Design and rationale of an intelligent algorithm to detect BuRnoUt in HeaLthcare workers in COVID era using ECG and artificiaL intelligence: The BRUCEE-LI study

Gupta, Mohit; Bansal, Ankit; Sarkar, Piyali; Mp, Girish; Jha, Manish K.; Yusuf, Jamal; Kumar, Suresh; Kumar, Satish; Jain, Ajeet; Kathuria, Sanjeev; Saijpaul, Rajni; Mishra, Anurag; Malhotra, Vikas; Yadav, Rakesh Kumar; Ramakrishanan, S.; Malhotra, Rajeev; Batra, Vishal; Shetty, Manu Kumar; Sharma, Nandini; Mukhopadhyay, Saibal; Garg, Sandeep; Gupta, Anubha

doi:10.1016/j.ihj.2020.11.145

Cited by 19 publications

(5 citation statements)

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“…To the authors' knowledge, this is the first study to assess and compare work stress and burnout between physical therapists and informaticians. We identified only one article concerning the application of AI in burnout assessment concerning the use of electrocardiogram data generated from people experiencing burnout to develop an AI-enabled model (convolutional neural network (CNN)) to predict the presence of stress and burnout in healthcare workers in the COVID-19 era [13]. We identified only two papers on the application of AI to stress detection and management.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Approach to Computational Classification of Burnout—Preliminary Findings

Prokopowicz

Mikołajewski

2022

Applied Sciences

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There is a common belief that medical professions generate more work-related stress and earlier job burnout. We tested two groups: study group 1: medical (physical therapists, n = 30), and study group 2: non-medical (informaticians, n = 30). The purpose of this study was to find new, more reliable models for calculating work-related stress and burnout in the two aforementioned different professional groups. In the paper, we focused on a new model of algorithm based on AI methods that extends the interpretability of the scale of results obtained using the MBI test. The outcomes of the Maslach Burnout Inventory (MBI) were analysed in both study groups. These became the starting point for the development of three different fuzzy models, from which, after comparison, the one best suited to the study groups and the way they were evaluated was selected. Among the patients participating in the study, the following results were obtained: MBI values expressed as median values were significantly higher in group 2 than in group 1. The computational analysis showed that the contribution of the different parts of the MBI test to the final score was unequal in both groups. AI allowed for optimal selection of the model parameters for the study group, from which an algorithm was created to optimise the selection of tools or their parameters. A computational tool can do this faster, more accurately, and more efficiently, becoming an important supporting tool. In the medical context, the main benefit of the results presented in this paper is the definition of an evaluation model that transforms the MBI test scores into a universal percentage scale while preserving the properties of the guidelines underlying the MBI. An additional advantage of the proposed solution is the readability and flexibility resulting from the linguistic rules underlying the model.

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Section: Introductionmentioning

confidence: 99%

Fuzzy Approach to Computational Classification of Burnout—Preliminary Findings

Prokopowicz

Mikołajewski

2022

Applied Sciences

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“…Thus, our system is distinct from other such programs and has unique features in terms of its development. Several studies have used AI to predict or manage burnout based on physiological indicators or to predict the degree of burnout [61][62][63][64]. Although predictions and measurements were developed, no suitable strategy for reducing burnout was presented.…”

Section: Discussionmentioning

confidence: 99%

Development of an Artificial Intelligence–Based Tailored Mobile Intervention for Nurse Burnout: Single-Arm Trial

Cho,

Cha,

Baek

2024

J Med Internet Res

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Background: Nurse burnout leads to an increase in turnover, which is a serious problem in the health care system. Although there is ample evidence of nurse burnout, interventions developed in previous studies were uniform and did not consider burnout dimensions and individual characteristics. Objective:The objectives of this study were to develop and optimize the first tailored mobile intervention for nurse burnout, which recommends programs based on artificial intelligence (AI) algorithms, and to test its usability, effectiveness, and satisfaction. Methods:In this study, an AI-based mobile intervention, Nurse Healing Space©, was developed to provide tailored programs for nurse burnout. The 4-week program included mindfulness meditation, laughter therapy, storytelling, reflective writing, and acceptance commitment therapy. The AI algorithm recommended one of these programs to participants by calculating similarity through a pre-test consisting of participants' demographics, research variables, and burnout dimension scores. After completing a 2-week program, program satisfaction and burnout were measured. Users were recommended another 2-week program based on the burnout dimension scores. AI recognized the recommended program as effective if the user's burnout score reduced after the 2-week program and updated the algorithm accordingly. After a pilot test (n=10), AI optimization was performed (n=300). A paired t-test and ANOVA and Spearman's correlation were conducted to test the effect of the intervention and algorithm optimization.Results: Nurse Healing Space© was implemented as a mobile application equipped with a system that recommended one program out of four based on similarity between users through AI. The AI algorithm worked well in matching the program recommended to participants who were most similar using valid data. Users scored the program more than 4 out of 5 points for convenience and visual quality, but were dissatisfied with the absence of notifications and inability to customize the program. The overall usability score of the application was 3.4 out of 5 points. Nurses' burnout scores decreased significantly after the completion of the first 2-week program and reduced further after the second 2-week program. After completing the Nurse Healing Space© program, job stress and stress responses decreased significantly. During the second 2-week program, the level of burnout reduction increased in the order of participation. User satisfaction increased for both the first and second programs, and the post-hoc test showed significant differences between the first 100 and the third 100 participants for the first and second programs.Conclusions: This program effectively reduced burnout, job stress, and stress responses. Nurse managers were able to prevent nurses from resigning and maintain the quality of medical services using this AI-based program to provide tailored interventions for nurse burnout. Thus, this application could improve qualitative health care, increase employee satisfaction, reduce costs, ...

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“…These data were recorded during supine paced breathing using VESTA 301i (500 Hz). Similarly, ECG data of 430 healthy subjects recorded in the study [ 34 ] at the same hospitals using the same machines were used as the control group data. We removed 12 post-COVID-19 and 3 healthy samples because their ECG data were very noisy.…”

Section: Methodsmentioning

confidence: 99%