Pasupathy, Kalyan S.

Abstract Background: Emergency Department Length of Stay (ED-LOS) is a crucial measurement for clinical and operational decisions and is used for regulatory reporting and research purposes. Accurate and standardized measurement of this duration improves the effectiveness of decisions and findings. Further, automated capture can eliminate dependence on human task-based data-entry. Objective: Compare a real-time location system (RTLS)-based measure of LOS against the current standard of practice (EMR); examine factors associated with discrepancy in LOS. Method: Estimation of LOS using the first and last RFID record in the RTL system, and statistical assessment of their similarity to the corresponding measurement from the EMR. Result: Length of stay durations from the two methods have very similar distributions, with a slight shift. Conclusion: RTLS provides an efficient and standardized means to measure LOS.

Objective.: The Dizziness Handicap Inventory (DHI) is a 25-item self-report questionnaire developed to measure the disabling and handicapping impact of dizziness. The present investigation was conducted in an effort to re-assess the factor structure of the DHI. Study Design.: Retrospective study. Setting.: Tertiary care center. Patients.: Subjects were 1,991 patients who were evaluated in the Mayo Clinic-Rochester Vestibular and Balance Laboratory. Main Outcome Measures.: Exploratory factor analysis: an exploratory bifactor analysis (EFA) with bifactor rotation was used to analyze a random sample of 999 patients. Analyses were used to determine the dominance of the general factor (i.e., total score) relative to the group factor (i.e., subscales). Confirmatory factor analysis: a confirmatory bifactor graded response model was fit with appropriate item-to-group relationships that was discovered by our exploratory analyses. To validate the bifactor model that was identified with the exploratory analyses, a bifactor model with three grouping factors (i.e., Physical manifestations, Catastrophic impact of dizziness, and the Emotional impact of dizziness) were fit to a different random sample of 992 patients using the new item-to-group factor specifications. Results.: In the confirmatory analyses, all items had a positive factor loading on the general factor. There were 14 items that loaded on the general factor only. The rest of the items (n = 11) loaded on both the general factor and one of three group factors. Conclusions.: Conclusions of the study revealed several findings: 1) reporting the result as a total score (i.e., a single general factor) is warranted, and, 2) there is statistical support for the existence of three subscales representing: the Physical manifestations, Catastrophic impact, and Emotional impact of dizziness and vertigo.

Emergency Department (ED) is a complex care delivery environment in a hospital that provides time sensitive urgent and lifesaving care [1]. Emergency medicine is an unscheduled practice and therefore providers experience extreme fluctuations in their workload. ED crowding is a major concern that affects the efficacy of the ED workflow, which often is challenged by long wait times, overuse of observation units, patients either leaving without being seen by a provider and non-availability of inpatient beds to accommodate patients after diagnosis [2]. Evaluating ED workflow is a challenging task due to its chaotic nature, with some success using time-motion studies and novel capacity management tools are nowadays becoming common in ED to address workflow related issues [3]. Several studies reveal that Electronic Medical Record (EMR) adoption has not resulted in significant ED workflow improvements nor reduced the cost of ED operations. Since raw EMR data does not offer operational and clinical decision making insights, advanced EMR data analytics are often sought to derive actionable intelligence from EMR data that can provide insights to improve ED workflow. Improving ED workflow has been an important topic of research because of its great potential to optimize the urgent care needed for the patients and at the same time save time and cost. Radio Frequency Identification Device (RFID) is a wireless automatic identification and data capture technology device that has the potential for improving safety, preventing errors, saving costs, and increasing security and therefore improving overall organizational performance. RFID technology use in healthcare has opened a new space in healthcare informatics research that provides novel data to identify workflow process pitfalls and provide new directions [4]. The potential advantages of RFID adoption in healthcare and especially in ED has been well recognized to save costs and improve care delivery [5]. However, the large upfront infrastructure costs, need for an integrated health information technology (HIT), advanced analytical tools for big data analysis emerging from RFID and skilled data scientists to tackle the data to derive actionable intelligence discourage many hospitals from adoption RFID technology despite its potential advantages. Our recent pilot study on the RFID data analytics demonstrated the feasibility of quantifying and analyzing two novel variables such as ‘patient alone’ time defined as the total time a patient spends alone without interaction with a health care staff in the ED and ‘provider time’ defined as the total time a patient spends interacting with any health care staff [6]. The study motivated a more comprehensive big data analytics of RFID data which can provide better insights into optimizing ED workflow which can improve the quality of care in the ED and also reduce cost. In this work, the authors attempt to describe the RFID adoption in the ED at the Saint Mary’s Hospital at Mayo Clinic, in Rochester, MN, a level one trauma center both for children and adults as a step towards optimizing ED workflow.

Common approaches to emergency department (ED) staffing are to optimize shifts based on historical patient volume or arrival patterns. The former is problematic because historical patient volumes are based on the volumes during existing shifts. Therefore, optimizing shifts based on these volumes can replicate the inefficiencies in these shifts. The latter approach ignores queueing effects. To address the shortcomings of these commonly used approaches, we use classification and regression trees to identify thresholds for patient-to-staff ratios, which split the patient subpopulations into two groups that have different empirical cumulative distribution functions (ecdfs) for patients’ lengths of stay in the ED; one has an extended length and the other has a shorter length. We apply these thresholds and ecdfs to historical patient volumes to calculate an ideal patient volume. After accounting for arrival patterns of ED patients, ideal patient volumes represent the load on the entire ED if patient-to-staff ratios are always kept under the identified thresholds. We then use a mixed-integer programming model to minimize understaffing with respect to the ideal patient volumes. The ED at Mayo Clinic Saint Marys Hospital in Rochester, Minnesota, a trauma center for both adults and pediatrics, implemented the new shift templates in the fourth quarter of 2015. The templates resulted in statistically significant improvements in several patient-centered metrics. In particular, the median length of stay, door-to-provider time, and door-to-bed time decreased by 11, 2.7, and 3 minutes, respectively, despite a six percent increase in patient volume.