Hellmich, Thomas

AbstractBackgroundIn 2013 the Accreditation Council for Graduate Medical Education (ACGME) introduced “Milestones” designed to nationally standardize the assessment of resident physicians. Previous studies compare resident self‐assessment on milestones to faculty assessment, with varying degrees of agreement, but integration of self‐assessment into the formative feedback process has not yet been directly studied. This study uses a conceptual framework of self‐determination theory, integrated with concepts from adult learning theory, to compare the perception of the feedback quality given in semiannual reviews before and after the incorporation of resident self‐assessment into the feedback process.MethodsThis was an interventional study conducted in a single emergency medicine residency program at a major academic hospital over 1 calendar year. Residents first engaged in a semiannual review without self‐assessment. At subsequent semiannual reviews, residents completed a Milestone‐based self‐assessment that was provided to the faculty member assigned to conduct their semiannual review. Residents and faculty completed surveys rating perception of feedback quality. Two‐sided Wilcoxon signed‐rank tests were used in comparison analysis.ResultsOne resident did not self‐assess prior to the semiannual review and was excluded leaving 25 paired surveys for analysis. Residents found feedback after the self‐assessment more actionable (p = 0.013), insightful (p = 0.010), and better overall (p = 0.025). Similarly, faculty felt the feedback they provided was more actionable (p < 0.001), more insightful (p < 0.001), and better communicated (p < 0.001); led to improved resident understanding of milestones (p < 0.001); and were overall more satisfied (p < 0.001). Free‐text comments explore pre‐ and postintervention perceptions of feedback.ConclusionsIntegration of self‐assessment into semiannual reviews improves perception of feedback given to residents as perceived by both residents and faculty. Although limited by sample size, the results are promising for a simple, evidence‐based intervention to improve feedback during an existing mandated feedback opportunity.

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.

Workload experienced by health care workers continues to be a challenge to define and quantify. The purpose of the current study is to present descriptive data on the application of sociometers in an emergency care environment during simulated hand-off scenarios and discuss their potential and limitations for quantifying individual’s workload in health care settings. Sociometer devices, worn by nine actors, collected body movement, interactions, and speech data during four simulated hand-off scenarios in the emergency department wards. Results found that sociometers distinguished body movement differences between sitting, standing, lying, and walking individuals. Interactions quantified by the devices were limited by obstructions, distance, and angles between the sociometer devices. The data collected by these devices show promise in providing human factors researchers a tool for quantifying the dynamic exposures experienced by health care workers over time.