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Abstract In recent years, healthcare spending has risen and become a burden on governments especially in the US. One of the reasons for this increase is hospital readmissions. Decreasing the number of readmissions can reduce the healthcare spending. The first part of this paper examines state-of-the-art applications that aim to reduce the number of readmissions at hospitals. We categorize these applications into three different categories and highlight not only their benefits, but also their weaknesses. Moreover, we describe the limitations and challenges faced by these systems to demonstrate the possible future research paths. The second part of this paper presents novel algorithms to reduce the number of readmissions by applying the concept of personalization and actionable patterns. First, we start by extracting all possible procedure paths (course of treatments) for a given procedure. Then, we cluster patients according to the similarities in their diagnoses in order to increase the predictability of the next 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 on average case. [ABSTRACT FROM AUTHOR]