Comparative analysis of personal protective equipment nonadherence detection: computer vision versus human observers.

Objectives: Human monitoring of personal protective equipment (PPE) adherence among healthcare providers has several limitations, including the need for additional personnel during staff shortages and decreased vigilance during prolonged tasks. To address these challenges, we developed an automated computer vision system for monitoring PPE adherence in healthcare settings. We assessed the system performance against human observers detecting nonadherence in a video surveillance experiment.

Analysis of Task Attributes Associated with Crisis Checklist Compliance in Pediatric Trauma Resuscitation.

Although checklists can improve overall team performance during medical crises, non-compliant checklist use poses risks to patient safety. We examined how task attributes affected checklist compliance by studying the use of a digital checklist during trauma resuscitation. We first determined task attributes and checklist compliance behaviors for 3,131 resuscitation tasks.

Real-time Context-Aware Multimodal Network for Activity and Activity-Stage Recognition from Team Communication in Dynamic Clinical Settings.

In clinical settings, most automatic recognition systems use visual or sensory data to recognize activities. These systems cannot recognize activities that rely on verbal assessment, lack visual cues, or do not use medical devices. We examined speech-based activity and activity-stage recognition in a clinical domain, making the following contributions.

An Analysis of Speech during Life Saving Interventions to Inform the Design of a Computerized System for Delay Detection.

We describe an analysis of speech during time-critical, team-based medical work and its potential to indicate process delays. We analyzed speech intention and sentence types during 39 trauma resuscitations with delays in one of three major lifesaving interventions: intravenous/intraosseous (IV/IO) line insertion, cardiopulmonary and resuscitation (CPR), and intubation. We found a significant difference in patterns of speech during delays vs.

Discovering interpretable medical process models: A case study in trauma resuscitation.

Understanding the actual work (i.e., "work-as-done") rather than theorized work (i.

Factors Affecting Peripheral Intravenous Catheter Placement During Pediatric Trauma Resuscitation.

Introduction: Intravenous access is required for resuscitation of injured patients but may be delayed in children because of challenges associated with peripheral intravenous (PIV) catheter placement. Early identification of factors predisposing patients to difficult PIV placement can assist in deciding strategies for timely intravenous access.

Methods: We conducted a retrospective, video-based review of injured children and adolescents treated between April 2018 and May 2019.

Multi-Label Activity Recognition using Activity-specific Features and Activity Correlations.

Multi-label activity recognition is designed for recognizing multiple activities that are performed simultaneously or sequentially in each video. Most recent activity recognition networks focus on single-activities, that assume only one activity in each video. These networks extract shared features for all the activities, which are not designed for multi-label activities.

VidTr: Video Transformer Without Convolutions.

We introduce Video Transformer (VidTr) with separable-attention for video classification. Comparing with commonly used 3D networks, VidTr is able to aggregate spatio-temporal information via stacked attentions and provide better performance with higher efficiency. We first introduce the vanilla video transformer and show that transformer module is able to perform spatio-temporal modeling from raw pixels, but with heavy memory usage.

Designing Interactive Alerts to Improve Recognition of Critical Events in Medical Emergencies.

Vital sign values during medical emergencies can help clinicians recognize and treat patients with life-threatening injuries. Identifying abnormal vital signs, however, is frequently delayed and the values may not be documented at all. In this mixed-methods study, we designed and evaluated a two-phased visual alert approach for a digital checklist in trauma resuscitation that informs users about undocumented vital signs.

Real-time medical phase recognition using long-term video understanding and progress gate method.

We introduce a real-time system for recognizing five phases of the trauma resuscitation process, the initial management of injured patients in the emergency department. We used depth videos as input to preserve the privacy of the patients and providers. The depth videos were recorded using a Kinect-v2 mounted on the sidewall of the room.

Personal Protective Equipment Adherence of Pediatric Resuscitation Team Members During the COVID-19 Pandemic.

Study Objective: During the COVID-19 pandemic, health care workers have had the highest risk of infection among essential workers. Although personal protective equipment (PPE) use is associated with lower infection rates, appropriate use of PPE has been variable among health care workers, even in settings with COVID-19 patients. We aimed to evaluate the patterns of PPE adherence during emergency department resuscitations that included aerosol-generating procedures.

Metrics of shock in pediatric trauma patients: A systematic search and review.

Introduction: Shock-index (SI) and systolic blood pressure (SBP) are metrics for identifying children and adults with hemodynamic instability following injury. The purpose of this systematic review was to assess the quality of these metrics as predictors of outcomes following pediatric injury.

Materials And Methods: We conducted a literature search in Pubmed, SCOPUS, and CINAHL to identify studies describing the association between shock metrics on the morbidity and mortality of injured children and adolescents.

Association of Ventilation during Initial Trauma Resuscitation for Traumatic Brain Injury and Post-Traumatic Outcomes: A Systematic Review.

Objectives: In the absence of evidence of acute cerebral herniation, normal ventilation is recommended for patients with traumatic brain injury (TBI). Despite this recommendation, ventilation strategies vary during the initial management of patients with TBI and may impact outcome. The goal of this systematic review was to define the best evidence-based practice of ventilation management during the initial resuscitation period.

Assessment of Non-Routine Events and Significant Physiological Disturbances during Emergency Department Evaluation after Pediatric Head Trauma.

Outcomes following pediatric traumatic brain injury (TBI) are dependent on initial injury severity and prevention of secondary injury. Hypoxia, hypotension, and hyperventilation following TBI are associated with increased mortality. The purpose of this study was to determine the association of non-routine events (NREs) during the initial resuscitation phase with these physiological disturbances.

Video-based Concurrent Activity Recognition for Trauma Resuscitation.

We introduce a video-based system for concurrent activity recognition during teamwork in a clinical setting. During system development, we preserved patient and provider privacy by pre-computing spatio-temporal features. We extended the inflated 3D ConvNet (i3D) model for concurrent activity recognition.

Speech-Based Activity Recognition for Trauma Resuscitation.

We present a speech-based approach to recognize team activities in the context of trauma resuscitation. We first analyzed the audio recordings of trauma resuscitations in terms of activity frequency, noise-level, and activity-related keyword frequency to determine the dataset characteristics. We next evaluated different audio-preprocessing parameters (spectral feature types and audio channels) to find the optimal configuration.

RESIDUAL RECURRENT NEURAL NETWORK FOR SPEECH ENHANCEMENT.

Most current speech enhancement models use spectrogram features that require an expensive transformation and result in phase information loss. Previous work has overcome these issues by using convolutional networks to learn the temporal correlations across high-resolution waveforms. These models, however, are limited by memory-intensive dilated convolution and aliasing artifacts from upsampling.

Classification strategies for non-routine events occurring in high-risk patient care settings: A scoping review.

Introduction: Non-routine events (NREs) are atypical or unusual occurrences in a pre-defined process. Although some NREs in high-risk clinical settings have no adverse effects on patient care, others can potentially cause serious patient harm. A unified strategy for identifying and describing NREs in these domains will facilitate the comparison of results between studies.

Assessment of Nonroutine Events During Intubation After Pediatric Trauma.

Background: Intubation in the early postinjury phase can be a high-risk procedure associated with an increased risk of mortality when delayed. Nonroutine events (NREs) are workflow disruptions that can be latent safety threats in high-risk settings and may contribute to adverse outcomes.

Materials And Methods: We reviewed videos of intubations of injured children (age<17 y old) in the emergency department occurring between 2014 and 2018 to identify NREs occurring between the decision to intubate and successful intubation ("critical window").

Mutual Correlation Attentive Factors in Dyadic Fusion Networks for Speech Emotion Recognition.

Emotion recognition in dyadic communication is challenging because: 1. Extracting informative modality-specific representations requires disparate feature extractor designs due to the heterogenous input data formats. 2.

Multimodal Attention Network for Trauma Activity Recognition from Spoken Language and Environmental Sound.

Trauma activity recognition aims to detect, recognize, and predict the activities (or tasks) during a trauma resuscitation. Previous work has mainly focused on using various sensor data including image, RFID, and vital signals to generate the trauma event log. However, spoken language and environmental sound, which contain rich communication and contextual information necessary for trauma team cooperation, are still largely ignored.

Process conformance is associated with successful first intubation attempt and lower odds of adverse events in a paediatric emergency setting.

Background: Intubation is an essential, life-saving skill but associated with a high risk for adverse outcomes. Intubation protocols have been implemented to increase success and reduce complications, but the impact of protocol conformance is not known. Our study aimed to determine association between conformance with an intubation process model and outcomes.

Association Between Prearrival Notification Time and Advanced Trauma Life Support Protocol Adherence.

Background: Prearrival notification of injured patients facilitates preparation of personnel, equipment, and other resources needed for trauma evaluation and treatment. Our purpose was to determine the impact of prearrival notification time on adherence to Advanced Trauma Life Support (ATLS) protocols.

Materials And Methods: Pediatric trauma activations of admitted patients were analyzed by video review to determine activities performed before and after patient arrival.

Process Mining the Trauma Resuscitation Patient Cohorts.

In this study, we present a framework for analyzing associations between patient cohorts and the trauma resuscitation procedures their patients received. Our framework works by quantifying associations between discovered patient cohorts and treatment patterns. We evaluated our framework on a trauma resuscitation dataset collected in a level 1 trauma center.

Object motion detection based on passive UHF RFID tags using a hidden Markov model-based classifier.

We present an object motion detection system using backscattered signal strength of passive UHF RFID tags as a sensor for providing information on the movement and identity of work objects-important cues for activity recognition. For using the signal strength for accurate detection of object movement we propose a novel Markov model with continuous observations, RSSI preprocessor, frame-based data segmentation, and motion-transition finder. We use the change of backscattered signal strength caused by tag's relocation to reliably detect movement of tagged objects.