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).

翻译：在医院环境中,一小部分经常病人对保健资源的使用量不成比例。此外,在许多这类情况下,通过减少经常探访,特别是减少与药物滥用、精神健康和医疗因素有关的经常性探访,可以通过社会-行为干预、门诊或预防性护理来改善;为了解决这个问题,我们制定了一个计算高效和可解释的框架,既查明使用率高的经常病人,又确定哪些常见病人对其经常探访的贡献最大。具体地说,我们提出了一个新奇的算法,称为最低相似性协会规则(MCAR),平衡信任-支持交换,以确定与经常紧急部门(ED)和住院探访最相关的条件。我们用大型电子健康记录(EHR)数据集验证了澳门特别行政区,其中部分解决办法被放置在菲利普产品病人流动能力套件(PFCS)中。