Journal Article

Social determinants of health and the prediction of missed breast imaging appointments

Paschalidis, Ioannis Ch, Fishman, Michael D. C., Sotudian, Shahabeddin, Rives, Anna F., Afran, Aaron, LeBedis, Christina A.

2022

Overview

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.

• Publisher: BioMed Central,BioMed Central Ltd,Springer Nature B.V,BMC
• Subject: Algorithms / Analysis / Biopsy / Breast cancer / Breast imaging / Cancer / Datasets / Decision trees / Diagnosis / Electronic health records / Forecasts and trends / Health Administration / Health care management / Health Informatics / Health services / Machine learning / Mammography / Marital status / Medical appointments and schedules / Medicine / Medicine & Public Health / Missed appointment / Nursing Research / Patients / Predictive model / Prevention / Public Health / Questionnaires / Radiology / Regression analysis / Social determinants of health / Support vector machines / Variables