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Critical Care Conference: 42nd International Symposium on Intensive Care and Emergency Medicine Brussels Belgium ; 27(Supplement 1), 2023.
Article in English | EMBASE | ID: covidwho-2318517

ABSTRACT

Introduction: Virtual communication has become common practice during the COVID-19 pandemic due to visitation restriction. We aimed to evaluate overall family satisfaction in intensive care unit (FS-ICU) with virtual communication strategies during the COVID-19 pandemic period. Method(s): In this prospective multi-centre study involving three metropolitan hospitals in Melbourne, Australia, the next of kin (NOK) of all the eligible ICU patients between 07/01/2022 and 10/31/2020 were required to complete an adopted version of FS-ICU 24-Questionnaire. Group comparisons were analysed for family satisfaction scores: ICU/ care (satisfaction with care), FS-ICU/dm (satisfaction with information/ decision-making) and FS-ICU total (overall satisfaction with the ICU) were calculated. The essential predictors that influence family satisfaction were identified using quantitative and qualitative analyses. Result(s): Seventy-three out of the 227 patients' NOK who initially agreed, completed the FS-ICU questionnaire (response rate 32.2%). The mean (SD) FS-ICU/total was 63.9 (30.8). The mean score for satisfaction with FS-ICU/dm was lower than the FS-ICU/care (62.1 [30.30 vs. 65.4 [31.4];p 0.001) (Fig. 1). There was no difference in mean FS-ICU/ total scores between survivors (n = 65;89%) and non-survivors (n = 8, 11%). Higher patient APACHE-III score, female NOK and the patient dying in ICU were independent predictors for FS-ICU/total score while a telephone call at least once a day by an ICU doctor was related to higher family satisfaction for FS-ICU/dm. Conclusion(s): There was low overall family satisfaction with ICU care and virtual communication strategies adopted during the COVID-19 pandemic. Effort should be targeted for improving factors with virtual communication that cause low family satisfaction during the COVID-19 pandemic.

2.
1st Annual Meeting of the Symposium on Human-Computer Interaction for Work, CHIWORK 2022 ; 2022.
Article in English | Scopus | ID: covidwho-1909846

ABSTRACT

Remote meetings have become more prevalent due to the COVID-19 pandemic and technology that facilitates remote work. There is limited research on the effect of remote meetings on group performance and the goal of this study is to identify how distractions affect the individual and group creativity in remote work meetings. A virtual study was conducted where groups of four people participated in divergent and convergent thinking tasks. One group member was assigned an additional non-meeting task while another was assigned as a scribe. Measures of creative performance (e.g., uniqueness of idea) of the distracted members and the group were analyzed. The results show that the distractee contributed (on average) less time and ideas when compared to monotaskers and those assigned as a scribe. The study highlights ways that remote meetings can facilitate creativity. © 2022 ACM.

3.
International Journal of Computer Science and Network Security ; 22(1):367-374, 2022.
Article in English | Web of Science | ID: covidwho-1744437

ABSTRACT

With the changing workplace landscape evident from the recent remote working arrangement, owing to the disruption caused by COVID-19 pandemic, the pace of adopting integrated services with AI has accelerated. In the insurance industry, there has been a gradual increase in business cases and research, with the introduction of AI technology in areas such as detection of unfair claims, claims adjusting, and insurance acceptance. In this study, an insurance underwriting model for accepting/rejecting new applicants was developed to reduce these discrepancies, based on underwriters, as well as enable faster processing. The data of Prudential Life Insurance from Kaggle was utilized to develop the insurance underwriting model. Among the feature selection methods, the filter-based and embedded methods were comparatively evaluated, and a Regularized Random Forest from the embedded methods was finally selected. For the insurance underwriting model, seven classification algorithms were applied for model optimization, and using the ensemble voting, the result of models with excellent classification performance with a recall score of 0.8 or higher was finally predicted by voting to ensure derivation of reliable results.

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