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.”
It’s fun to complain that powerful Artificial General Intelligence (Hal-AI), the kind destined to enslave us, hasn’t yet cured cancer. But, focusing too much on what Hal-AI can’t yet do makes it easy to overlook the accomplishments of what Practical Artificial Intelligence (Siri-AI) can.
For example, consider a recent article by Dr. Dave Levin, former CMIO for the Cleveland Clinic. He claims that AI currently offers little of value to healthcare, “Chronic diseases like diabetes and hypertension… recognizing and treating acute conditions like sepsis, heart attacks and strokes… better prenatal care, prevention and wellness. This is where the vast burden of illness, suffering and costs lie… AI likely has little to offer here of immediate value and can divert resources and attention from these harder (and frankly less sexy) needs.”
However, it is precisely in these large, “less sexy” areas where Siri-AI holds great promise. Siri-AI has the potential to improve the management of chronic disease like diabetes, prevent common problems like sepsis caused by pressure ulcers, and empower less expensive preventative care.
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.