Interpretable Identification of Comorbidities Associated with Recurrent ED and Inpatient Visits

TL;DR

This paper introduces MSAR, a novel interpretable algorithm that identifies key comorbidities linked to recurrent ED and inpatient visits, aiming to improve patient outcomes and reduce healthcare costs.

Contribution

The paper presents MSAR, a new algorithm that efficiently and interpretably identifies comorbidities associated with recurrent hospital visits using large EHR data.

Findings

MSAR effectively identifies comorbidities linked to recurrent visits.

Application on EHR data validates the algorithm's accuracy.

Potential to inform targeted interventions and reduce costs.

Abstract

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.