AI scheduling boosts nurse satisfaction, work conditions

After reducing night shifts and increasing weekend off-duty time, Inha University Hospital nurses reported improved work quality and higher job satisfaction. These changes followed the adoption of an AI-powered scheduling system developed in-house.

The Inha University Hospital Nursing AI Scheduling System (IH-NASS) has made a tangible difference in the real world, the hospital said. Designed to automate nurse shift scheduling, IH-NASS was introduced to reduce scheduling conflicts and improve fairness—issues long associated with manually written timesheets.

A joint research team from the Inha University Hospital Nursing Headquarters and the Department of Nursing published a study analyzing the impact of IH-NASS in the international journal BMC Nursing, the hospital said Monday.

Since 2023, the hospital has used IH-NASS, based on the automatic timesheet generation program MATRON from the Korea Institute of Education Management (KIEM), to create shift schedules for nurses. The system was customized in 2022 by Inha University Hospital with support from seven of its nurses, two IT experts, and KIEM developers.

IH-NASS was applied to 14 hospital wards. The study analyzed data from 253 nurses working in three shifts across those wards, comparing outcomes from December 2022 (manual scheduling) and December 2023 (AI-based scheduling).

The findings showed a reduction in unfavorable scheduling patterns such as Night-Off-Evening (NOE) shifts, which dropped from an average of 0.49 to 0.34 per nurse. The number of instances in which nurses had two or more consecutive days off after a night shift increased from 1.53 to 1.66. Off-days on Sundays also rose significantly, from 1.70 to 1.99. Total hours worked before 6 a.m. or after 6 p.m. on weekdays decreased from an average of 39.5 to 37.1 hours.

Organizational improvements were also observed. New nurses with less than one year of experience saw their average weekly shifts drop from 1.43 to 1.00, suggesting more skill-based staffing. Given the high intensity of daytime work, the research team noted this change could enhance both patient safety and care quality.

Subjective evaluations from nurses supported the system’s effectiveness. On a 4-point scale, IH-NASS received an average rating of 2.54 for convenience, 2.44 for fairness, and 2.12 for satisfaction. Overall job satisfaction scored 29.94 out of a possible 45. Regression analysis revealed that satisfaction with IH-NASS, perceived convenience, and reduced NOE shifts had a significant impact on job satisfaction.

“The study proved that the AI-based scheduling automation system is not just a convenience but also makes a real difference in nurses' job satisfaction and working environment,” said Choi Hwa-sook, head of the hospital’s Nursing Headquarters. “It will be a meaningful turning point in the digital transformation of nursing workforce management and the construction of smart hospitals.”