Reduction of hospital readmissions

In recent years, healthcare spending has risen and become a burden on many governments. There are multiple reasons for this increase such as overtesting, long medical treatment path, ignoring doctors' orders, ineffective use of technologies, medical errors, many hospital readmissions, unnecessary emergency room (ER) visits, and medical treatment acquired side effects and infections. The first part of this editorial presents Healthcare Cost and Utilization Project (HCUP) datasets and their hierarchical partition used to build hierarchically structured personalized recommendation systems in healthcare domain. The second part outlines a simple strategy for reducing the number of readmissions using the concept of action rules to provide recommendations. First, we extract from HCUP datasets all possible procedure paths (course of treatments) for a given initial medical procedure. Then, we cluster patients according to the similarities in their diagnoses in order to increase the predictability of the course of treatment following this initial procedure. Finally, we present a novel algorithm that provides recommendations (actionable knowledge) to the physicians to put patients on a treatment path that would result in optimal reduction of the number of readmissions for these patients. There is not much research done on decreasing the number of readmissions to hospitals after initial procedure and almost none based on action rules.