English speakers in a postal code, percentage of single caregivers in a postal code, appointment time slot (morning, afternoon, evening), and day of the week (Monday to Sunday). We trained univariate Logistic Regression (LR) models using the training sets and identified predictive (significant) features that remained significant in the test sets. We also implemented multivariate Random Forest (RF) models to predict the endpoints. We achieved Area Under the Receiver Operating Characteristic Curve (AUC) of 0.82 and 0.73 for predicting no-show and long waiting room time endpoints, respectively. The univariate LR analysis on DI appointments uncovered the effect of the time of appointment during the day/week, and patients' demographics such as income and the number of caregivers on the no-shows and long waiting room time endpoints. For predicting no-show, we found age, time slot, and percentage of single caregiver to be the most critical contributors. Age, distance, and percentage of non-English speakers were the most important features for our long waiting room time prediction models. We found no sex discrimination among the scheduled pediatric DI appointments. Nonetheless, inequities based on patient features such as low income and language barrier did exist.
format Online
Article
Text
id pubmed-9998668
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-99986682023-03-11 Exploring potential barriers in equitable access to pediatric diagnostic imaging using machine learning Taheri-Shirazi, Maryam Namdar, Khashayar Ling, Kelvin Karmali, Karima McCradden, Melissa D. Lee, Wayne Khalvati, Farzad Front Public Health Public Health In this work, we examine magnetic resonance imaging (MRI) and ultrasound (US) appointments at the Diagnostic Imaging (DI) department of a pediatric hospital to discover possible relationships between selected patient features and no-show or long waiting room time endpoints. The chosen features include age, sex, income, distance from the hospital, percentage of non-English speakers in a postal code, percentage of single caregivers in a postal code, appointment time slot (morning, afternoon, evening), and day of the week (Monday to Sunday). We trained univariate Logistic Regression (LR) models using the training sets and identified predictive (significant) features that remained significant in the test sets. We also implemented multivariate Random Forest (RF) models to predict the endpoints. We achieved Area Under the Receiver Operating Characteristic Curve (AUC) of 0.82 and 0.73 for predicting no-show and long waiting room time endpoints, respectively. The univariate LR analysis on DI appointments uncovered the effect of the time of appointment during the day/week, and patients' demographics such as income and the number of caregivers on the no-shows and long waiting room time endpoints. For predicting no-show, we found age, time slot, and percentage of single caregiver to be the most critical contributors. Age, distance, and percentage of non-English speakers were the most important features for our long waiting room time prediction models. We found no sex discrimination among the scheduled pediatric DI appointments. Nonetheless, inequities based on patient features such as low income and language barrier did exist. Frontiers Media S.A. 2023-02-24 /pmc/articles/PMC9998668/ /pubmed/36908403 http://dx.doi.org/10.3389/fpubh.2023.968319 Text en Copyright © 2023 Taheri-Shirazi, Namdar, Ling, Karmali, McCradden, Lee and Khalvati. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Public Health
Taheri-Shirazi, Maryam
Namdar, Khashayar
Ling, Kelvin
Karmali, Karima
McCradden, Melissa D.
Lee, Wayne
Khalvati, Farzad
Exploring potential barriers in equitable access to pediatric diagnostic imaging using machine learning
title Exploring potential barriers in equitable access to pediatric diagnostic imaging using machine learning
title_full Exploring potential barriers in equitable access to pediatric diagnostic imaging using machine learning
title_fullStr Exploring potential barriers in equitable access to pediatric diagnostic imaging using machine learning
title_full_unstemmed Exploring potential barriers in equitable access to pediatric diagnostic imaging using machine learning
title_short Exploring potential barriers in equitable access to pediatric diagnostic imaging using machine learning
title_sort exploring potential barriers in equitable access to pediatric diagnostic imaging using machine learning
topic Public Health
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9998668/
https://www.ncbi.nlm.nih.gov/pubmed/36908403
http://dx.doi.org/10.3389/fpubh.2023.968319
work_keys_str_mv AT taherishirazimaryam exploringpotentialbarriersinequitableaccesstopediatricdiagnosticimagingusingmachinelearning
AT namdarkhashayar exploringpotentialbarriersinequitableaccesstopediatricdiagnosticimagingusingmachinelearning
AT lingkelvin exploringpotentialbarriersinequitableaccesstopediatricdiagnosticimagingusingmachinelearning
AT karmalikarima exploringpotentialbarriersinequitableaccesstopediatricdiagnosticimagingusingmachinelearning
AT mccraddenmelissad exploringpotentialbarriersinequitableaccesstopediatricdiagnosticimagingusingmachinelearning
AT leewayne exploringpotentialbarriersinequitableaccesstopediatricdiagnosticimagingusingmachinelearning
AT khalvatifarzad exploringpotentialbarriersinequitableaccesstopediatricdiagnosticimagingusingmachinelearning