Reducing red blood cell transfusions

The health care industry in the United States is undergoing a paradigm shift from the traditional fee-for-service model to various payment and incentive models based on quality of care rather than quantity of services. One specific scenario where more treatment does not equate to better care is red blood cell transfusions. While blood transfusions often save lives, there are numerous complications which can result and blood should be transfused only if medically necessary. Several studies have indicated that a very high percentage of units transfused are not clinically appropriate. These transfusions increase cost and negatively impact patient outcomes. In this paper, we will present an analytics project to identify and track the transfusions which are performed without clear necessity. Furthermore, we will describe how we utilized data discovery and supervised learning to improve our classification algorithm and the accuracy of our results. We will demonstrate that our project is effectively reducing red blood cell transfusions.