  In the hospital setting, a small percentage of recurrent frequent patients
contribute to a disproportional amount of healthcare resource usage. Moreover,
in many of these cases, patient outcomes can be greatly improved by reducing
reoccurring visits, especially when they are associated with substance abuse,
mental health, and medical factors that could be improved by social-behavioral
interventions, outpatient or preventative care. Additionally, health care costs
can be reduced significantly with fewer preventable recurrent visits.
  To address this, we developed a computationally efficient and interpretable
framework that both identifies recurrent patients with high utilization and
determines which comorbidities contribute most to their recurrent visits.
Specifically, we present a novel algorithm, called the minimum similarity
association rules (MSAR), balancing confidence-support trade-off, to determine
the conditions most associated with reoccurring Emergency department (ED) and
inpatient visits. We validate MSAR on a large Electric Health Record (EHR)
dataset.
