Real-time Capacity Research Articles

Results from HART-c: innovations in prehospital triage for acute cardiac symptoms, a multicentre prospective study

Abstract Introduction Cardiac symptoms are one of the most prevalent reasons for emergency department (ED) visits [1], however most of these patients do not have acute cardiovascular disease. This leads to ED overcrowding which subsequently leads to worse patient outcomes and increased costs [2,3]. Attempts to reduce overcrowding have focused mostly on in-hospital triage. The Hollands-midden Acute Regional Triage – cardiology (HART-c) study uses a newly developed triage platform which includes live monitoring, real-time admission capacity, in-hospital data and cardiologist consultation for improved prehospital triage. Purpose The HART-c study aims to safely increase the percentage of patients with cardiac symptoms not referred to the hospital after emergency medical service (EMS) consultation. Methods Patients aged 18 years or older visited by the EMS for cardiac symptoms were included in the region Hollands-Midden from September 2019 till March 2020 (non-COVID period) and compared with the year earlier. Patients were excluded when primary PCI was indicated. EMS consultation consisted of medical history, physical examination, vital parameters and ECG. All data were transferred to a newly developed platform combining pre-hospital data, shown in real-time, and hospital data, such as medical records and admission capacity. The paramedic contacted an on-call triage cardiologist and decided whether admission was necessary and, if so, which regional hospital was most appropriate (figure 1). The study objective was defined as the percentage of patients not referred to the hospital after EMS consultation. Safety of the triage method was defined in the non-referred patients in the intervention as the percentage of MACE (death and acute coronary syndrome) 30 days after non-referral. Results In the intervention group 1755 patients (age 69±15 years, 53% men), and in the control group 1629 patients (age 68±15 years, 53% men) were consulted by the EMS during the HART-c study. In the intervention group 11.4% of patients consulted to the EMS were left at home, compared to 5.5% in the control group (figure 2). Logistic regression was performed to evaluate the effect of the triage intervention. The model was corrected for gender, age and seasonal changes. The chance of being left at home after EMS consultation was 2.29 (95% CI 1.73–3.02, p<0.001) times higher in the intervention group compared to the control. All patients left at home in the intervention group and their GP's were contacted for adverse events, after case-by-case review the MACE rate was <1%. Furthermore a decrease in interhospital transfers was seen, from 206 in the intervention to 173 in the control. Conclusion Implementation of an innovative triage method successfully increased the percentage of patients with cardiac symptoms safely left at home. Funding Acknowledgement Type of funding sources: None. Triage without (A) and with (B) platformNon-referral in intervention and control

Control of the Distributed Hybrid Energy Storage System Considering the Equivalent SOC

A hybrid energy storage system (HESS) consists of two or more types of energy storage components and the power electronics circuit to connect them. Therefore, the real-time capacity of this system highly depends on the state of the system and cannot be simply evaluated with traditional battery models. To tackle this challenge, an equivalent state of charge (ESOC) which reflects the remaining capacity of a HESS unit in a specific operation mode, is proposed in this paper. Furthermore, the proposed ESOC is applied to the control of the distributed HESS which contains several units with their own local targets. To optimally distribute the overall power target among these units, a sparse communication network-based hierarchical control framework is proposed. This framework considers the distributed control and optimal power distribution in the HESS from two aspects - the power output capability and the ESOC balance. Based on the primary droop control, the total power is allocated according to the maximum output capacity of each unit, and the secondary control is used to adjust the power from the perspective of ESOC balance. Therefore, each energy storage unit can be controlled to meet the local power demand of the microgrid. Simulation results based on MATLAB/Simulink verify the effectiveness of the application of the proposed equivalent SOC.

Recorp: Receiver-oriented Policies for Industrial Wireless Networks

Future Industrial Internet-of-Things (IIoT) systems will require wireless solutions to connect sensors, actuators, and controllers as part of high data rate feedback-control loops over real-time flows. A key challenge in such networks is to provide predictable performance and adaptability in response to link quality variations. We address this challenge by developing RECeiver ORiented Policies (Recorp), which leverages the stability of IIoT workloads by combining offline policy synthesis and run-time adaptation. Compared to schedules that service a single flow in a slot, Recorp policies share slots among multiple flows by assigning a coordinator and a list of flows that may be serviced in the same slot. At run-time, the coordinator will execute one of the flows depending on which flows the coordinator has already received. A salient feature of Recorp is that it provides predictable performance: a policy meets the end-to-end reliability and deadline of flows when the link quality exceeds a user-specified threshold. Experiments show that across IIoT workloads, policies provided a median increase of 50% to 142% in real-time capacity and a median decrease of 27% to 70% in worst-case latency when schedules and policies are configured to meet an end-to-end reliability of 99%.

Feedback linearization-based MIMO model predictive control with defined pseudo-reference for hydrogen regulation of automotive fuel cells

Proton exchange membrane fuel cells (PEMFCs) possess the benefits of high conversion efficiency, low operation noise and no pollution which are considered as potential power solutions for the automotive application. The supply and recirculation of hydrogen determines the output performance of PEMFC that is required to be well regulated. The hydrogen excess ratio (HER) is asked to locate at appropriate operating points and the pressure difference between anode and cathode should be limited within a reasonable range. In this paper, a pseudo-reference-based multiple-input multiple-output (MIMO) model predictive control (MPC) integrated with the feedback linearization is proposed for the HER regulation and the pressure balance of electrodes. The nonlinear hydrogen delivery system model is developed which consists of two actuators: flow control valve and hydrogen circulating pump. To address the high nonlinearity of the hydrogen delivery system, the feedback linearization is formulated. The MIMO MPC is then developed based on the feedback linearized model with the defined pseudo-reference which can effectively reduce the overshoot of HER. Comparing with the normal MPC based on one working-point first-order Taylor expansion, the more accurate hydrogen regulation performance of the proposed MPC can be achieved. The proposed MPC can effectively reduce the HER overshoot by 59.25% at the initial response phase with nearly same convergence time. Under the New European Driving Cycle (NEDC) simulation condition, the average overshoot reduction of anode pressure and HER can reach to 47.45% and 77.34% that demonstrates a well application prospect of the proposed MPC in the automotive PEMFC hydrogen regulation. A hardware in loop (HIL) experiment was finally conducted to show the real-time capacity of the proposed MPC method.

Probabilistic real-time deep-water natural gas hydrate dispersion modeling by using a novel hybrid deep learning approach

Computational Fluid Dynamic (CFD) has been widely used for the gas release and dispersion modeling, which however could not support real-time emergency response planning due to its high computation overhead. Surrogate models offer a potential alternative to rigorous computational approaches, however, as the point-estimation alternatives, the existing neural network-based surrogate models are not able to quantify the uncertainty of the released gas spatial concentration. This study aims to fill a gap by proposing an advanced hybrid probabilistic Convolutional-Variational Autoencoder-Variational Bayesian neural network (Conv-VAE-VBnn). Experimental study based on a benchmark simulation dataset was conducted. The results demonstrated the additional uncertainty information estimated by the proposed model contributes to reducing the harmful effect of too ‘confidence’ of the point-estimation models. In addition, the proposed model exhibits competitive accuracy with R2 = 0.94 compared and real-time capacity with inference time less than 1s. Latent size Nz = 2, noise σz=0.1 and Monte Carlo sampling number m = 500 to ensure the model’s real-time capacity, were also given. Overall, our proposed model could provide a reliable alternative for constructing a digital twin for emergency management during the exploration and exploitation of marine natural gas hydrate (NHG) in the near future.

Dynamic real-time capacity constrained routing algorithm for evacuation planning problem

<span>Usually, disasters occurred over a relatively short time in anytime and anywhere. Most occupancies haven’t absolute knowledge about the prevention or safety consciousness to deal with disasters. During disaster occurred, evacuation processes are conducted to save people life, and if there is no appropriate evacuation plan, the situation will become worse. Thus, finding optimal planning to evacuate the occupancy people is critical in many cases i.e. emergency evacuation. In this paper, a Dynamic Real-Time Capacity Constrained Routing (DRTCCR) Algorithm has been proposed and analyzed. Such algorithm will investigate the capacity constraints of the evacuation network in real-time by modelling the capacities at the time of series to improve current solutions of the evacuation planning problem. Such algorithm will produce an optimal solution for evacuation planning problem. Performance evaluation on many network models illustrates that the DRTCCR algorithm improves the previous evacuation planning by reducing the evacuation time as well as the computational cost. In addition, DRTCCR algorithm has the ability to recalculate and find out the optimal path dynamically in real-time irrespective the number of trapped people as well as the transportation network size. Analytical experiments have been done and illustrate the efficiency of the proposed algorithm.</span>

Real-time traffic incident detection based on a hybrid deep learning model

Small sample sizes and imbalanced datasets have been two difficulties in previous traffic incident detection-related studies. Moreover, real-time characteristics of incident detection models must be improved to satisfy the needs of traffic management. In this study, a hybrid model is proposed to address the above problems. In the proposed model, a generative adversarial network (GAN) is used to expand the sample size and balance datasets, and a temporal and spatially stacked autoencoder (TSSAE) is used to extract temporal and spatial correlations of traffic flow and detect incidents. Using a real-world dataset, the model is evaluated from different aspects. The results show that the proposed model, considering both temporal and spatial variables, outperforms some benchmark models. The model can both increase the incident sample size and balance the dataset. Furthermore, the sample selection method improves the real-time capacity of the detection.

Relation between uterine morphology and severity of primary dysmenorrhea.

Objective:This study aimed to evaluate whether uterine dimensions including uterine volume, uterine shape, uterine length, cervix length, and cervix thickness measurements have a role in the severity of primary dysmenorrhea in virgin girls.Materials and Methods:Enrollment included 90 virgin girls suffering from primary dysmenorrhea. The girls were divided into three groups according to the severity of dysmenorrhea, which was determined by the visual analog scale (VAS). Patients with VAS scores of 8-10 comprised the severe primary dysmenorrhea group (n=30), 4-7 the moderate primary dysmenorrhea group (n=30), and 1-3 the mild primary dysmenorrhea group (n=30). Uterine characteristics including uterine volume, uterine shape, uterine length, cervix length, and cervix thickness were measured by a high-resolution four-dimensional ultrasound device with real-time capacity. They were recorded to determine if they can be predictors of dysmenorrhea severity.Results:Girls with severe primary dysmenorrhea were more likely to complain of midline pain as opposed to mild and moderate cases with lateral or diffuse pain. None of the uterine characteristics on ultrasonography examination were significant for predicting the severity of primary dysmenorrhea. There were no significant positive correlations between the dysmenorrhea severity and uterine corpus length, cervix length, and uterine volume degree. Any combination of the measured uterine features was not predictive for determining the severity of dysmenorrhea.Conclusion:Ultrasonographic measurements of uterine dimensions in virgins have low accuracy for predicting the severity of pain in primary dysmenorrhea.

COVID 19 and Nepal: Identification of Critical Public Health Measures

The COVID-19 pandemic is unfolding at an unprecedented pace. The unprecedented threat provides an opportunity to emerge with robust health systems. Nepal has implemented several containment measures such as Rapid Response Team formulation; testing; isolation; quarantine; contact tracing;surveillance, establishment of COVID-19 Crisis Management Centre and designation of dedicated hospitals to gear up for the pandemic. The national public health emergency management mechanisms need further strengthening with the proactive engagement of relevant ministries; we need a strong, real-time national surveillance system and capacity building of a critical mass of health care workers; there is a need to further assess infection prevention and control capacity; expand the network of virus diagnostic laboratories in the private sector with adequate surge capacity;implement participatory community engagement interventions and plan for a phased lockdown exit strategy enabling sustainable suppression of transmission at low-level and enabling in resuming some parts of economic and social life.

Near real-time bedside detection of spinal cord ischaemia during aortic repair by microdialysis of the cerebrospinal fluid.

Spinal cord ischaemia (SCI) remains the most devastating complication after thoraco-abdominal aortic aneurysm (TAAA) repair. Its early detection is crucial if therapeutic interventions are to be successful. Cerebrospinal fluid (CSF) is readily available and accessible to microdialysis (MD) capable of detecting metabolites involved in SCI [i.e. lactate, pyruvate, the lactate/pyruvate ratio (LPR), glucose and glycerol] in real time. Our aim was to evaluate the feasibility of CSF MD for the real-time detection of SCI metabolites. In a combined experimental and translational approach, CSF MD was evaluated (i) in an established experimental large animal model of SCI with 2 arms: (a) after aortic cross-clamping (AXC, N = 4), simulating open TAAA repair and (b) after total segmental artery sacrifice (Th4-L5, N = 8) simulating thoracic endovascular aortic repair. The CSF was analysed utilizing MD every 15 min. Additionally, CSF was collected hourly from 6 patients undergoing open TAAA repair in a high-volume aortic reference centre and analysed using CSF MD. In the experimental AXC group, CSF lactate increased 3-fold after 10 min and 10-fold after 60 min of SCI. Analogously, the LPR increased 5-fold by the end of the main AXC period. Average glucose levels demonstrated a 1.5-fold increase at the end of the first (preconditioning) AXC period (0.60±0.14 vs 0.97±0.32 mmol/l); however, they decreased below (to 1/3 of) baseline levels (0.60±0.14 vs 0.19±0.13 mmol/l) by the end of the experiment (after simulated distal arrest). In the experimental segmental artery sacrifice group, lactate levels doubled and the LPR increased 3.3-fold within 30 min and continued to increase steadily almost 5-fold 180 min after total segmental artery sacrifice (P < 0.05). In patients undergoing TAAA repair, lactate similarly increased 5-fold during ischaemia, reaching a maximum at 6 h postoperatively. In 2 patients with intraoperative SCI, indicated by a decrease in the motor evoked potential of >50%, the LPR increased by 200%. CSF is widely available during and after TAAA repair, and CSF MD is feasible for detection of early anaerobic metabolites of SCI. CSF MD is a promising new tool combining bedside availability and real-time capacity to potentially enable rapid detection of imminent SCI, thereby maximizing chances to prevent permanent paraplegia in patients with TAAA.

Joint Optimization of Relay Deployment, Channel Allocation, and Relay Assignment for UAVs-Aided D2D Networks

Unmanned aerial vehicles (UAVs) can be deployed in the air to provide high probabilities of line of sight (LoS) transmission, thus UAVs bring much gain for wireless communication systems. In this paper, we study a UAVs-aided self-organized device-to-device (D2D) network. Relay deployment, channel allocation and relay assignment are jointly optimized, aiming to maximize the capacity of the relay network. On account of the coupled relationship between the three optimization variables, an alternating optimization approach is proposed to solve this problem. The original problem is divided into two sub-problems. The first one is that of optimizing the channel allocation and relay assignment with fixed relay deployment. Considering without central controller, a reinforcement learning algorithm is proposed to solve this sub-problem. The second sub-problem is that of optimizing the relay deployment with fixed channel allocation and relay assignment. Assuming no knowledge of channel model and exact positions of the communication nodes, an online learning algorithm based on real-time capacity is proposed to solve this sub-problem. By solving the two sub-problems alternately and iteratively, the original problem is finally solved. Simulation results show that the UAVs-aided D2D network can achieve a high capacity via the joint optimization of relay deployment, channel allocation, and relay assignment.

Real-Time Vehicle Detection Framework Based on the Fusion of LiDAR and Camera

Vehicle detection is essential for driverless systems. However, the current single sensor detection mode is no longer sufficient in complex and changing traffic environments. Therefore, this paper combines camera and light detection and ranging (LiDAR) to build a vehicle-detection framework that has the characteristics of multi adaptability, high real-time capacity, and robustness. First, a multi-adaptive high-precision depth-completion method was proposed to convert the 2D LiDAR sparse depth map into a dense depth map, so that the two sensors are aligned with each other at the data level. Then, the You Only Look Once Version 3 (YOLOv3) real-time object detection model was used to detect the color image and the dense depth map. Finally, a decision-level fusion method based on bounding box fusion and improved Dempster–Shafer (D–S) evidence theory was proposed to merge the two results of the previous step and obtain the final vehicle position and distance information, which not only improves the detection accuracy but also improves the robustness of the whole framework. We evaluated our method using the KITTI dataset and the Waymo Open Dataset, and the results show the effectiveness of the proposed depth completion method and multi-sensor fusion strategy.

Future Advances in Neurolaryngology.

Neurolaryngology as a subspecialty of laryngology has developed considerably in the last four decades with more laryngologists, neurologists, speech and swallow therapists, and neurophysiologists taking interest in the field. The North American and Japanese laryngology societies have increasingly focused on conditions which are mainly concerned with aberrations of the nervous system affecting the larynx directly or indirectly. In the last few years, societies in Europe and the Asia-Pacific have also recognized the need to collaborate both within their organizations and with other societies globally. Cross-border pollination of ideas has increasingly become easier and with the aid of technology - almost seamless with real-time capacity to share operating experience, lectures, and panel discussions. The future advances in neurolaryngology will require incremental improvements in processes of diagnostics, objectivization (where possible) of pathology, standardization of treatments with comparison of results using accepted patient-based tests, investigations and imaging where possible. Ultimately, from the contributions in the previous chapters, it is fairly obvious that many conditions are still poorly understood and therefore management becomes more symptom based rather than dealing with the root cause of the problem. An understanding of the physiology of vocalization, swallow, and breathing beyond a rudimentary acceptance of many towards the vagus nerve and other neural factors may help understand what has otherwise been a rather simplistic approach to one of the most complex parts of the human body, essential to life and equally important - the quality of life. In this chapter, we aim to look at where advances in neurolaryngology may and perhaps will take place. We will look at the potential of better imaging modalities, neurophysiological testing and physiology of the brain. Tests and treatments currently in use may require some refinements or be possibly abandoned and replaced with more effective ones that can demonstrate a difference in the management of various patient groups. The future is hard to predict, and the rate of advancement equally so, but given the rate at which information technology, artificial intelligence, and basic science research are progressing, neurolaryngology may indeed have its welcome boost in the not too distant future.

Uniform State-of-Charge Control Strategy for Plug-and-Play Electric Vehicle in Super-UPS

To prevent electric vehicles (EVs) from overcharging and discharging in a Super-uninterrupted power system (UPS) and to evaluate the influence of plug and play of EVs on the voltage of direct-current (dc) bus, a sliding-mode control method based on the multi-agent technology is employed in this article, and the output voltage, current, and the state of charge (SoC) of each EV's lithium battery are considered as the control object. The control design considers the current unitary value of each EV's lithium battery connected to a Super-UPS, the battery's SoC, and the average SoC of their adjacent EVs' lithium batteries. To prevent chattering and to consider the stability and authenticity of simulation, an improved sliding-mode function is presented, and the real-time capacities of EVs' lithium batteries are taken into account. Finally, the voltage stability of dc bus and the consistency of SoC of EVs' lithium batteries connected to Super-UPS are achieved by the new control scheme. The reliability and effectiveness of the proposed new control design method are confirmed by simulations.

A Novel Double-Quadrant SoC Consistent Adaptive Droop Control in DC Microgrids

This brief uses a double-quadrant state-of-charge (SoC) consistent adaptive droop control method for extending the service life of distributed energy storage units (DESUs). During charging, the power absorbed by DESU is inversely proportional to its SoC. On the other hand, during discharging, the power emitted by DESU is proportional to its SoC. The SoC of DESUs and the power absorbed or emitted by the DESUs tend to be consistent under charging and discharging conditions. To compensate for the direct-current (DC) bus voltage change caused by droop control during charging and discharging of DESUs, this brief proposes a novel double-quadrant SoC consistent adaptive droop control method which is based on multi-agent approach and considers the real-time capacities of DESUs. The reliability of the proposed novel control design method is confirmed by simulations.

A framework for application of dynamic line rating to aluminum conductor steel reinforced cables based on mechanical strength and durability

Dynamic line rating can be described as a method of overloading the power line within reliability and safety limits. Power line’s loading limits can be increased, if its temperature is controlled to be below the maximum allowable conductor temperature, which is defined by the grid regulations. Dynamic rating brings additional uncertainties and risks to the grid operation due to high variability of weather conditions, which plays an essential role in determining real-time capacity limits. Power lines often are under the influence of risk factors related to power system performance, however, they could also be subjected to additional risks related to their mechanical structure. Overhead lines, which are composed of more than one stranded material, are exposed to increasing mechanical stress due to differences in thermal expansion characteristics of different materials. The reliability analysis of transient expansion/shrinkage of the material has identified the risks to the conductor mechanical strength that are associated with dynamic heating and cooling.This study determines an optimal dynamic line rating application, which not only would take into account electrical properties of the system and economic benefits, but would also minimize the aging of steel reinforced aluminum overhead lines. Alternatively to hourly line rating adjustment, 2 h, 3 h and 4 h ratings are suggested as possible way to decrease impact of DLR on conductor mechanical durability. Comparing the mechanical durability and cost benefits between different frequencies of loading limit adjustments, allows suggesting improvements to dynamic line rating application.

Motion Simulator System for Rendezvous and Docking Based on RTX

Motion simulator system for rendezvous and docking needs high precision and high real-time capacity. The main structure, crucial indexes and control system structure are introduced at first. Software demand is also analyzed, software is modular designed, makes use of fine interface of Windows, use real-time processing ability of RTX and high precision of control algorithm. The experimental results show that the motion simulator system for rendezvous and docking based on RTX have the high precision and dynamic real-time capacity, which can satisfies the demand of application.

Availability of Bedside and Laboratory Testing for Carbon Monoxide Poisoning in the Upper Midwestern United States.

IntroductionThe objective of this study was to assess the ability to test patients for carbon monoxide (CO) exposure in all hospitals in three United States (U.S.) Midwestern states.MethodsWe surveyed hospitals in three states. Telephone queries assessed processes for measuring carboxyhemoglobin, including capacity for real-time vs send-out testing. Facilities were separated based on their location’s population size for further analysis. Descriptive statistics are reported.ResultsOf the 250 hospitals queried, we ultimately excluded 25. Nearly all (220, 97.8%) reported a process in place to test for CO exposure. Over 40% (n=92) lacked real-time testing. Testing ability was positively associated with increasing population size quartile (range 32.6% – 100%). Hospitals in the lowest-quartile population centers were more likely to report that they were unable to test in real time than those in the largest-quartile population centers (67.4% vs 0%).ConclusionIn a large geographic region encompassing three states, hospital-based and real-time capacity to test for CO exposure is not universal. Hospitals in smaller population areas are more likely to lack real-time testing or any testing at all. This may have significant public health, triage, and referral implications for patients.

Highly Paralleled Low-Cost Embedded HEVC Video Encoder on TI KeyStone Multicore DSP

Although HEVC, the emerging video coding standard, has doubled the coding performance of its predecessor H.264/AVC, its significantly increased computational complexity imposes great obstacles for HEVC encoders to be employed in real-time applications with embedded processors, such as digital signal processors (DSPs). In this paper, a TI Keystone multicore TMS320C6678 DSP-based highly paralleled low-cost fast HEVC encoding solution is well designed and implemented. First, the overall structure of HEVC encoder with CTU-level parallelism is re-designed to well support the encoding parallelism, with full consideration of the hardware characteristics. Second, a low-delay and low-memory multicore data transmission mechanism is proposed to reduce the latency of data access between internal L2 memory and external DDR3. Third, the encoding bottlenecks, i.e., the most time-consuming encoding modules, are identified and optimized for acceleration with TI powerful C6000 SIMD instructions. Experimental results show that our proposed HEVC encoder on TI TMS320C6678 DSPs can significantly improve the real-time capacity with tolerable performance loss, 0.93 dB performance loss under on average 465.50 times speedup as compared to CPU-based HM reference software, more specifically, which makes it desirable in power-constrained real-time video applications.

Effects of Real-time EMS Direction on Optimizing EMS Turnaround and Load-balancing Between Neighboring Hospital Campuses

Background: Implemented in September 2017, the “nurse navigator program” identified the preferred emergency department (ED) destination within a single healthcare system using real-time assessment of hospital and ED capacity and crowding metrics. Objective: The primary objective of the navigator program was to improve load-balancing between two closely situated emergency departments, both of which feed into the same inpatient facilities of a single healthcare system. A registered nurse in the hospital command center made real-time recommendations to emergency medical services (EMS) providers via radio, identifying the preferred destination for each transported patient based on such factors as chief complaint, ED volume, and waiting room census. The destination decision was made via the utilization of various real-time measures of health system capacity in conjunction with existing protocols dictating campus-specific clinical service availability. The objective of this study was to evaluate the efficacy of this real-time ambulance destination direction program as reflected in changes to emergency medical services (EMS) turnaround time and the incidence of intercampus transports. Methods: A before-and-after time series was performed to determine if program implementation resulted in a change in EMS turnaround time or incidence of intercampus transfers. Results: Implementation of the nurse navigator program was associated with a statistically significant decrease in EMS turnaround times for all levels of dispatch and transport at both hospital campuses. Intercampus transfers also showed significant improvement following implementation of the intervention, although this effect lagged behind implementation by several months. Conclusion: A proactive approach to EMS destination control using a nurse navigator with access to real-time hospital and ED capacity metrics appears to be an effective method of decreasing EMS turnaround time.