Human intention recognition for trauma resuscitation: An interpretable deep learning approach for medical process data.

Objective: Trauma resuscitation is the initial evaluation and management of injured patients in the emergency department. This time-critical process requires the simultaneous pursuit of multiple resuscitation goals. Recognizing whether the required goal is being pursued can reduce errors in goal-related task performance and improve patient outcomes.

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.

Factors associated with correction of personal protective equipment nonadherence in a multidisciplinary emergency department setting: A retrospective video review.

Background: Despite local and national recommendations, health care provider adherence to personal protective equipment (PPE) varied during the COVID-19 pandemic. Previous studies have identified factors influencing initial PPE adherence but did not address factors influencing behaviors leading to correction after initial nonadherence.

Methods: We conducted a retrospective video review of 18 pediatric resuscitations involving aerosol-generating procedures from March 2020 to December 2022 to identify factors associated with nonadherence correction.

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.