From prediction to practice: machine learning models and real-world interventions for magnetic resonance imaging appointment no-shows in a hospital setting.

Mark McMahon^1,2, Jonas Kluckert^1,2,3, Olivio F Donati³, Nicolas Andres Perez Gonzalez^1,2, Laura Kinkead^1,2, Christian Blüthgen³, Andreas Hötker³, Thomas Frauenfelder³, Michael Krauthammer^1,2

¹Department of Quantitative Biomedicine, University of Zürich, Zürich, 8006, Switzerland.

²Biomedical Informatics, University Hospital of Zürich, Zürich, 8091, Switzerland.

³Institute for Diagnostic and Interventional Radiology, University Hospital Zurich, Zürich, 8091, Switzerland.

PubMed PMC DOI PDF

Abstract

OBJECTIVE: To develop a machine learning (ML) model to predict magnetic resonance imaging (MRI) appointment no-shows and test the effectiveness of a targeted phone call intervention in reducing the no-show rate. MATERIALS AND METHODS: Outpatient MRI appointments from a university hospital (2015-2021) were used to train and compare Logistic Regression, Random Forest, and XGBoost models. The best-p…

Create a free account to keep reading

Free members get 10 full research views every month across publications, clinical trials, FDA clearances, adverse events, and NIH grants. No credit card required.

Create Free Account Sign In

Want unlimited research access? See Pro plans

Data Accuracy Notice: Research intelligence on Health AI Central is aggregated from public sources (PubMed, ClinicalTrials.gov, FDA, NIH, CMS, and others) and refreshed nightly. Classifications and derived metrics are produced by automated methods described in our Methodology. We recommend verifying critical data points against the primary sources before making decisions.