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 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

Demographics · Evening · Logistic regression · Machine learning · Magnetic resonance imaging · Morning · Multivariate analysis · Multivariate statistics · Odds · Radiology · Random forest · Receiver operating characteristic · Univariate · Univariate analysis · Computer Science · Demography · Healthcare Operations and Scheduling Optimization · Healthcare Policy and Management · Hospital Admissions and Outcomes · Medicine · Artificial Intelligence · Internal Medicine

• Extremely randomized trees

  Open Access•Pierre Geurts, Damien Ernst et al.•Machine Learning•2006

  Añadir item

• Implicit bias in healthcare professionals

  Open Access•Chloë FitzGerald, Samia Hurst•BMC Medical Ethics•2017

  Añadir item

• Implicit Racial/Ethnic Bias Among Health Care Professionals and Its Influence on Health Care Outcomes

  William J Hall, Mimi V Chapman et al.•American Journal of Public Health•2015

  Añadir item