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.

Development of Content for a Virtual Reality Simulation to Understand and Mitigate Moral Distress in Healthcare Workers.

Background In high-stakes situations, healthcare workers are prone to suffer moral injury, the psychological, social, and spiritual impact of events involving betrayal or transgression of one's own deeply held moral beliefs and values. As a result, this may negatively impact their capacity to provide adequate levels of care to patients. There is a lack of educational resources catered to help healthcare workers navigate ethical situations in clinical settings that may lead to or worsen moral distress. The aim of this report is to describe the methodology of development and resulting outcomes in the form of an educational resource that includes a virtual reality (VR) simulation to help healthcare workers understand and mitigate moral distress as a result of internal and external constraints at their workplaces. Methodology A study using a method outlining a set of constraint parameters, followed by ideation utilizing design thinking (DT), and concluding with a consensus-building exercise using Delphi methodology (DM) with a group of 13 experts in healthcare simulation, VR, psychiatry, psychology, and nursing. The constraints parameters included technology use (VR), use of experiential learning theory, and duration of the intervention (15 minutes). A DT process was performed to generate and expand on ideas on the scenario and intervention of a possible VR simulation which were funneled into a three-round DM to define the foundations of the VR simulation. Average, standard deviations, and free-text comments in the DM were used to assess the inclusion of the produced requirements. Finally, a focus group interview was conducted with the same experts to draft the VR simulation. Results Within the specified constraints, the DT process produced 33 ideas for the VR simulation scenario and intervention that served as a starting point to short-list the requirements in Round 1. In Rounds 1 to 2, 25 items were removed, needed revising, and/or were retained for the subsequent rounds, which resulted in eight items at the end of Round 2. Round 2 also required specialists to provide descriptions of potential scenarios and interventions, in which five were submitted. In Round 3, experts rated the descriptions as somewhat candidate to use in the final VR simulation, and the open feedback in this round proposed combining the elements from each of the descriptions. Using this data, a prototype of the VR simulation was developed by the project team together with VR designers. Conclusions This development demonstrated the feasibility of using the constraints-ideation-consensus approach to define the content of a possible VR simulation to serve as an educational resource for healthcare workers on how to understand and mitigate moral distress in the workplace. The methodology described in this development may be applied to the design of simulation training for other skills, thereby advancing healthcare training and the quality of care delivered to the greater society.