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There is a growing amount of evidence that machine learning (ML) algorithms can be used to develop accurate clinical risk scores for a wide range of medical conditions. However, the degree to which such algorithms can affect clinical decision-making is not well understood. Our work attempts to address this problem, investigating the effect of algorithmic predictions on human expert judgment. Leveraging an online survey of medical providers and data from a leading U.S. hospital, we develop a ML algorithm and compare its performance with that of medical experts in the task of predicting 30-day readmissions after solid-organ transplantation. We find that our algorithm is not only more accurate in predicting clinical risk but can also positively influence human judgment. However, its potential impact is mediated by the users’ degree of algorithm aversion and trust. We show that, while our ML algorithm establishes non-linear associations between patient characteristics and the outcome of interest, human experts mostly attribute risk in a linear fashion. To capture potential synergies between human experts and the algorithm, we propose a human-algorithm “centaur” model. We show that it is able to outperform human experts and the best ML algorithm by systematically enhancing algorithmic performance with human-based intuition. Our results suggest that implementing the centaur model could reduce the average patient readmission rate by 26.4%, yielding up to a $770k reduction in annual expenditure at our partner hospital and up to $67 million savings in overall U.S. healthcare expenditures.