Predicting Pediatric Diagnostic Imaging Patient No-show and Extended Wait-Times Using LLMs, Regression, and Tree Based Models

Daniel Rafique^1,2Xuan Liu²Bo Gong^3,4Laura Belsito⁵Melissa D Mccradden^6,7Mjaye L Mazwi⁸Wayne Lee²Graham Ohanlon²Kyle Tsang²Birgit Ertl-Wagner^10,2,3,9Farzad Khalvati^{1,10,11,2,3,4,9*}

• ¹Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, Canada
• ²Research Institute, The Hospital for Sick Children, Toronto, Canada
• ³Department of Medical Imaging, University of Toronto, Toronto, Canada
• ⁴Department of Computer Science, University of Toronto, Toronto, Canada
• ⁵Department of Diagnostic & Interventional Radiology, Toronto, Canada
• ⁶Australian Institute for Machine Learning, University of Adelaide, Adelaide, Australia
• ⁷Women's and Children's Health Network, Adelaide, Australia
• ⁸Seattle Children's Heart Center, Seattle Washington, United States
• ⁹Department of Diagnostic & Interventional Radiology, The Hospital for Sick Children, Toronto, Canada
• ¹⁰Institute of Medical Science, University of Toronto, Toronto, Canada
• ¹¹Vector Institute, Toronto, Canada

Patients missing their appointments (no-shows) are a persistent issue that results in idle resources while delaying critical patient prognosis. Likewise, long waiting times increase frustration for patients, leaving a negative impression on the appointment. In this paper, we explore 3 modalities of diagnostic and interventional radiology appointments for pediatric patients at the Hospital for Sick Children (SickKids), Toronto, ON, Canada. Our goal was to survey machine learning methods that best predict the risk of patient no-shows and long wait-times exceeding 1 hour for scheduling teams to propose targeted downstream accommodations. We experimented with 6 predictive model types separately trained on both tasks which included extreme gradient boosting (XGBoost), Random Forest (RF), Support Vector Machine, Logistic Regression, Artificial Neural Network, and a pre-trained large language model (LLM). Utilizing 20 features containing a mixture of patient demographics and appointment related data, we experimented with different data balancing methods including instance hardness threshold (IHT) and class weighting to reduce bias in prediction. We then conducted a comparative study of the improvements made by utilizing continuous contextual data in our LLM which boasted a 51% improvement in F1 score for the wait-time model. Our XGBoost model had the best combination of AUC and F1 scores (0.96 and 0.62, respectively) for predicting no-show while RF had the best AUC and F1 scores (0.83 and 0.61, respectively) for wait-time prediction. The LLMs also performed well for 90% probability thresholds (high risk patients) while being robustly calibrated on unseen test data. Our results surveyed multiple algorithms and data balancing methods to propose the greatest performing models on our tasks, implemented a unique methodology to use LLMs on heterogeneous data within this domain, and demonstrated the greater importance of contextual appointment data over patient demographic features for a more equitable prediction algorithm. Going forward, the predictive output (calibrated probabilities of events) can be used as stochastic input for risk-based optimized scheduling to provide accommodation for patients less likely to receive quality access to healthcare.