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  • Probabilistic Prediction of Laboratory Test Information Yield. AMIA ... Annual Symposium proceedings. AMIA Symposium Jiang, Y., Lee, A. H., Ni, X., Corbin, C. K., Irvin, J. A., Ng, A. Y., Chen, J. H. 2023; 2023: 1007-1016

    Abstract

    Low-yield repetitive laboratory diagnostics burden patients and inflate cost of care. In this study, we assess whether stability in repeated laboratory diagnostic measurements is predictable with uncertainty estimates using electronic health record data available before the diagnostic is ordered. We use probabilistic regression to predict a distribution of plausible values, allowing use-time customization for various definitions of "stability" given dynamic ranges and clinical scenarios. After converting distributions into "stability" scores, the models achieve a sensitivity of 29% for white blood cells, 60% for hemoglobin, 100% for platelets, 54% for potassium, 99% for albumin and 35% for creatinine for predicting stability at 90% precision, suggesting those fractions of repetitive tests could be reduced with low risk of missing important changes. The findings demonstrate the feasibility of using electronic health record data to identify low-yield repetitive tests and offer personalized guidance for better usage of testing while ensuring high quality care.

    View details for PubMedID 38222438

    View details for PubMedCentralID PMC10785903