Development of a data-driven digital phenotype profile of distress experience of healthcare workers during COVID-19 pandemic.

BACKGROUND AND OBJECTIVE: Due to the constraints of the COVID-19 pandemic, healthcare workers have reported acting in ways that are contrary to their moral values, and this may result in moral distress. This paper proposes the novel digital phenotype profile (DPP) tool, developed specifically to evaluate stress experiences within participants. The DPP tool was evaluated using the COVID-19 VR Healthcare Simulation of Stress Experience (HSSE) dataset (NCT05001542), which is composed of passive physiological signals and active mental health questionnaires. The DPP tool focuses on correlating electrocardiogram, respiration, photoplethysmography, and galvanic skin response with moral injury outcome scale (Brief MIOS). METHODS: Data-driven techniques are encompassed to develop a tool for robust evaluation of distress among participants. To accomplish this, we applied pre-processing techniques which involved normalization, data sanitation, segmentation, and windowing. During feature analysis, we extracted domain-specific features, followed by feature selection techniques to rank the importance of the feature set. Prior to classification, we employed k-means clustering to group the Brief MIOS scores to low, moderate, and high moral distress as the Brief MIOS lacks established severity cut-off scores. Support vector machine and decision tree models were used to create machine learning models to predict moral distress severities. RESULTS: Weighted support vector machine with leave-one-subject-out-cross-validation evaluated the separation of the Brief MIOS scores and achieved an average accuracy, precision, sensitivity, and F1 of 98.67%, 98.83%, 99.44%, and 99.13%, respectively. Various machine learning ablation tests were performed to support our results and further enhance the understanding of the predictive model. CONCLUSION: Our findings demonstrate the feasibility to develop a DPP tool to predict distress experiences using a combination of mental health questionnaires and passive signals. The DPP tool is the first of its kind developed from the analysis of the HSSE dataset. Additional validation is needed for the DPP tool through replication in larger sample sizes.

The Role of Collaborative Observational Practice and Feedback-Discourse to Promote Remote Acquisition of Technical Surgical Skills.

INTRODUCTION: Acquisition of technical skills remotely in a decentralized model requires an efficacious way of providing feedback. The primary objective was to test the efficacy of various forms of feedback on the acquisition of surgical skills by medical students. METHODS: Forty volunteers were randomized to four experimental groups, differing from the nature of feedback (free text versus structured) and who provided the feedback (expert versus peer learners). They had to perform sutures and upload attempts on a learning management system to receive interactive feedback. The pretest and retention test performances were assessed. RESULTS: All groups significantly improved from pretests to retention tests; however, participants using checklist showed statistically lower improvements than the other groups, which did not differ from each other. CONCLUSIONS: Remote learners can acquire surgical skills, and most importantly, peers who provide feedback, are as effective as experts if they use open-ended comments and not checklists.

Digital Interventions to Reduce Distress Among Health Care Providers at the Frontline: Protocol for a Feasibility Trial.

BACKGROUND: Stress, anxiety, distress, and depression are high among health care workers during the COVID-19 pandemic, and they have reported acting in ways that are contrary to their moral values and professional commitments that degrade their integrity. This creates moral distress and injury due to constraints they have encountered, such as limited resources. OBJECTIVE: The purpose of this study is to develop and show the feasibility of digital platforms (a virtual reality and a mobile platform) to understand the causes and ultimately reduce the moral distress of health care providers during the COVID-19 pandemic. METHODS: This will be a prospective, single cohort, pre- and posttest study examining the effect of a brief informative video describing moral distress on perceptual, psychological, and physiological indicators of stress and decision-making during a scenario known to potentially elicit moral distress. To accomplish this, we have developed a virtual reality simulation that will be used before and after the digital intervention for monitoring short-term impacts. The simulation involves an intensive care unit setting during the COVID-19 pandemic, and participants will be placed in morally challenging situations. The participants will be engaged in an educational intervention at the individual, team, and organizational levels. During each test, data will be collected for (1) physiological measures of stress and after each test, data will be collected regarding (2) thoughts, feelings and behaviors during a morally challenging situation, and (3) perceptual estimates of psychological stress. In addition, participants will continue to be monitored for moral distress and other psychological stresses for 8 weeks through our Digital intervention/intelligence Group mobile platform. Finally, a comparison will be conducted using machine learning and biostatistical techniques to analyze the short- and long-term impacts of the virtual reality intervention. RESULTS: The study was funded in November 2020 and received research ethics board approval in March 2021. The study is ongoing. CONCLUSIONS: This project is a proof-of-concept integration to demonstrate viability over 6 months and guide future studies to develop these state-of-the-art technologies to help frontline health care workers work in complex moral contexts. In addition, the project will develop innovations that can be used for future pandemics and in other contexts prone to producing moral distress and injury. This project aims to demonstrate the feasibility of using digital platforms to understand the continuum of moral distress that can lead to moral injury. Demonstration of feasibility will lead to future studies to examine the efficacy of digital platforms to reduce moral distress. TRIAL REGISTRATION: ClinicalTrials.gov NCT05001542; https://clinicaltrials.gov/ct2/show/NCT05001542. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/32240.

A Model for an Online Learning Management System for Simulation-Based Acquisition of Psychomotor Skills in Health Professions Education.

The current coronavirus disease (COVID-19) pandemic has shifted traditional educational approaches in health professions education (HPE) from in-person to remote learning. Although pedagogical strategies have been developed and implemented rapidly to support cognitive and affective domains of learning in HPE, less progress has occurred in psychomotor skills acquisition. Psychomotor skills, referred to as technical skills training, are underpinned by educational theories and conceptual frameworks. Considering the widening gap in learning domains, this editorial provides an overview and recommendations for developing and implementing remote training supported by educational theories, such as deliberate practice, and conceptual frameworks in technical skills acquisition in HPE. We begin by discussing the unique curricular needs for remote psychomotor skills in medical teaching-learning contexts and subsequently present a theory-driven and evidence-based model for remote psychomotor skills acquisition.