Hypertrophic cardiomyopathy (HCM) is a genetic heart disease that is the leading cause of sudden cardiac death (SCD) in young adults. Despite the well-known risk factors and existing clinical practice guidelines, HCM patients are underdiagnosed and sub-optimally managed. Developing machine learning models on electronic health record (EHR) data can help in better diagnosis of HCM and thus improve hundreds of patient lives. Automated phenotyping using HCM billing codes has received limited attention in the literature with a small number of prior publications. In this paper, we propose a novel predictive model that helps physicians in making diagnostic decisions, by means of information learned from historical data of similar patients. We assembled a cohort of 11,562 patients with known or suspected HCM who have visited Mayo Clinic between the years 1995 to 2019. All existing billing codes of these patients were extracted from the EHR data warehouse. Target ground truth labeling for training the machine learning model was provided by confirmed HCM diagnosis using the gold standard imaging tests for HCM diagnosis echocardiography (echo), or cardiac magnetic resonance (CMR) imaging. As the result, patients were labeled into three categories of "yes definite HCM", "no HCM phenotype", and "possible HCM" after a manual review of medical records and imaging tests. In this study, a random forest was adopted to investigate the predictive performance of billing codes for the identification of HCM patients due to its practical application and expected accuracy in a wide range of use cases. Our model performed well in finding patients with "yes definite", "possible" and "no" HCM with an accuracy of 71%, weighted recall of 70%, the precision of 75%, and weighted F1 score of 72%. Furthermore, we provided visualizations based on multidimensional scaling and the principal component analysis to provide insights for clinicians' interpretation. This model can be used for the identification of HCM patients using their EHR data, and help clinicians in their diagnosis decision making.

Hospital emergency department (ED) operations are affected when critically ill or injured patients arrive. Such events often lead to the initiation of specific protocols, referred to as Resuscitation-team Activation (RA), in the ED of Mayo Clinic, Rochester, MN where this study was conducted. RA events lead to the diversion of resources from other patients in the ED to provide care to critically ill patients; therefore, it has an impact on the entire ED system. This paper presents a data-driven and flexible statistical learning model to quantify the impact of RA on the ED. The model learns the pattern of operations in the ED from historical patient arrival and departure timestamps and quantifies the impact of RA by measuring the deviation of the departure of patients during RA from normal processes. The proposed method significantly outperforms baseline methods based on measuring the average time patients spend in the ED.

The primary objective of this study was to investigate the impact of interrupting nurses on mental workload in emergency departments by using a Natural Goals Operators Methods and Selection rules Language (NGOMSL) simulation model. The model advanced our understanding of how interrupting nurses influenced their mental workload. A time study was conducted to collect emergency nurses’ behaviors related to clinical activities at the Mayo Clinic in Minnesota. After that, the NGOMSL simulation model was developed based on the time study data. Compared to the non-interruption scenario, the result showed that the nurse’s mental workload was 2.04 times higher during patient care activities and 4.72 times higher during EMR charting in the interruption scenario. The simulation results indicated that the NGOMSL model could demonstrate the impact on mental workload caused by interruptions in emergency departments. The findings of this study will contribute to developing a new way to measure nursing mental workload caused by the interruptions.