The 2020 presidential elections are inching closer, and more than a few candidates are proposing or considering a universal health insurance system. Schulman and Milstein provide a “back of the envelope” estimation for the financial impact of a “Medicare for All” approach to health insurance on US hospitals (https://jamanetwork.com/journals/jama/fullarticle/2730485). This is a tremendous estimation of the impact of such a policy on hospital revenue on an aggregate level. However, we aim to develop a model to inform policy makers and voters about the potential impact of such a policy on deeper levels. We will populate this model using available data on Medicare and private insurer reimbursement. Currently, our model measures the aggregate impact on hospitals in the United States. Future iterations will allow users to explore more granular analysis, such as individual hospitals. This model will be made available to the public to do their own analysis using a user-friendly UI powered by RShiny. The UI will display creative visualizations of the impact of “Medicare for All.”
Effective management of operating room resources relies on accurate predictions of surgical case durations. This prediction problem is known to be particularly difficult in pediatric hospitals due to the extreme variation in pediatric patient populations. We have developed a novel metric for measuring accuracy of predictions which captures key issues relevant to hospital operations. With this metric in mind we have proposed several tree-based prediction models. Some are automated (they do not require input from surgeons) while others are semi-automated (they do require input from surgeons). We see that many of our automated methods generally outperform currently used algorithms and even achieve the same performance as surgeons. Our semi-automated methods can outperform surgeons by a significant margin. This work provides insights into the predictive value of different features and suggests avenues of future work.