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Abstract Background Accurate diagnostic strategies to identify SARS-CoV-2 positive individuals rapidly for management of patient care and protection of health care personnel are urgently needed. The predominant diagnostic test is viral RNA detection by RT-PCR from nasopharyngeal swabs specimens, however the results are not promptly obtainable in all patient care locations. Routine laboratory testing, in contrast, is readily available with a turn-around time (TAT) usually within 1-2 hours. Method We developed a machine learning model incorporating patient demographic features (age, sex, race) with 27 routine laboratory tests to predict an individual’s SARS-CoV-2 infection status. Laboratory testing results obtained within 2 days before the release of SARS-CoV-2 RT-PCR result were used to train a gradient boosting decision tree (GBDT) model from 3,356 SARS-CoV-2 RT-PCR tested patients (1,402 positive and 1,954 negative) evaluated at a metropolitan hospital. Results The model achieved an area under the receiver operating characteristic curve (AUC) of 0.854 (95% CI: 0.829-0.878). Application of this model to an independent patient dataset from a separate hospital resulted in a comparable AUC (0.838), validating the generalization of its use. Moreover, our model predicted initial SARS-CoV-2 RT-PCR positivity in 66% individuals whose RT-PCR result changed from negative to positive within 2 days. Conclusion This model employing routine laboratory test results offers opportunities for early and rapid identification of high-risk SARS-CoV-2 infected patients before their RT-PCR results are available. It may play an important role in assisting the identification of SARS-CoV-2 infected patients in areas where RT-PCR testing is not accessible due to financial or supply constraints.

Recent healthcare reform has focused on reducing excessive waste in the U.S. healthcare system, with duplicate testing being one of the main culprits. We explore the factors associated with duplicate tests when patients utilize healthcare services from multiple providers, and yet information sharing across these providers is fragmented. We hypothesize that implementation of health information sharing technologies will reduce the duplication rate more for radiology tests compared to laboratory tests, especially when health information sharing technologies are implemented across disparate provider organizations. We utilize a unique panel data set consisting of 39,600 patient visits from 2005 to 2012, across outpatient clinics of 68 hospitals, to test our hypotheses. We apply a quasi-experimental approach to investigate the impact of health information sharing technologies on the duplicate testing rate. Our results indicate that usage of information sharing technologies across health organizations is associated with lower duplication rates, and the extent of reduction in duplicate tests is more pronounced among radiology tests compared to laboratory tests. Our results support the need for implementation of health information exchanges as a potential solution to reduce the incidence of duplicate tests.