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Development of a nurse scheduling program using robotic process automation in Korea

Sun Young Jung et al.

Health Informatics Journal2026https://doi.org/10.1177/14604582251414581article
ABDC C
Weight
0.50

What the paper says

<b>Background:</b> Effective nurse scheduling promotes patient safety, nurse well-being, and compliance with regulations. <b>Objective:</b> To develop and evaluate a robotic process automation (RPA)-based scheduling system to improve accuracy, reflect nurse preferences, and align with national guidelines. <b>Methods:</b> A total of 102 nurses from a 500-bed hospital were assigned to the experimental or control group. The RPA system was integrated into nurse managers' workflows to automate monthly shift planning. Nurses submitted shift preferences and the system generated schedules accordingly. Pre- and post-intervention data on work characteristics, health status, and work-life balance were analyzed using chi-square and paired t-tests with SPSS. <b>Results:</b> The RPA-based scheduling significantly improved nurses' work-life balance. No significant differences were found in health status or work characteristics. <b>Conclusion:</b> Integrating RPA into nurse scheduling can enhance work-life balance and support fairer scheduling practices. Broader organizational adoption and supportive policies are recommended to ensure sustainable impact and improved care quality.

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https://doi.org/https://doi.org/10.1177/14604582251414581

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@article{sun2026,
  title        = {{Development of a nurse scheduling program using robotic process automation in Korea}},
  author       = {Sun Young Jung et al.},
  journal      = {Health Informatics Journal},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1177/14604582251414581},
}

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Development of a nurse scheduling program using robotic process automation in Korea

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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