Nestler, David M.

After successfully implementing an acute care telemedicine program internally, health care institutions often consider expanding and scaling services externally beyond the initial organizational network. This report outlines important considerations when expanding acute care telemedicine services externally, including cultural alignment and agility, practice context, staffing models, telemedicine technology selection, interrelated services, legal and compliance considerations, quality assurance, risk management, financing, and integration with medical education. These factors may inform program leaders of major planning considerations, potential barriers, and could influence strategic planning. Prospectively identifying and resolving challenges prior to contracting with new external sites will facilitate smooth, successful implementation and high utilization of the acute care telemedicine service.

Objectives: Patient involvement in the choice of whether to undergo emergency department observation unit (EDOU) admission and cardiac stress testing or follow-up with a physician could increase knowledge, increase satisfaction, and safely decrease resource use. We test this hypothesis in a randomized trial. Methods: We developed and tested Chest Pain Choice, a decision aid that communicates the pre-test probability of an acute coronary syndrome (ACS) within 45 days and makes management options (EDOU admission and stress testing or 24-72 hr follow-up with a physician) explicit to the patient. Patients with a primary complaint of chest pain and no known coronary artery disease who were being considered for EDOU admission were eligible. Patient-clinician pairs were randomized to intervention (decision aid plus risk estimate) or usual care (no decision aid, no risk estimate). We used patient surveys and 30-day phone follow-up to assess the primary outcome (patient knowledge regarding their short-term risk for ACS), patient satisfaction, safety (delayed or missed ACS defined as acute myocardial infarction, ventricular arrhythmia, cardiogenic shock, or cardiac/unknown death), and resource use. Analysis was by intention to treat. Results: The 205 patients had the following characteristics: mean age (SD) 54.7 (11.8), 59% female, 36% history of HTN and 10% history of diabetes. Compared with usual care patients (n=104), patients receiving the decision aid (n=101) less frequently decided to be admitted to the EDOU for cardiac stress testing (58% vs 77%, absolute difference = 19%, 95% CI 6, 31), had a lower rate of stress testing (74% vs 90%, absolute difference = 16%, 95% CI 6, 26), greater knowledge of their exact pre-test probability of ACS (25% vs 1%, absolute difference = 24%, 95% CI 15, 33) and reported greater satisfaction with the decision-making process (strongly agree: 61% vs 40%, absolute difference = 21%, 95% CI 7, 33). There were no cases of delayed or missed ACS in either arm. Conclusion: Use of a decision aid in ED chest pain patients increased knowledge, increased satisfaction, and safely decreased resource use. In an era of health care reform, shared decision-making is a promising approach that may both increase patient engagement and decrease resource use.Funding(This research has received full or partial funding support from the American Heart Association, National Center)

Abstract Real-time location systems (RTLS) such as radio frequency identification (RFID) have been shown to improve safety, save cost, and increase patient satisfaction in a healthcare setting especially in the emergency department (ED). Hospital administrators have realized the potential of these applications for improved workflow and operations and are positively adopting it despite the substantial implementation costs of such technologies. Our group has reported several studies with RFID data in the ED showing the relationship between RFID determined patient contact times and length of stay (LOS). In this study, we use ED RFID data to determine the relationship between the total LOS in ED to the overall patient satisfaction obtained from a survey report. ED survey results from 35 questions were obtained from patients after discharge. An observational cohort study was performed in the ED using RFID data from Jan 1 – Dec 31, 2017 matched with the ED survey participants. A total of 2595 survey data was used for logistic regression analysis with the RFID data. Results show that higher treatment LOS influenced overall patient satisfaction negatively. The results provide new insights into designing ideal patient-care team interactions to reduce overall LOS while providing optimal ED care, thereby improving patient satisfaction.

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.

The Joint Commission requires ongoing and focused provider performance evaluations (OPPEs/FPPEs). The authors aim to describe current approaches in emergency medicine (EM) and identify consensus-based best practice recommendations. An online survey was distributed to leaders in EM to gain insight into current practices. A modified Delphi approach was then used to develop consensus to recommend best practice. A variety of strategies are currently in use for OPPE/FPPE. “Peer reviewed cases with opportunity for improvement” was identified as a preferred metric for OPPE. Although the preference was for use of peer review in OPPE, a consistent and standard adoption of robust internal care review processes is needed to establish expected norms. National benchmarking is not available currently. This was a limited survey of self-identified leaders, and there is an opportunity for additional engagement of leaders in EM to identify a unified approach that appropriately relates to patient outcomes.

Background: Patient satisfaction surveys are vital to measuring a patient’s experience of care. How scores of patients managed by emergency medicine (EM) residents change as residents progress through training is not known. Objectives: To evaluate whether EM residents’ patient satisfaction scores improve as residency training progresses, similar to clinical skill improvement. Methods: A retrospective cross-sectional study evaluated the correlation of patient satisfaction scores with EM resident year of training from 2015 through 2017. We evaluated for a change in score over time for the 4 “physician questions” and the “overall” score. Results: We evaluated 1684 Press Ganey surveys linked to 40 EM resident physicians during the study period. The mean top box scores for the 4 physician questions (concern for comfort [ P = .72], courtesy [ P = .55], informative about treatment [ P = .46], and listening [ P = .91]) and overall assessment of emergency department care ( P = .51) were not significantly improved over the course of resident. Conclusion: We did not observe a difference in EM residents’ patient experience scores as their level of training progressed. Comprehensive patient experience training for residents might be needed.

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.