Real-time Vital Signs Research Articles

Deep learning models for the prediction of acute postoperative pain in PACU for video‐assisted thoracoscopic surgery

BackgroundPostoperative pain is a prevalent symptom experienced by patients undergoing surgical procedures. This study aims to develop deep learning algorithms for predicting acute postoperative pain using both essential patient details and real-time vital sign data during surgery.MethodsThrough a retrospective observational approach, we utilized Graph Attention Networks (GAT) and graph Transformer Networks (GTN) deep learning algorithms to construct the DoseFormer model while incorporating an attention mechanism. This model employed patient information and intraoperative vital signs obtained during Video-assisted thoracoscopic surgery (VATS) surgery to anticipate postoperative pain. By categorizing the static and dynamic data, the DoseFormer model performed binary classification to predict the likelihood of postoperative acute pain.ResultsA total of 1758 patients were initially included, with 1552 patients after data cleaning. These patients were then divided into training set (n = 931) and testing set (n = 621). In the testing set, the DoseFormer model exhibited significantly higher AUROC (0.98) compared to classical machine learning algorithms. Furthermore, the DoseFormer model displayed a significantly higher F1 value (0.85) in comparison to other classical machine learning algorithms. Notably, the attending anesthesiologists' F1 values (attending: 0.49, fellow: 0.43, Resident: 0.16) were significantly lower than those of the DoseFormer model in predicting acute postoperative pain.ConclusionsDeep learning model can predict postoperative acute pain events based on patients' basic information and intraoperative vital signs.

Advancements in Flexible Electronics Fabrication: Film Formation, Patterning, and Interface Optimization for Cutting-Edge Healthcare Monitoring Devices.

Flexible electronics can seamlessly adhere to human skin or internal tissues, enabling the collection of physiological data and real-time vital sign monitoring in home settings, which give it the potential to revolutionize chronic disease management and mitigate mortality rates associated with sudden illnesses, thereby transforming current medical practices. However, the development of flexible electronic devices still faces several challenges, including issues pertaining to material selection, limited functionality, and performance instability. Among these challenges, the choice of appropriate materials, as well as their methods for film formation and patterning, lays the groundwork for versatile device development. Establishing stable interfaces, both internally within the device and in human-machine interactions, is essential for ensuring efficient, accurate, and long-term monitoring in health electronics. This review aims to provide an overview of critical fabrication steps and interface optimization strategies in the realm of flexible health electronics. Specifically, we discuss common thin film processing methods, patterning techniques for functional layers, interface challenges, and potential adjustment strategies. The objective is to synthesize recent advancements and serve as a reference for the development of innovative flexible health monitoring devices.

Development of wearable device and synchronized Mobile application to monitor vital signs in real time

AbstractSilent hypoxia is a complication that can lead to severe respiratory disorders at a time when people feel well, especially in Covid-19, and lung destruction if not intervened early. In this paper, a wearable device and a mobile application synchronized with the device were developed to measure people’s vitals and reduce the risk of silent hypoxia. The device consists of 2 parts: a wristband and a finger-clip, and includes a micro-controller, a bluetooth module, a Li-Po battery and various sensors to measure body temperature, pulse and SpO2 values. It can measure body temperature, pulse and SpO2 instantly or at certain programmable intervals like a holter device through the mobile app. These measured values can be recorded and various analyzes can be performed on them. Also the measurement results can be sent via an e-mail. When the vitals reach or exceed the predefined risk levels, a notification can be generated. The proposed device was evaluated in various performance tests and compared to a commercial pulse oximeter as well as alternatives in the literature. It was tested for connectivity and transmission, battery endurance and field testing. In the comparison test, error values of 1.8%, 3.5% and 0.6% were achieved in SpO2, pulse and temperature measurements, respectively. The device is both a measurement and holter instrument for body temperature, pulse and SpO2 values. With this feature, it can be actively used by both patients and their relatives in hospital or home environment.

Autonomous Triboelectric Smart Textile Sensor for Vital Sign Monitoring.

Wearable smart textile sensors for monitoring vital signs are fast, noninvasive, and highly desirable for personalized health management to diagnose health anomalies such as cardiovascular diseases and respiratory dysfunction. Traditional biosignal sensors, with power consumption issues, constrain the use of wearable medical devices. This study introduces an autonomous triboelectric smart textile sensor (AUTS) made of reduced graphene oxide/manganese dioxide/polydimethylsiloxane (RGO-M-PDMS) and polytetrafluoroethylene (TEFLON)-knitted silver electrode, offering promise for vital sign monitoring with self-powering, flexibility, and wearability. The sensor exhibits impressive output performance, with a sensitivity of 7.8 nA/kPa, response time of ≈40 ms, good stability of >15,000 cycles, stretchability of up to 40%, and machine washability of >20 washes. The AUTS has been integrated to the TriBreath respiratory belt for monitoring respiratory signals and pulse strap for pulse signals concurrently at different body pulse points. These sensors wirelessly transmitted the acquired biosignals to a smartphone, demonstrating the potential of a self-powered and real-time vital sign monitoring system.

Real-time Vital Signs Monitoring and Data Management Using a Low-Cost IoT-based Health Monitoring System

This study describes the creation and evaluation of a low-cost internet of things (IoT)-based health monitoring system for the continuous monitoring of vital signs such as temperature, pulse rate, oxygen saturation (SpO2) and blood pressure (BP) (both systolic and diastolic). Along with an organic light-emitting diode (OLED) display and an ESP8266 microcontroller, the system includes BP, non-contact temperature, SpO2 and electrocardiogram (ECG) sensors. Using the visual programming tool, Node-RED, the data from these sensors are gathered, processed and transmitted to the Google Cloud platform for archival and visualisation. The process involved mounting the sensors and microcontrollers on a special printed circuit board and designing the circuit with EasyEDA. The device measures systolic, diastolic and pulse rates from the BP sensor, as well as temperature, ECG and SpO2 values. The system works by using three push switches to read and display these values on demand. The gathered data are simultaneously shown on the OLED and sent to the Node-RED dashboard, where it is then sent to a Google Spreadsheet for archiving and analysis. This research article gives a thorough overview of the health monitoring system, the way it was implemented, and how it was successfully validated in a real-time setting. This study examines certain vital signs but additional health measures, such as respiration rate or glucose monitoring, could be included. Machine learning algorithms could also be used for predictive analytics. This would uncover data anomalies and trends early, improving healthcare management.

“Research on the construction of internet based aerobics competitive ability assessment model”

A vast field that focuses on restoring motor abilities through exercise is known as sports rehabilitation training, which is also sometimes referred to as recovery athletic exercise. A background in sporting recovery can assist competitors in getting back into peak physical condition, recovering from injuries, preventing additional damage, and better coping with the demands of regular practice. The importance of sports rehabilitation teaching for physical activity is analyzed and discussed at length throughout this research. The definition of sports therapy is presented first, followed by an examination of how it might be used to fitness. Make use of this information as a reference to discover how to include physical therapy in the workout plan you use for sports conditioning. Many tried-and-true algorithms for monitoring patients' vitals over time have emerged as a result of widespread use and advancements in IHM technology, which has led to an increase in the number of such algorithms. The purpose of this research is to investigate the ways in which intelligent health monitoring systems can be applied to the process of developing rehab methods for athletes. It not only provides the structure for a smart wellness terminal, but it also offers the usual ways for monitoring a moving object. Methods such as this include the optical flow strategy, the frame contrast technique, and the background difference method, to name just a few examples. The process of rehabilitation that an athlete went through in X city is presented as an example to show the processes that are included in an ideal study. According to the findings of the study, 92.6 percent of the athletes with injuries were able to make a full recovery with the support of rehabilitative physical exercise. The researchers were able to determine this by keeping close track on the athletes' vital signs in real time using sophisticated health monitoring equipment.

MiVitals–Mixed Reality Interface for Vitals Monitoring: A HoloLens based prototype for healthcare practices

In this paper, we introduce MiVitals—a Mixed Reality (MR) system designed for healthcare professionals to monitor patients in wards or clinics. We detail the design, development, and evaluation of MiVitals, which integrates real-time vital signs from a biosensor-equipped wearable, VitalitiTM. The system generates holographic visualizations, allowing healthcare professionals to interact with medical charts and information panels holographically. These visualizations display vital signs, trends, other significant physiological signals, and medical early warning scores in a comprehensive manner. We conducted a User Interface/User Experience (UI/UX) study focusing on novel holographic visualizations and interfaces that intuitively present medical information. This approach brings traditional bedside medical information to life in the real environment through non-contact 3D images, supporting rapid decision-making, vital pattern and anomaly detection, and enhancing clinicians' performance in wards. Additionally, we present findings from a usability study involving medical doctors and healthcare practitioners to assess MiVitals' efficacy. The System Usability Scale study yielded a score of 84, indicating that the MiVitals system has high usability.

A Novel Continuous Real-Time Vital Signs Viewer for Intensive Care Units: Design and Evaluation Study.

Clinicians working in intensive care units (ICUs) are immersed in a cacophony of alarms and a relentless onslaught of data. Within this frenetic environment, clinicians make high-stakes decisions using many data sources and are often oversaturated with information of varying quality. Traditional bedside monitors only depict static vital signs data, and these data are not easily viewable remotely. Clinicians must rely on separate nursing charts-handwritten or electric-to review physiological patterns, including signs of potential clinical deterioration. An automated physiological data viewer has been developed to provide at-a-glance summaries and to assist with prioritizing care for multiple patients who are critically ill. This study aims to evaluate a novel vital signs viewer system in a level 1 trauma center by subjectively assessing the viewer's utility in a high-volume ICU setting. ICU attendings were surveyed during morning rounds. Physicians were asked to conduct rounds normally, using data reported from nurse charts and briefs from fellows to inform their clinical decisions. After the physician finished their assessment and plan for the patient, they were asked to complete a questionnaire. Following completion of the questionnaire, the viewer was presented to ICU physicians on a tablet personal computer that displayed the patient's physiologic data (ie, shock index, blood pressure, heart rate, temperature, respiratory rate, and pulse oximetry), summarized for up to 72 hours. After examining the viewer, ICU physicians completed a postview questionnaire. In both questionnaires, the physicians were asked questions regarding the patient's stability, status, and need for a higher or lower level of care. A hierarchical clustering analysis was used to group participating ICU physicians and assess their general reception of the viewer. A total of 908 anonymous surveys were collected from 28 ICU physicians from February 2015 to June 2017. Regarding physicians' perception of whether the viewer enhanced the ability to assess multiple patients in the ICU, 5% (45/908) strongly agreed, 56.6% (514/908) agreed, 35.3% (321/908) were neutral, 2.9% (26/908) disagreed, and 0.2% (2/908) strongly disagreed. Morning rounds in a trauma center ICU are conducted in a busy environment with many data sources. This study demonstrates that organized physiologic data and visual assessment can improve situation awareness, assist clinicians with recognizing changes in patient status, and prioritize care.

Real-Time Monitoring of Neonatal Incubator Parameters Via Iot-Integrated Device: Design, Implementation, and Evaluation

The Internet of Things (IoT) has revolutionized healthcare solutions’ design, construction, and deployment. This research project focuses on developing an external device incorporating Internet of Things (IoT) technology for real-time monitoring of neonatal incubator parameters in sub-Saharan Africa. This IoT is intended to monitor real-time vital signs such as body temperature, humidity, pulse rate, oxygen concentration, air quality, and the sound level of the immediate environment and transmit the data via a mobile application. The Internet of Things has a micro controller for storing temporal sensor data. Temperature, humidity, and heart rate sensors monitor temperature, humidity, pulse oximetry, and heart rate, respectively, and a gas sensor detects harmful gases. The mic sensor detects abnormal sound levels around the incubator, while the Wi-Fi module provides the micro controller access to a Wi-Fi network. The sensor data is sent to a cloud-based platform that houses the data, analysis, and retrieval. This research paper contains the details of system design, implementation, and performance evaluation. The evaluation findings reveal the device to be trus table and accurate, providing neonate caregivers with valuable information.

Use of Machine Learning to Predict 30-Day Reutilization of Care for Patients with Sickle Cell Disease Treated for Vaso-Occlusive Crisis

Background Sickle cell disease (SCD) is an inherited disorder of red blood cells affecting millions of people worldwide. SCD is characterized by recurrent episodes of severe pain attacks, also called vaso-occlusive crisis (VOC), and are the most common reason for hospitalization. Approximately 90% of the hospital admissions are for pain treatment and the hospital readmission rate is alarmingly high, as 30-50% are re-admitted within 30 days. We previously were successful in developing machine learning models to predict pain using inpatient vital signs data as well as Apple Watch data in patients with SCD hospitalized for VOC. We now aimed to predict reutilization of care within 30 days in patients with SCD treated for a VOC. Methods Patients with SCD aged 18 years and above, who were admitted for a VOC to the day hospital or Duke University Hospital between April and June 2022, were eligible for this study. Following informed consent, demographics, SCD genotype, details from the hospitalization such as length of stay, pain scores, as well as vital signs measured per standard of care were collected from the electronic medical records including blood pressure, pulse rate, respiratory rate, oxygen saturation and temperature. Baseline vital signs during regular visits were collected 6 months before and after the hospitalization. We used the values of nearest neighbors to fill in the empty entries. The vital signs over the whole hospital stay were averaged and those numbers were used as the predictor values for the model. The primary outcome was reutilization of care defined as readmission within 30 days to the day hospital and/or hospital. The predictors were used to fit 3 different machine learning classification models for the prediction of reutilization of care: random forest, logistic regression, gradient boosting. By fitting random forest model on the whole dataset, we were able to rank all the features using mean decrease in impurities. To avoid overfitting, we only used the best four predictors which were diastolic blood pressure, pulse rate, respiratory rate and pain score.The performance of the machine learning models was evaluated using the following metrics: accuracy, precision, recall, F1 score and area under the receiver-operating-curve (AUC). Results Eighteen participants with SCD were included in this study. The median age at inclusion was 30 years (IQR 22-34). The majority of the participants had SCD genotype HbSS (68%), and all were Black of African-American. There were 10 participants treated at day hospital (56%), while the other 8 participants were admitted to the hospital with a median length of stay of 7.5 days (IQR 2.5-10). After discharge, 15 participants sought medical care at least once within 30 days (83%); 8 were hospitalized (44%), and 13 were readmitted to the day hospital (72%). This pilot study consisted of 88 vital sign data points across the 18 patients. The metrics of our best-performing machine learning model, the random forest model, were: accuracy 70%, precision 0.94, recall 0.71, F1 score 0.81 and AUC 0.61 (Figure 1 and 2). The difference in precisions and recalls for all the models reflects the class imbalance in the dataset. To test how the model will perform for independent data sets, we used 5-fold cross-validation, and the cross-validation accuracy was 66% with a standard deviation of 7.7%. Conclusion In this pilot study, our machine learning model was able to accurately predict health care reutilization within 30 days following discharge, with real-time vital signs data collected during clinic visits and hospital admissions in participants with SCD. Prediction of reutilization may help healthcare providers identify those at high risk and allow considerations for inpatient and outpatient strategies for patient management. Our next efforts include prediction of re-utilization from data collected from consumer wearables and in a larger number of participants.

A flexible and ultra-highly sensitive tactile sensor based on Mg-doped ZnO nanorods for human vital signs and activity monitoring

Development of high-performance nano-ZnO based piezoelectric tactile sensors with outstanding sensitivity, good linearity, flexibility, durability and biocompatibility has a great significance for smart robotics, human healthcare devices, smart sensors and electronic skins. A flexible piezoelectric pressure sensor with a sandwich structure was fabricated by using PI/Cu film as top and bottom electrodes, Mg-doped ZnO nanorods (MZO NRs) as sensing layer and polydimethylsiloxane polymer as charge coupling layer. The MZO NRs were synthesized by a one-step hydrothermal method and their structure, morphology and defect states were characterized by using scanning electron microscopy, atomic force microscope and x-ray photoelectron spectroscopy. The piezoelectric properties of the flexible tactile sensors based on MZO NRs hydrothermally synthesized from different Mg2+/Zn2+ molar ratios were investigated comparatively by evaluating their force response actions. The results indicate that the sensors exhibit a linear response in the range of 0–1 N. The device based on MZO NRs can achieve an ultra-high sensitivity of 3.1 V N−1 (308.5 mV kpa−1) due to higher piezoelectric coefficient of MZO NRs, which is about five times higher than that of the undoped ZnO NRs sensor. Moreover, the sensor shows excellent stability and durability by loading 2500 cycles of pressing. It is demonstrated that the sensor can used to accurately monitor human motion status and vital signs in real time, such as facial motion, joint movement, wrist pulse, cervical artery, and heartbeat.

Medical system based on thermal optical system and neural network

<span lang="EN-US">Military personnel in the training or operational phases always need constant medical examination, but the presence of efficient medical care is difficult to implement in real-time for such cases. A wireless system for thermal tracking of soldiers was proposed, as well as tracking their vital signs in real time. Thermal cameras are used with an optical system designed to increase the accuracy of the thermal images captured as the change in the electro-cardiogram, heart rate, and temperature measurements are measured using a specially designed circuit. The results from both the thermal system and the biometric system are combined and sent to a computer for analysis using a model prepared with neural network technology. The proposed system was tested, and a database was created for 127 males and 110 females during training and rest times. The neural network model achieved a response time of 85 seconds until the release of the final analysis, and the accuracy of the proposed tracking system is 96%. The main contribution of this paper is the design of an integrated portable system for rapid, in-field, real-time military medical diagnostics.</span>

Review on Analysis and Optimization of Gait Stroke Detection using Variable Sensors with Machine Learning Approach

Abstract: Gait monitoring is considered an important marker of impairment, disability and gait symmetry. This research aims to develop a real-time consumer health monitoring system based on IoT sensors and machine learning technology to detect health disorders such as the onset of stroke. The proposed consumer stroke prediction system consists of IoT-based gait monitoring sensors, real-time vital signs monitoring, and a machine learning-based disease prediction model that predicts impaired and healthy gait. This study is useful for post-stroke walking coordination rehabilitation and consumer health monitoring service.

A Safety Goggles for Dental Treatment That Can Monitor the Pulse

Objective: Using the safety goggles to protect and treat the process of fog, powder or laser injury to the eyes, reduce noise and thus reduce the patient's discomfort. At the same time, pulse frequency and vital signs of patients can be detected during treatment to improve the safety of treatment. Methods: The device is a safety goggles for dental treatment that can monitor the pulse, including a mirror frame, a protective lens mounted on one side of the frame and a first elastic band and a second elastic band located at both ends of the frame. The first elastic band and the second elastic band are connected and fixed together. The first elastic band is provided with a holding box body near the frame, and the top of the holding box body is provided with a power switch, an audio switch and a pulse monitoring switch. The body of the containing box is provided with a control chip, a power supply component, a pulse sensor, a memory and an audio component. The control chip is electrically connected with the memory, a pulse sensor, an audio component, a speaker and a power supply component, and the audio component is electrically connected with the first earplug and the second earplug through the data cable. Results: A kind of safety goggles for dental treatment which can monitor pulse was successfully designed. The device is equipped with safety goggles, so as to prevent the air fog and other dirt damage to the eyes; Pulse sensor was set to obtain pulse signal; Earplugs with extendable length are suitable for individual differences and can improve the sound insulation effect. Conclusion: The safety goggles can solve the problems of harsh light source equipped with dental treatment chair, easy to splash the eyes with aerosol during operation, and loud noise of operating instruments. In addition, it can also monitor patients' vital signs in real time during tooth implantation and extraction and prevent sudden symptoms.

2D-Materials-Based Wearable Biosensor Systems.

As an evolutionary success in life science, wearable biosensor systems, which can monitor human health information and quantify vital signs in real time, have been actively studied. Research in wearable biosensor systems is mainly focused on the design of sensors with various flexible materials. Among them, 2D materials with excellent mechanical, optical, and electrical properties provide the expected characteristics to address the challenges of developing microminiaturized wearable biosensor systems. This review summarizes the recent research progresses in 2D-materials-based wearable biosensors including e-skin, contact lens sensors, and others. Then, we highlight the challenges of flexible power supply technologies for smart systems. The latest advances in biosensor systems involving wearable wristbands, diabetic patches, and smart contact lenses are also discussed. This review will enable a better understanding of the design principle of 2D biosensors, offering insights into innovative technologies for future biosensor systems toward their practical applications.

My Real Avatar has a Doctor Appointment in the Wepital: A System for Persistent, Efficient, and Ubiquitous Medical Care

COVID-19 created a great deal of personal, social, and economic anxiety in the USA and across the globe and exposed the inadequacy of traditional medical systems in handling large-scale emergencies. While telemedicine and virtual visits have become popular as a result, they end once a visit is over, hence lacking data persistence and continuity in caring for patients. Using the design science research approach with support from the theory of affordances, this paper proposes the design of a medical system (called wepital) in which patients receive care through their real avatars, enabling hospitals and other medical centers to provide immediate care that can continue for as long as a patient needs it. Real avatars are digital representations of patients that embody their real-time vital signs and health information. We have created a functional prototype to demonstrate how the proposed design can work. To assess the usability of the design, we have used the prototype in an experiment to provide medical advice to patient volunteers. Based on a theory-based conceptual model, we collected survey data after the experiment to identify factors contributing to the success of such a system, as measured by patient satisfaction. We report the factors that significantly contribute to the patients’ satisfaction. As part of the application and policy implications of our work, we propose a nationwide system that could supplement and expand the capacity of medical systems at the national or even global level.

Heart Rate Detection using a Piezoelectric Ceramic Sensor: Preliminary results

Real-time vital signs monitoring, particularly heart rate, is essential in today's medical practice and research. Heart rate detection allows the doctor to monitor the patient's health status to provide immediate action against possible cardiovascular diseases. We present a possible alternative to traditional heart rate signal monitoring systems, a cardiac pulse system using low-cost piezoelectric signal identification. This system could benefit health care and develop continuous pulse waveform monitoring systems. This paper introduces a heartbeat per minute (BPM) cardiac pulse detection system based on a low-cost piezoelectric ceramic sensor (PCS). The PCS is placed under the wrist and adjusted with a silicone wristband to measure the pressure exerted by the radial artery on the sensor and thus obtain the patient's BPM. We propose a signal conditioning stage to reduce the sensor's noise when acquiring the data and make it suitable for real-time BPM visualization. As a comparison, we performed a statistical test to compare the low-cost PCS with types of traditional sensors, along with the help of 21 volunteers. Experimental results show that the data collected by the PCS, when used for heart rate detection, is highly accurate and close to traditional sensor measurements. Therefore, we conclude that the system efficiently monitors the cardiac pulse signal in BPM. Keywords: Heart rate; Piezoelectric, BPM; Pulse Detection.

VitalDB, a high-fidelity multi-parameter vital signs database in surgical patients

In modern anesthesia, multiple medical devices are used simultaneously to comprehensively monitor real-time vital signs to optimize patient care and improve surgical outcomes. However, interpreting the dynamic changes of time-series biosignals and their correlations is a difficult task even for experienced anesthesiologists. Recent advanced machine learning technologies have shown promising results in biosignal analysis, however, research and development in this area is relatively slow due to the lack of biosignal datasets for machine learning. The VitalDB (Vital Signs DataBase) is an open dataset created specifically to facilitate machine learning studies related to monitoring vital signs in surgical patients. This dataset contains high-resolution multi-parameter data from 6,388 cases, including 486,451 waveform and numeric data tracks of 196 intraoperative monitoring parameters, 73 perioperative clinical parameters, and 34 time-series laboratory result parameters. All data is stored in the public cloud after anonymization. The dataset can be freely accessed and analysed using application programming interfaces and Python library. The VitalDB public dataset is expected to be a valuable resource for biosignal research and development.

Improving PACU Throughput Using an Electronic Dashboard: A Quality Improvement Initiative

PurposePatients boarding in a 14-bed postanesthesia care unit (PACU) impacted throughput in a large university hospital in West Virginia. The PACU and operating room (OR) boarder rate in 2019 was 12% (n = 1241) and 5% (n = 503), respectively. The purpose of this initiative was to improve PACU throughput by developing an efficient and evidence-based handoff communication process between the PACU and the medical-surgical floors. DesignThe design of the project was evidence-based quality improvement. MethodsAn interprofessional team created a nursing patient dashboard in the electronic medical record to be used during handoff communication between the PACU and medical-surgical floor nurses. The dashboard displayed real-time vital signs, patient history, medications, and laboratory results. When the patient met Aldrete score requirements for transfer, PACU nurses notified the floor that the patient was ready for transfer. After allowing 20 minutes for the floor nurse to view the dashboard, the PACU nurse and floor nurse spoke on the phone to discuss any outstanding questions. PACU and OR boarder rates were obtained from the electronic medical record 12 months before and after implementing the nursing patient dashboard. User feedback was gathered via plan-do-study-act cycles, posters, and interviews. FindingsThe electronic dashboard was a successful strategy to improve handoff communication between the PACU and receiving units. The dashboard was accepted by the staff with 70% (n = 24) of the comments being positive. A significant decrease in the number of PACU (χ2(1, n = 20,608) =122.63, P < .00001) and OR boarding (χ2 (1, n = 20,283) =14.55, P = .000136) of patients was found in the C-year compared to 2019. For patients who were boarded in the PACU, no significant difference in PACU delay duration was found (t(11) =1.49, P = .149) with the mean in 2019 of 166.96 (SD = 68.38) and the C-Year mean 132.84 (SD = 39.74). For patients who boarded in the OR, there was a significant difference (t(11) =15.590162, P <.00001) between groups for average duration of boarding with the mean in 2019 of 19.06 minutes (SD = 3.72) compared to 1.62 (SD = 1.1) in C-year. However, in July 2020 the PACU intermittently opened 2 flexible beds when the PACU was full, suggesting that OR boarding was not a reliable measure, but PACU boarding remained an accurate measure. ConclusionsThe findings of this evidence-based quality improvement project demonstrated the usefulness of an electronic dashboard tool combined with verbal report to improve patient throughput by decreasing the number of patients boarding in the PACU.

Infant Botulism.

Emergency medicine and pediatric residents, and pediatric emergency medicine (PEM) fellows. Botulism is a rare but serious cause of infant hypotonia, vomiting, and respiratory failure. The differential diagnosis and management of a hypotonic infant with progressive weakness leading to respiratory failure is a rare presentation with high morbidity and mortality.1 Infants with botulism generally present with vague complaints that progressively worsen over time.2 Recognition of descending paralysis in an infant as well as signs of respiratory failure are key to preventing an adverse outcome. A key component of botulism treatment is recognizing the need to mobilize local resources to obtain BabyBIG® (botulism immune globulin). This process can and should begin in the emergency department. After this simulation learners should be able to: 1) develop a differential diagnosis for the hypotonic infant, 2) recognize signs and symptoms of infant botulism, 3) recognize respiratory failure and secure the airway with appropriate rapid sequence intubation (RSI) medications, 4) initiate definitive treatment of infant botulism by mobilizing resources to obtain antitoxin, 5) continue supportive management and admit the patient to the pediatric intensive care unit (PICU), 6) understand the pathophysiology and epidemiology of infant botulism, 7) develop communication and leadership skills when evaluating and managing critically ill infants. This simulation case was performed using a high-fidelity Laerdal SimBaby with intubating capabilities and real-time vital sign monitoring. Additionally, this case can be performed with low fidelity manikins with supplemental scripting and visual stimuli. With minor adjustments, this case could be modified into an oral boards case. We obtained feedback from a convenience sample of random participants after the simulation case and debrief were completed. The sample of emergency medicine residents (N=21) and PEM fellow (N=1) completed 5 questions on a 5-point Likert scale. The emergency medicine residents and PEM fellow had mostly favorable feedback regarding the simulation and debriefing. Most strongly agreed or agreed that this would improve their performance in an actual clinical setting. Infant botulism is a rare condition, presenting as vague non-specific complaints that worsen over time. It is important to differentiate infant botulism from other causes of weakness, hypotonia, and respiratory failure. This case presents learners with a high acuity, rare case of infant botulism and allows them to work through a complex pediatric patient encounter in a psychologically safe space. The presence of a standardized patient to play the patient's parent is key to assess learners' nontechnical communication skills and to increase fidelity during the simulation. Infant botulism, pediatric emergency medicine, respiratory failure, hypotonia, toxicology.