Reduction of Hospital Readmissions through Clustering Based Actionable Knowledge Mining

Healthcare spending has been increasing in the last few decades. One of the main reasons for this increase is hospital readmissions, which is defined as a re-hospitalization of a patient after being discharged from a hospital within a short period of time. The excessive amount of money spent every year on hospital readmissions and the urge to enhance healthcare quality make reducing hospital readmissions a necessity. In this paper, we extract knowledge from a medical dataset and apply the concept of mining actionable rules to guide the health domain experts in their decision-making process. We present novel algorithms to increase the predictability of the patients' paths (the sequence of procedures that patients undertakes to reach a desired treatment) by clustering the patients according to their set of diagnoses. Moreover, we present a scoring metric to evaluate procedures in procedure graphs (the tree of all possible procedure paths) and a scoring metric to evaluate clusters of diagnoses which would allow us to anticipate the number of following readmissions for a new patient. Finally, we present an algorithm to evaluate the score (average number of following readmissions) for new patients prior to applying the action rules and after. The results presented in this paper show that our algorithms are able to reduce the average number of readmissions to a high degree.