<p>Machine learning model’s performance.</p>

Next, we developed a decision-support machine learning model to predict whether a laboratory test should be repeated, leveraging the patient’s prior community laboratory results and sociodemographic features. For complete blood count, electrolytes, and C-reactive protein, we found that only 13.3%, 16.3%, and 19.0% of the repeated tests, respectively, were deemed justified. Moreover, we show that an out-of-the-box XGboost model can predict the necessity for repeating these laboratory tests with 91.8%, 89.1%, and 79.2% accuracy, respectively.

In the absence of false negatives, a condition of high clinical importance, the model maintains accuracies of 73.9%, 67.3%, and 55.4% for the three laboratory categories, respectively. This performance level remains sufficient to substantially reduce unnecessary repeat testing for complete blood count and electrolyte tests, but less so for C-reactive protein. The benefits of the model in clinical practice should be further studied.</p></div>