Improving healthcare operations management with machine learning

Healthcare institutions need modern and powerful technology to provide high-quality, cost-effective care to patients. However, despite the considerable progress in the computerization and digitization of medicine, efficient and robust management tools have yet to materialize. One important reason for this is the extreme complexity and variability of healthcare operations, the needs of which have outgrown conventional management. Machine learning algorithms, scalable and adaptive to complex patterns, may be particularly well suited to solving these problems. Two major advantages of machine learning-the power of building strong models from a large number of weakly predictive features, and the ability to identify key factors in complex feature sets-have a particularly direct connection to the principal operational challenges. The main goal of this work was to study this relationship using two major types of operational problems: predicting operational events, and identifying key workflow drivers. Using practical examples, we demonstrate how machine learning can improve human ability to understand and manage healthcare operations, leading to more efficient healthcare. While computerization and digitization of medicine have advanced substantially, management tools in healthcare have not yet benefited much from these developments due to the extreme complexity and variability of healthcare operations. The ability of machine learning algorithms to build strong models from a large number of weakly predictive features, and to identify key factors in complex feature sets, is tested in operational problems involving hospital datasets on workflow and patient waiting time.