Machine learning pipelines developed for the prediction of cancelation of inappropriate parathyroid hormone-related peptide orders demonstrate poor performance in predicting provider behavior

Abstract

Background: Quantification of circulating parathyroid hormone-related peptide (PTHrP) aids in the diagnosis of humoral hypercalcemia of malignancy. However, utilization of this test in the setting of low pre-test probability leads to false positive results, unnecessary follow-up testing, and patient anxiety. As part of an initiative to improve laboratory utilization, all PTHrP orders at our institution are reviewed by a laboratory medicine resident (LMR), who contacts the ordering physician when an order is deemed to have low utility. This review process is time- and labor-intensive, and may sow discontent with providers who feel they are being “second guessed”. We sought to apply machine learning to automate this review process and minimize futile LMR interventions.