Formal and informal hospital emergency management practices: Managing for safety and performance amid crisis.

BACKGROUND: Although formal preparedness for unexpected crises has long been a concern of health care policy and delivery, many hospitals struggled to manage staff and equipment shortages, precarious finances, and supply chain disruptions among other difficulties during the COVID-19 pandemic. Our purpose was to analyze how hospitals used formal and informal emergency management practices to maintain safe and high-quality care while responding to crisis. METHODS: We conducted a qualitative study based on 26 interviews with hospital leaders and emergency managers from 12 U.S. hospitals purposively sampled to vary along geographic location, urban/rural delineation, size, resource availability, system membership, teaching status, and performance levels among other characteristics. RESULTS: In order to manage staff, space, supplies, and systems related challenges, hospitals engaged formal and informal practices around planning, teaming, and exchanging resources and information.Relying solely only on formal or informal practices proved inadequate, especially when prespecified plans, the incident command structure, and existing contracts and communication platforms failed to support resilient response. We identified emergent capabilities - imaginative planning, recombinant teaming, and transformational exchange - through which hospitals achieved harmonious interplay between the formal and informal practices of emergency management that supported safe care and resilience amid crisis. CONCLUSION: Managing emergent challenges for and amid crisis calls for health care delivery organizations to engage creative planning processes, enable motivated workers with diverse skill sets to team up, and establish rich inter- and intra-organizational partnerships that support vital exchange.

5G-enabled UAVs for energy-efficient opportunistic networking.

The article explores the potential of 5G-enabled Unmanned Aerial Vehicles (UAVs) in establishing opportunistic networks to improve network resource management, reduce energy use, and boost operational efficiency. The proposed framework utilizes 5G-enabled drones and edge command and control software to provide energy-efficient network topologies. As a result, UAVs operate edge computing for efficient data collecting and processing. This invention enhances network performance using modern Artificial Intelligence (AI) algorithms to improve UAV networking capabilities while conserving energy. An empirical investigation shows a significant improvement in network performance measures when using 5G technology compared to older 2.4 GHz systems. The communication failure rate decreased by 50 %, from 12 % to 6 %. The round-trip time was lowered by 58.3 %, from 120 Ms to 50 Ms. The payload efficiency improved by 13.3 %, dropping from 15 % to 13 %. The data transmission rate increased significantly from 1 Gbps to 5 Gbps, representing a 400 % boost. The numerical findings highlight the significant impact that 5G technology may have on UAV operations. Testing on a 5G-enabled UAV confirms the effectiveness of our technique in several domains, including precision agriculture, disaster response, and environmental monitoring. The solution seriously improves UAV network performance by reducing energy consumption and using peripheral network command-and-control software. Our results emphasize the versatile networking capacities of 5G-enabled drones, which provide new opportunities for UAV applications.

Prescribed-time command filtered control for a class uncertain nonlinear systems.

This article delves into the intricate challenge of implementing prescribed-time command filtered control in the context of uncertain nonlinear systems. Firstly, a prescribed-time function is defined to lay the groundwork for subsequent controller design. Subsequently, a novel prescribed-time command Filtered controller is proposed for high-order nonlinear systems featuring unknown parameters. This controller guarantees swift error convergence within a predefined time range, with the added capability of periodic error convergence to zero during subsequent controller operations. A pivotal innovation in this study lies in the controller's design, which remains unaffected by the system's initial conditions. This unique feature enables the prescribed time to be flexibly set within physical constraints, diverging markedly from conventional finite-time control theory. Theoretical analysis has conclusively shown that the controller achieves full-state tracking error convergence within the specified time frame. The efficacy of the research findings is substantiated through two simulation cases, underscoring a substantial contribution to the refinement and adaptability of nonlinear system control theory.

Design and Implementation of an Intensive Care Unit Command Center for Medical Data Fusion.

The rapid advancements in Artificial Intelligence of Things (AIoT) are pivotal for the healthcare sector, especially as the world approaches an aging society which will be reached by 2050. This paper presents an innovative AIoT-enabled data fusion system implemented at the CMUH Respiratory Intensive Care Unit (RICU) to address the high incidence of medical errors in ICUs, which are among the top three causes of mortality in healthcare facilities. ICU patients are particularly vulnerable to medical errors due to the complexity of their conditions and the critical nature of their care. We introduce a four-layer AIoT architecture designed to manage and deliver both real-time and non-real-time medical data within the CMUH-RICU. Our system demonstrates the capability to handle 22 TB of medical data annually with an average delay of 1.72 ms and a bandwidth of 65.66 Mbps. Additionally, we ensure the uninterrupted operation of the CMUH-RICU with a three-node streaming cluster (called Kafka), provided a failed node is repaired within 9 h, assuming a one-year node lifespan. A case study is presented where the AI application of acute respiratory distress syndrome (ARDS), leveraging our AIoT data fusion approach, significantly improved the medical diagnosis rate from 52.2% to 93.3% and reduced mortality from 56.5% to 39.5%. The results underscore the potential of AIoT in enhancing patient outcomes and operational efficiency in the ICU setting.

High-performance finite-time adaptive control strategy for three-level T-type converters.

Three-level T-type converters are necessary interfaces for distributed energy resources to interact with the public grid. Naturally, designing a control strategy, featuring superior dynamics and strong robustness, is a promising solution to guarantee the efficient operation of converters. This article presents an improved finite-time control (IFTC) strategy for three-level T-type converters to enhance the dynamic performance and anti-disturbance capacity. The IFTC strategy integrates a dual-loop structure to regulate the dc-link voltage and grid currents. Specifically, the voltage regulation loop employs a finite-time adaptive controller that can counteract load disturbances without relying on current sensors. In the current tracking loop, finite-time controllers combined with a command filter are constructed to obtain fast and accurate current tracking. In this loop, the command filter is utilized to avoid calculating the derivative of current references. Theoretical analysis and experimental results demonstrate the IFTC strategy's effectiveness.

Task-based EEG and fMRI paradigms in a multimodal clinical diagnostic framework for disorders of consciousness.

Disorders of consciousness (DoC) are generally diagnosed by clinical assessment, which is a predominantly motor-driven process and accounts for up to 40 % of non-communication being misdiagnosed as unresponsive wakefulness syndrome (UWS) (previously known as prolonged/persistent vegetative state). Given the consequences of misdiagnosis, a more reliable and objective multimodal protocol to diagnosing DoC is needed, but has not been produced due to concerns regarding their interpretation and reliability. Of the techniques commonly used to detect consciousness in DoC, task-based paradigms (active paradigms) produce the most unequivocal result when findings are positive. It is well-established that command following (CF) reliably reflects preserved consciousness. Task-based electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) can detect motor-independent CF and reveal preserved covert consciousness in up to 14 % of UWS patients. Accordingly, to improve the diagnostic accuracy of DoC, we propose a practical multimodal clinical decision framework centered on task-based EEG and fMRI, and complemented by measures like transcranial magnetic stimulation (TMS-EEG).

Descending motor command to prime mover of dependent finger induces tactile gating in target and distant non-target finger.

This study examined whether tactile gating induced by the descending motor command to one finger spreads out to the other fingers to which the command is not delivered and whether this gating is dependent on the target finger to which the command is delivered. The change in perceptual threshold to the digital nerve stimulation of one finger induced by tonic contraction of the first dorsal interosseous or abductor digiti minimi muscle was examined. The perceptual threshold to the digital nerve stimulation of the thumb or little finger was increased by tonic contraction of the abductor digiti minimi muscle. This finding indicates that the descending motor command to the prime mover of the little finger abduction induces tactile gating not only in the finger to which the command is delivered but also in the other finger to which the command is not delivered. Tonic contraction of the first dorsal interosseous muscle did not change the perceptual threshold to the digital nerve stimulation in any finger. This finding means that tactile gating occurs particularly when the descending motor command is delivered to the dependent finger. Spreading out of tactile gating of one finger, to which the descending motor command is not delivered, is likely mediated by surround inhibition.

Mechanosensory and command contributions to the Drosophila grooming sequence.

Flies groom in response to competing mechanosensory cues in an anterior-to-posterior order using specific legs. From behavior screens, we identified a pair of cholinergic command-like neurons, Mago-no-Te (MGT), whose optogenetic activation elicits thoracic grooming by the back legs. Thoracic grooming is typically composed of body sweeps and leg rubs in alternation, but clonal analysis coupled with amputation experiments revealed that MGT activation only commands the body sweeps: initiation of leg rubbing requires contact between the leg and thorax. With new electron microscopy (EM) connectome data for the ventral nerve cord (VNC), we uncovered a circuit-based explanation for why stimulation of posterior thoracic mechanosensory bristles initiates cleaning by the back legs. Our previous work showed that flies weigh mechanosensory inputs across the body to select which part to groom, but we did not know why the thorax was always cleaned last. Here, the connectome for the VNC enabled us to identify a pair of GABAergic inhibitory neurons, UMGT1, that receives diverse sensory inputs and synapses onto both MGT and components of its downstream circuits. Optogenetic activation of UMGT1 suppresses thoracic cleaning, representing a mechanism by which mechanosensory stimuli on other body parts could take precedence in the grooming hierarchy. We also anatomically mapped the pre-motor circuit downstream of MGT, including inhibitory feedback connections that may enable rhythmicity and coordination of limb movement during thoracic grooming. The combination of behavioral screens and connectome analysis allowed us to identify a neural circuit connecting sensory-to-motor neurons that contributes to thoracic grooming.

Optimization method for human-robot command combinations of hexapod robot based on multi-objective constraints.

Due to the heavy burden on human drivers when remotely controlling hexapod robots in complex terrain environments, there is a critical need for robot intelligence to assist in generating control commands. Therefore, this study proposes a mapping process framework that generates a combination of human-robot commands based on decision target values, focusing on the task of robot intelligence assisting drivers in generating human-robot command combinations. Furthermore, human-robot state constraints are quantified as geometric constraints on robot motion and driver fatigue constraints. By optimizing and filtering the feasible set of human-robot commands based on human-robot state constraints, instruction combinations are formed and recommended to the driver in real-time, thereby enhancing the efficiency and safety of human-machine coordination. To validate the effectiveness of the proposed method, a remote human-robot collaborative driving control system based on wearable devices is designed and implemented. Experimental results demonstrate that drivers utilizing the human-robot command recommendation system exhibit significantly improved robot walking stability and reduced collision rates compared to individual driving.

Command filter-based event-triggered control for stochastic MEMS gyroscopes with finite-time prescribed performance.

This paper proposes an adaptive neural control strategy for stochastic microelectromechanical system (MEMS) gyroscopes, aiming to achieve a prescribed performance in a finite time. The radial basis function neural network is introduced to address the system's unknown nonlinear dynamics and stochastic disturbances. Then, the technology of finite-time prescribed performance function, along with the method of command-filtered backstepping design, is utilized to ensure both transient and steady-state performance and simultaneously solve the problem of "explosion of complexity." Moreover, a switching threshold event-triggered control law is proposed to cut down on communication resources and eliminate corresponding parametric inequality restrictions. The proposed adaptive state feedback control strategy is able to guarantee that the output tracking error converges to a prescribed, arbitrarily small residual set. Additionally, the closed-loop system's signals can be semi-globally ultimately uniformly bounded in probability. Finally, numerical simulations demonstrate the effectiveness and superiority of the proposed strategy.

Trend of mortality and length of stay in the emergency department following implementation of a centralized sepsis alert system.

Introduction: Sepsis alerts based on laboratory and vital sign criteria were found insufficient to improve patient outcomes. While most early sepsis alerts were implemented into smaller scale operating systems, a centralized new approach may provide more benefits, overcoming alert fatigue, improving deployment of staff and resources, and optimizing the overall management of sepsis. The objective of the study was to assess mortality and length of stay (LOS) trends in emergency department (ED) patients, following the implementation of a centralized and automated sepsis alert system. Methods: The automated sepsis alert system was implemented in 2021 as part of a hospital-wide command and control center. Administrative data from the years 2018 to 2021 were collected. Data included ED visits, in-hospital mortality, triage levels, LOS, and the Canadian Triage and Acuity Scale (CTAS). Results: Mortality rate for patients classified as CTAS I triage level was the lowest in 2021, after the implementation of the automated sepsis alert system, compared to 2020, 2019, and 2018 (p < 0.001). The Kaplan-Meier survival curve revealed that for patients classified as CTAS I triage level, the probability of survival was the highest in 2021, after implementation of the sepsis alert algorithm, compared to previous years (Log Rank, Mantel-Cox, χ²=29.742, p < 0.001). No significant differences in survival rate were observed for other triage levels. Conclusion: Implementing an automated sepsis alert system as part of a command center operation significantly improves mortality rate associated with LOS in the ED for patients in the highest triage level. These findings suggest that a centralized early sepsis alert system has the potential to improve patient outcomes.

Fixed-time command filtered output feedback control for twin-roll inclined casting system with prescribed performance.

The article investigates the issue of fixed-time control with adaptive output feedback for a twin-roll inclined casting system (TRICS) with disturbance. First, by using the mean value theorem, the nonaffine functions are decoupled to simplify the system. Second, radial basis function neural networks (RBFNNs) are introduced to approximate an unknown term, and a nonlinear neural state observer is created to handle the effects of unmeasured states. Then, the backstepping design framework is combined with prescribed performance and command filtering techniques to demonstrate that the scheme proposed in this article guarantees system performance within a fixed-time. The control design parameters determine the upper bound of settling time, regardless of the initial state of the system. Meanwhile, it ensures that all signals in the closed-loop system (CLS) remain bounded, and it can also maintain the tracking error within a predefined range within a fixed time. Finally, simulation results assert the effectiveness of the method.

Distributed finite-time bearing-based formation control for underactuated surface vessels with Levant differentiator.

In this paper, a distributed bearing-based formation control scheme with finite-time convergency is proposed for multiple underactuated surface vessels (USVs). By virtue of the guide point-based model transformation method, the dimension of underactuated tracking error dynamics can be reduced from three to two. This allows us to convert the actuator model to a fully form that matches dimension-reduced tracking error dynamics. Further, to reduce the computation complexity, a finite-time recruited filter is designed according to first-order Levant differentiator. At meanwhile, auxiliary error compensation signals are introduced to address unknowns stemming from filter process and system dynamics. Since merely relative-distances and inner bearings are utilized in the proposed bearing-based formation approaches, the proposed control scheme provides a feasible solution to achieve formation control under body-fixed frame. Finally, theoretical analysis and numerical simulations are presented to demonstrate the efficacy.

A century of exercise physiology: key concepts in neural control of the circulation.

Early in the twentieth century, Walter B. Cannon (1871-1945) introduced his overarching hypothesis of "homeostasis" (Cannon 1932)-the ability to sustain physiological values within a narrow range necessary for life during periods of stress. Physical exercise represents a stress in which motor, respiratory and cardiovascular systems must be integrated across a range of metabolic stress to match oxygen delivery to oxygen need at the cellular level, together with appropriate thermoregulatory control, blood pressure adjustments and energy provision. Of these, blood pressure regulation is a complex but controlled variable, being the function of cardiac output and vascular resistance (or conductance). Key in understanding blood pressure control during exercise is the coordinating role of the autonomic nervous system. A long history outlines the development of these concepts and how they are integrated within the exercise context. This review focuses on the renaissance observations and thinking generated in the first three decades of the twentieth century that opened the doorway to new concepts of inquiry in cardiovascular regulation during exercise. The concepts addressed here include the following: (1) exercise and blood pressure, (2) central command, (3) neurovascular transduction with emphasis on the sympathetic nerve activity and the vascular end organ response, and (4) tonic neurovascular integration.

Simple spike patterns and synaptic mechanisms encoding sensory and motor signals in Purkinje cells and the cerebellar nuclei.

Whisker stimulation in awake mice evokes transient suppression of simple spike probability in crus I/II Purkinje cells. Here, we investigated how simple spike suppression arises synaptically, what it encodes, and how it affects cerebellar output. In vitro, monosynaptic parallel fiber (PF)-excitatory postsynaptic currents (EPSCs) facilitated strongly, whereas disynaptic inhibitory postsynaptic currents (IPSCs) remained stable, maximizing relative inhibitory strength at the onset of PF activity. Short-term plasticity thus favors the inhibition of Purkinje spikes before PFs facilitate. In vivo, whisker stimulation evoked a 2-6 ms synchronous spike suppression, just 6-8 ms (∼4 synaptic delays) after sensory onset, whereas active whisker movements elicited broadly timed spike rate increases that did not modulate sensory-evoked suppression. Firing in the cerebellar nuclei (CbN) inversely correlated with disinhibition from sensory-evoked simple spike suppressions but was decoupled from slow, non-synchronous movement-associated elevations of Purkinje firing rates. Synchrony thus allows the CbN to high-pass filter Purkinje inputs, facilitating sensory-evoked cerebellar outputs that can drive movements.

Dynamic performance of rotor-side nonlinear control technique for doubly-fed multi-rotor wind energy based on improved super-twisting algorithms under variable wind speed.

The paper proposes a nonlinear controller called dual super-twisting sliding mode command (DSTSMC) for controlling and regulating the rotor side converter (RSC) of multi-rotor wind power systems that use doubly-fed induction generators. It was proposed that this controller be developed as an alternative to the direct power control (DPC), which makes use of a pulse width modulation (PWM) strategy to regulate the RSC's functioning. Overcoming the power/current quality issue with the proposed technique (DPC-DSTSMC-PWM) is characterized by great robustness and excellent performance. The designed strategy was contrasted with the standard method of control and other methods already in use. So, the unique proposed control strategy's robustness, performance, efficiency, and efficacy in enhancing system characteristics were tested and validated in Matlab/Simulink. In both tests, the proposed method resulted in significant improvements, reducing active power ripples by 83.33%, 57.14%, and 48.57% in the proposed tests. When compared with the traditional regulation method, the reduction rates of reactive power ripples are 64.06%, 52.47%, and 68.7% in the tests. However, in contrast to the conventional method, the proposed tests showed a decrease of between 72.46%, 50%, and 76.22% in the value of total harmonic distortion (THD) of the provided currents. These ratios show how effective the proposed plan is in ameliorating and enhancing aspects of the energy system.

The four most frequently diagnosed vector-borne diseases among service member and non-service member beneficiaries in the geographic combatant commands, 2010-2022.

Vector-borne diseases (VBDs) may pose an increased risk for U.S. service members during recurring military training exercises, operations, and response missions, in addition to residence in endemic regions within and outside the continental U.S. Prior MSMR reports address VBD surveillance, described by surveillance data for 23 reportable medical events (RMEs), among active duty and reserve component service members. This report covers a 13-year surveillance period, from January 2010 to December 2022, and provides linear trends of selected VBDs among Armed Forces service and non-service member beneficiaries diagnosed at installations within the Northern Command (NORTHCOM), Africa Command (AFRICOM), Central Command (CENTCOM), European Command (EUCOM), Indo-Pacific Command (INDOPACOM), or Southern Command (SOUTHCOM). Trends of only the 4 mostfrequently reported VBDs were evaluated, as Lyme disease, malaria, Rocky Mountain Spotted Fever (RMSF), and dengue fever comprised 90% (n=5,199) of all 23 VBDs (n=5,750) among Military Health System (MHS) beneficiaries documented as RMEs during the surveillance period.

Centering Health Equity in the Implementation of the Hospital Incident Command System: A Qualitative Case Comparison Study.

OBJECTIVE: Disasters exacerbate inequities in health care. Health systems use the Hospital Incident Command System (HICS) to plan and coordinate their disaster response. This study examines how 2 health systems prioritized equity in implementing the Hospital Incident Command System (HICS) during the coronavirus disease 2019 (COVID-19) pandemic and identifies factors that influenced implementation. METHODS: This is a qualitative case comparison study, involving semi-structured interviews with 29 individuals from 2 US academic health systems. Strategies for promoting health equity were categorized by social determinants of health. The Consolidated Framework for Implementation Research (CFIR) guided analysis using a hybrid inductive-deductive approach. RESULTS: The health systems used various strategies to incorporate health equity throughout implementation, addressing all 5 social determinants of health domains. Facilitators included HICS principles, external partnerships, community relationships, senior leadership, health equity experts and networks, champions, equity-stratified data, teaming, and a culture of health equity. Barriers encompassed clarity of the equity representative role, role ambiguity for equity representatives, tokenism, competing priorities, insufficient resource allocation, and lack of preparedness. CONCLUSIONS: These findings elucidate how health systems centered equity during HICS implementation. Health systems and regulatory bodies can use these findings as a foundation to revise the HICS and move toward a more equitable disaster response.

A command centre implementation before and during the COVID-19 pandemic in a community hospital.

INTRODUCTION: The objective of the study was to assess the effects of high-reliability system by implementing a command centre (CC) on clinical outcomes in a community hospital before and during COVID-19 pandemic from the year 2016 to 2021. METHODS: A descriptive, retrospective study was conducted at an acute care community hospital. The administrative data included monthly average admissions, intensive care unit (ICU) admissions, average length of stay, total ICU length of stay, and in-hospital mortality. In-hospital acquired events were recorded and defined as one of the following: cardiac arrest, cerebral infarction, respiratory arrest, or sepsis after hospital admissions. A subgroup statistical analysis of patients with in-hospital acquired events was performed. In addition, a subgroup statistical analysis was performed for the department of medicine. RESULTS: The rates of in-hospital acquired events and in-hospital mortality among all admitted patients did not change significantly throughout the years 2016 to 2021. In the subgroup of patients with in-hospital acquired events, the in-hospital mortality rate also did not change during the years of the study, despite the increase in the ICU admissions during the COVID-19 pandemic.Although the in-hospital mortality rate did not increase for all admitted patients, the in-hospital mortality rate increased in the department of medicine. CONCLUSION: Implementation of CC and centralized management systems has the potential to improve quality of care by supporting early identification and real-time management of patients at risk of harm and clinical deterioration, including COVID-19 patients.