Consumer ultrasound is a rapidly growing field with immense potential. Hand-held devices that utilize ultrasound technology are becoming increasingly available and affordable, allowing people to take control of their health care in new and empowering ways. Medical professionals are taking notice of the potential of consumer ultrasound. A recent study found that 41 percent of surveyed physicians believe that hand-held ultrasound will be an important part of medical care in the future. The study also found that nearly half of physicians believe that consumer ultrasound will help to improve patient safety.

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. 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. Part of the solution is deployed in Philips product Patient Flow Capacity Suite (PFCS).