Interpretable Identification of Comorbidities Associated with Recurrent ED and Inpatient Visits
This addresses the issue of reducing preventable recurrent ED and inpatient visits to improve patient outcomes and lower costs, but it is incremental as it builds on existing association rule methods.
The paper tackled the problem of identifying recurrent patients with high healthcare utilization and determining which comorbidities contribute most to their recurrent visits, resulting in a novel algorithm called MSAR that balances confidence-support trade-off for interpretable identification.
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.