This Series on “Data-Driven Laboratory Stewardship” is edited by Dr. Lee Schroeder from Department of Pathology, University of Michigan, Michigan Medicine, Ann Arbor, MI, USA
Review Article on Data-Driven Laboratory Stewardship
Data-driven laboratory stewardship: an implementation science perspective
Machine learning pipelines developed for the prediction of cancelation of inappropriate parathyroid hormone-related peptide orders demonstrate poor performance in predicting provider behavior
A clinical laboratorian’s journey in developing a machine learning algorithm to assist in testing utilization and stewardship
Disclosure:
The series “Data-Driven Laboratory Stewardship” was commissioned by the editorial office, Journal of Laboratory and Precision Medicine without any sponsorship or funding. Lee Schroeder serves as the unpaid Guest Editor for the series.