Journal of the American College of Radiology : JACR · 2026
Radiology No-Show Calculator: A Social Determinants of Health-Enriched Machine Learning Prediction Model With Financial Analysis.
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Tippareddy Charit, Anderson Wyatt, Khan Sameed, Cindric Joseph, Hahn Lauren, Bera Kaustav, Bittencourt Leonardo Kayat, Ramaiya Nikhil, Tirumani Sree Harsha, Mohamed Inas
Аннотация
OBJECTIVE: Outpatient radiology appointment no-shows delay timely diagnosis and exacerbate health care disparities, while also resulting in a significant financial and operational burden on health care systems. Machine learning models offer a promising tool for identifying at-risk patients before no-shows occur. We sought to develop a machine learning model enriched with social determinants of health (SDOH) factors to predict outpatient radiology no-shows and evaluate the health equity and financial implications of targeted interventions.
METHODS: In this institutional review board-approved retrospective case-control study at a large academic health system, we analyzed 32,776 outpatient radiology appointments from 9,998 patients (5,000 with at least one no-show during the study period, 4,998 without) between January 2023 and December 2024. The 1:1 case-control design was chosen to enrich the outcome for stable model training (population no-show prevalence 3.9%). A Random Forest model with 16 predictors, including the Area Deprivation Index, was developed and internally validated. Predicted probabilities were recalibrated to the population prevalence to form a deployable risk calculator. Generalized estimating equations logistic regression quantified adjusted associations between SDOH factors and no-show risk.
RESULTS: The Random Forest achieved an area under the receiver operating characteristic of 0.760. No-show rates demonstrated a clear Area Deprivation Index gradient, ranging from 15.0% in the least deprived neighborhoods to 31.8% in the most deprived. The top 10% of highest-risk appointments captured 25.7% of all no-shows, with a number needed to intervene of 1.8. The estimated yearly cost burden of missed appointments was $57.7 million.
CONCLUSION: The Radiology No-Show Calculator is a prediction model that uses SDOH factors to estimate the likelihood of missed appointments. The calculator demonstrates moderate discrimination for predicting no-shows and enables prospective targeted outreach. Future deployment of this model holds promise for improving health outcomes among socioeconomically vulnerable patients while lowering institutional cost burden.
METHODS: In this institutional review board-approved retrospective case-control study at a large academic health system, we analyzed 32,776 outpatient radiology appointments from 9,998 patients (5,000 with at least one no-show during the study period, 4,998 without) between January 2023 and December 2024. The 1:1 case-control design was chosen to enrich the outcome for stable model training (population no-show prevalence 3.9%). A Random Forest model with 16 predictors, including the Area Deprivation Index, was developed and internally validated. Predicted probabilities were recalibrated to the population prevalence to form a deployable risk calculator. Generalized estimating equations logistic regression quantified adjusted associations between SDOH factors and no-show risk.
RESULTS: The Random Forest achieved an area under the receiver operating characteristic of 0.760. No-show rates demonstrated a clear Area Deprivation Index gradient, ranging from 15.0% in the least deprived neighborhoods to 31.8% in the most deprived. The top 10% of highest-risk appointments captured 25.7% of all no-shows, with a number needed to intervene of 1.8. The estimated yearly cost burden of missed appointments was $57.7 million.
CONCLUSION: The Radiology No-Show Calculator is a prediction model that uses SDOH factors to estimate the likelihood of missed appointments. The calculator demonstrates moderate discrimination for predicting no-shows and enables prospective targeted outreach. Future deployment of this model holds promise for improving health outcomes among socioeconomically vulnerable patients while lowering institutional cost burden.