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Accepted for/Published in: Journal of Medical Internet Research

Date Submitted: Nov 2, 2023
Open Peer Review Period: Nov 1, 2023 - Nov 16, 2023
Date Accepted: May 7, 2024
(closed for review but you can still tweet)

The final, peer-reviewed published version of this preprint can be found here:

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

Cha C, Cho A, Baek G

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

J Med Internet Res 2024;26:e54029

DOI: 10.2196/54029

PMID: 38905631

PMCID: 11226930

AI-Based Tailored Mobile Intervention for Nurse Burnout: Development Study

  • Chiyoung Cha; 
  • Aram Cho; 
  • Gumhee Baek

ABSTRACT

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, and ultimately improve the efficiency of the health care system.


 Citation

Please cite as:

Cha C, Cho A, Baek G

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

J Med Internet Res 2024;26:e54029

DOI: 10.2196/54029

PMID: 38905631

PMCID: 11226930

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